The following disclosure is directed to methods and systems for the detection of software remotely through a web browser and, more specifically, methods and systems for detection of software remotely through a web browser by detecting the presence of webinjects in a web browser.
Modern software often uses webinjects to change with a user's web browsing experience. Examples of such software include malware, adware, browser extensions, and anti-virus programs. Webinjects are pieces of foreign code, e.g., Hypertext Markup Language (HTML) or JavaScript elements, that can be locally injected into webpages visited by the user. These webinjects can be injected through several techniques, for example, through a browser extension application programming interface (API), browser process memory injection, or local network proxies. The webinjects can change the webpage to steal information (e.g., passwords, personal data, etc.), present additional content to the user (e.g., advertising), and/or improve the user's browsing experience (e.g., by blocking advertising, presenting useful information, improving functionality, etc.). Motives for inserting webinjects into webpages can range from stealing information to displaying advertising, or even improving the user's experience.
Disclosed herein are systems and methods to detect webinjects, and their sources, in webpages. Some approaches for detecting software presence remotely include (i) scanning the Internet for systems that publicly expose services and (ii) using a sinkhole to isolate a domain and receive software connections as the software reaches the sinkhole, which, in some instances requires the subject domain to be expired or otherwise available. Another approach uses crawlers in a peer-to-peer (P2P) configuration, where the crawler joins the P2P network and receives connections from other peers. However, this approach is limited to P2P-enabled software. This method, if deployed through advertising networks or other partners that can provide large amounts of traffic, can detect a considerable number of software installations by detecting the presence of webinjects in the browser. The exemplary methods and systems described herein can be used alone or complement any one or more of the above methods to detect webinjects.
In a first aspect, a computer-implemented method is provided for the detection of webinjects. The method includes delivering a detection webpage to a web browser. The detection webpage has detection code configured to detect a presence of the webinject in the detection webpage. The method further includes inspecting, by the detection code, rendering of content of the detection webpage in the browser to detect webinject content inserted into the detection webpage by the webinject. The webinject content includes one or more Hypertext Markup Language (HTML) components. The method further includes, if webinject content is detected, generating, by the detection code, a fingerprint for each of the one or more HTML components; transmitting, by the detection code, the one or more fingerprints to an external server; and classifying, by the external server, the webinject based on the one or more fingerprints.
Embodiments of the method can include any one or more of the below features. The method can include transmitting to the external sever, by the detection code, one or more HTML components of the detected webinject content. The transmission can be on a portion of the executions of the detection code. The transmission can be on a small sample of the executions. For example, the transmission can be on 1% or less of the executions of the detection code. These HTML component(s) can be transmitted with their respective fingerprints. The method can include identifying the origin software of the HTML component(s) by (i) searching for the HTML component(s) in sandboxed executions of software and/or (ii) searching through privately- and/or publicly-available data sources. The sandboxed executions of software may be associated with, may be related to, or possibly be the origin software. The method can further include generating a database including (a) the fingerprint(s), (b) the name of the origin software, (c) one or more features of the detected webinject content, and/or (d) one or more capabilities (e.g., intercepting communication or changing form contents) of the detected webinject content. This database can be used to classify webinject(s) detected on remote systems.
Delivering the detection webpage having detection code can further include configuring the detection code such that at least one of a source domain, a path, or an HTML structure of the detection webpage is configured to trigger an injection of the webinject content by the webinject. The detection code can include JavaScript or Content Security Policy (CSP). The detection webpage can be inserted into an Hypertext Markup Language (HTML) inline frame. The method can further include generating a classification of the one or more webinjects. Classifying the webinject based on the one or more fingerprints can further include determining an originating software of the webinject based on the one or more fingerprints. Classifying the webinject based on the one or more fingerprints can further include mapping the one or more fingerprints to a feature set of the webinject. The detection webpage can be delivered by a traffic generating entity. Delivering a detection webpage to a web browser can further include embedding, by the traffic generating entity, the detection webpage into an external webpage. Delivering a detection webpage to a web browser can occur upon receiving an indication of a user interaction with the content of a webpage, wherein the webpage is separate from the detection webpage. The webinject content can include added or modified content by the webinject.
In a second aspect, a system is provided for detection of webinjects. The system includes one or more computer systems programmed to perform operations that include delivering a detection webpage to a web browser. The detection webpage has detection code configured to detect a presence of the webinject in the detection webpage. The operations further include inspecting, by the detection code, rendering of content of the detection webpage in the browser to detect webinject content inserted into the detection webpage by the webinject. The webinject content includes one or more Hypertext Markup Language (HTML) components. The operations further include, if webinject content is detected, generating, by the detection code, a fingerprint for each of the one or more HTML components; transmitting, by the detection code, the one or more fingerprints to an external server; and classifying, by the external server, the webinject based on the one or more fingerprints.
Embodiments of the system can include any one or more of the below features. The operations can include transmitting to the external sever, by the detection code, one or more HTML components of the detected webinject content. The transmission can be on a portion of the executions of the detection code. The transmission can be on a small sample of the executions. For example, the transmission can be on 1% or less of the executions of the detection code. These HTML component(s) can be transmitted with their respective fingerprints. The operations can include identifying the origin software of the HTML component(s) by (i) searching for the HTML component(s) in sandboxed executions of software and/or (ii) searching through privately- and/or publicly-available data sources. The sandboxed executions of software may be associated with, may be related to, or possibly be the origin software. The operations can further include generating a database of (a) the fingerprint(s), (b) the name of the origin software, (c) one or more features of the detected webinject content, and/or (d) one or more capabilities (e.g. intercepting communication or changing form contents) of the detected webinject content. This database can be used to classify webinject(s) detected on remote systems.
Delivering the detection webpage having detection code can further include configuring the detection code such that at least one of a source domain, a path, or an HTML structure of the detection webpage is configured to trigger an injection of the webinject content by the webinject. The detection code can include JavaScript or Content Security Policy (CSP). The detection webpage is inserted into an Hypertext Markup Language (HTML) inline frame. The system can further include generating a classification of the one or more webinjects. Classifying the webinject based on the one or more fingerprints can further include determining an originating software of the webinject based on the one or more fingerprints. Classifying the webinject based on the one or more fingerprints can further include mapping the one or more fingerprints to a feature set of the webinject. The detection webpage can be delivered by a traffic generating entity. Delivering a detection webpage to a web browser can further include embedding, by the traffic generating entity, the detection webpage into an external webpage. Delivering a detection webpage to a web browser can occur upon receiving an indication of a user interaction with the content of a webpage, wherein the webpage is separate from the detection webpage. The webinject content can include added or modified content by the webinject.
Disclosed herein are exemplary embodiments of systems and methods for the remote detection of software, specifically by the detection of webinjects in a web browser. The detection and classification of webinjects can be particularly useful in researching how to better secure and protect computer systems, especially those connected to the Internet. In some instances, the automatic classification of webinjects enabled by the systems and methods described herein provides significant increases in processing efficiencies over conventional techniques. Remote detection can be achieved without the use of installed detection software on a system. For example, instead of installed detection software, software can be remotely detected on any web browser that visits an detection webpage. For the purposes of clarity and conciseness, the methods and systems of
In step 102 of the method 100, one or more detection webpages 204 are delivered to one or more web browser. In some embodiments, the one or more detection webpages 204 can be distributed by a third party traffic generating entity 206 with access to a high volume of web traffic (e.g., an advertising network, a website with a large number of daily visitors, etc.) that enables the one or more detection webpages 204 to reach a large number of browsers 208 across the Internet. In some embodiments, the system 200 may receive an indication of a user interaction with the content of a webpage in a browser. For example, the one or more detection webpages 204 can be delivered to a web browser 208 after a user clicks an advertisement in a webpage. The advertisement in the webpage can link to the detection webpage(s) 204 and may be acquired for the purpose of generating traffic to the detection webpage(s) 204. For instance, the advertisement can be configured such that, once a user clicks on an advertisement in the webpage, the browser is redirected to the detection webpage. In some embodiments, the one or more detection webpages 204 are not delivered directly as the main page of the web browser 28. Instead, the one or more webpages 204 can be embedded by a traffic generating entity 206 into an external main webpage 209 (e.g., a third-party webpage that is not part of the detection system), as one or more HTML inline frames (also referred to as an “iframe”. Iframes enable the embedding and/or displaying of a first HTML page into a second HTML page. One advantage of using iframes is that, because iframes can be made invisible to the user (and can be sandboxed and isolated from the external main webpage 209), there is minimal to zero impact to the user's navigation experience and/or to the operation of the traffic generating entity.
While some webinjects are injected into as many webpages 209 as possible (and therefore, into every detection webpage 204 associated with the webpage), some software (“webinject originator” 216) may only inject its webinject(s) 212 when a specific website is visited (e.g., online banking websites, social media websites, etc.). To detect this webinject 212, one or more source domains, one or more paths, and/or an HTML structure of the detection webpage 204 is configured to match the webinject targets (i.e., the online banking website, etc.). For example, the webinject 212 may only be injected by the originator 216 if the browser 208 is visiting the site:
In step 104, the detection code 202 is executed during and/or after the rendering of the detection webpage 204 in the browser 208, to detect the webinject 212 content on the detection webpage 204 Document Object Model (DOM) The detection code can inspect the rendering of the detection webpage 204 by using JavaScript functions that are triggered on specific webpage rendering events. The webinject content can include one or more Hypertext Markup Language (HTML) components. The detection code inspects the rendering of the detection webpage 204 in the web browser 208. This inspection can be done through the use of a JavaScript function that compares the content of the detection webpage 204 after the content is rendered with the content that were delivered, through monitoring specific JavaScript function calls that are commonly used by webinjects 212 or through using content security policy (CSP) rules that trigger an action on any change to the original delivered detection webpage 204.
In step 106, if detection code 202 detects webinject content in the detection webpage 204, the detection code 202 generates a set of fingerprints based on the webinject content. These fingerprints are generated using an algorithm that selects one or more webinject blocks of code, and normalizes the one or more blocks of code. An example of a webinject block of code is an inline HTML script tag added to the webpage. There may be one or more blocks of code belonging to one or more webinjects. For example, normalization of the blocks include removing parts of the blocks that are specific to the browser instance (such as unique identifiers), normalizing character case, etc. The algorithm then creates a unique identifier of each block's contents that can be smaller than the webinject content itself and that is unique for a particular content. This unique identifier, also referred to as fingerprint in this document, can be calculated using hashing functions or even simpler cyclic redundancy check (CRC) algorithms that produce a unique number for a given input content.
In step 108, code 202 transmits the fingerprints to one or more server(s) 214 where they are stored and/or processed. Additionally, on a small sample of the executions of the detection code 202, the detected webinject content is transmitted to an external server, along with the respective fingerprints. In an exemplary embodiment utilizing CSP, CSP reports are sent by browser 208 to server 214 if a webinject is detected, the fingerprint is then calculated by a method similar to the one described above but on the server 214 and using the contents of the CSP report.
The one or more fingerprints can be used to classify the webinject 212 and/or identify the originator 216 of the webinject 212. Thus, in step 110, server 214 classifies the webinject 212 based on the received fingerprints. The server 214 can classify the detected webinject into a specific category, based on a database that maps each of the fingerprints to details about the webinject 212 and/or originator 216. In some embodiments, method 100 can include generating a database including the fingerprint(s), the name of the origin software, and/or a list of features and/or capabilities of the detected webinject content. For example, features or capabilities can include intercepting communication or changing form contents. This database can be built manually and/or by automated processing of the webinject content blocks that are sent, along with the respective fingerprints, to the server 214 in step 108. Once these samples are received in server 214, they are used to identify the originator 216 of the webinject, by searching for the presence of the same blocks of webinject code in the sandboxed execution of the software and by searching other, open or commercially available data sources.
In some examples, some or all of the processing described above can be carried out on a personal computing device, on one or more centralized computing devices, or via cloud-based processing by one or more servers. In some examples, some types of processing occur on one device and other types of processing occur on another device. In some examples, some or all of the data described above can be stored on a personal computing device, in data storage hosted on one or more centralized computing devices, or via cloud-based storage. In some examples, some data are stored in one location and other data are stored in another location. In some examples, quantum computing can be used. In some examples, functional programming languages can be used. In some examples, electrical memory, such as flash-based memory, can be used.
The memory 320 stores information within the system 300. In some implementations, the memory 320 is a non-transitory computer-readable medium. In some implementations, the memory 320 is a volatile memory unit. In some implementations, the memory 320 is a non-volatile memory unit.
The storage device 330 is capable of providing mass storage for the system 300. In some implementations, the storage device 330 is a non-transitory computer-readable medium. In various different implementations, the storage device 330 may include, for example, a hard disk device, an optical disk device, a solid-date drive, a flash drive, or some other large capacity storage device. For example, the storage device may store long-term data (e.g., database data, file system data, etc.) The input/output device 340 provides input/output operations for the system 300. In some implementations, the input/output device 340 may include one or more of a network interface devices, e.g., an Ethernet card, a serial communication device, e.g., an RS-232 port, and/or a wireless interface device, e.g., an 802.11 card, a 3G wireless modem, or a 4G wireless modem. In some implementations, the input/output device may include driver devices configured to receive input data and send output data to other input/output devices, e.g., keyboard, printer and display devices 360. In some examples, mobile computing devices, mobile communication devices, and other devices may be used.
In some implementations, at least a portion of the approaches described above may be realized by instructions that upon execution cause one or more processing devices to carry out the processes and functions described above. Such instructions may include, for example, interpreted instructions such as script instructions, or executable code, or other instructions stored in a non-transitory computer readable medium. The storage device 330 may be implemented in a distributed way over a network, such as a server farm or a set of widely distributed servers, or may be implemented in a single computing device.
Although an example processing system has been described in
The term “system” may encompass all kinds of apparatus, devices, and machines for processing data, including by way of example a programmable processor, a computer, or multiple processors or computers. A processing system may include special purpose logic circuitry, e.g., an FPGA (field programmable gate array) or an ASIC (application specific integrated circuit). A processing system may include, in addition to hardware, code that creates an execution environment for the computer program in question, e.g., code that constitutes processor firmware, a protocol stack, a database management system, an operating system, or a combination of one or more of them.
A computer program (which may also be referred to or described as a program, software, a software application, a module, a software module, a script, or code) can be written in any form of programming language, including compiled or interpreted languages, or declarative or procedural languages, and it can be deployed in any form, including as a standalone program or as a module, component, subroutine, or other unit suitable for use in a computing environment. A computer program may, but need not, correspond to a file in a file system. A program can be stored in a portion of a file that holds other programs or data (e.g., one or more scripts stored in a markup language document), in a single file dedicated to the program in question, or in multiple coordinated files (e.g., files that store one or more modules, sub programs, or portions of code). A computer program can be deployed to be executed on one computer or on multiple computers that are located at one site or distributed across multiple sites and interconnected by a communication network.
The processes and logic flows described in this specification can be performed by one or more programmable computers executing one or more computer programs to perform functions by operating on input data and generating output. The processes and logic flows can also be performed by, and apparatus can also be implemented as, special purpose logic circuitry, e.g., an FPGA (field programmable gate array) or an ASIC (application specific integrated circuit).
Computers suitable for the execution of a computer program can include, by way of example, general or special purpose microprocessors or both, or any other kind of central processing unit. Generally, a central processing unit will receive instructions and data from a read-only memory or a random access memory or both. A computer generally includes a central processing unit for performing or executing instructions and one or more memory devices for storing instructions and data. Generally, a computer will also include, or be operatively coupled to receive data from or transfer data to, or both, one or more mass storage devices for storing data, e.g., magnetic, magneto optical disks, or optical disks. However, a computer need not have such devices. Moreover, a computer can be embedded in another device, e.g., a mobile telephone, a personal digital assistant (PDA), a mobile audio or video player, a game console, a Global Positioning System (GPS) receiver, or a portable storage device (e.g., a universal serial bus (USB) flash drive), to name just a few.
Computer readable media suitable for storing computer program instructions and data include all forms of nonvolatile memory, media and memory devices, including by way of example semiconductor memory devices. e.g., EPROM, EEPROM, and flash memory devices; magnetic disks, e.g., internal hard disks or removable disks; magneto optical disks; and CD-ROM and DVD-ROM disks. The processor and the memory can be supplemented by, or incorporated in, special purpose logic circuitry.
To provide for interaction with a user, embodiments of the subject matter described in this specification can be implemented on a computer having a display device, e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor, for displaying information to the user and a keyboard and a pointing device, e.g., a mouse or a trackball, by which the user can provide input to the computer. Other kinds of devices can be used to provide for interaction with a user as well; for example, feedback provided to the user can be any form of sensory feedback, e.g., visual feedback, auditory feedback, or tactile feedback; and input from the user can be received in any form, including acoustic, speech, or tactile input. In addition, a computer can interact with a user by sending documents to and receiving documents from a device that is used by the user; for example, by sending web pages to a web browser on a user's user device in response to requests received from the web browser.
Embodiments of the subject matter described in this specification can be implemented in a computing system that includes a back end component. e.g., as a data server, or that includes a middleware component, e.g., an application server, or that includes a front end component, e.g., a client computer having a graphical user interface or a Web browser through which a user can interact with an implementation of the subject matter described in this specification, or any combination of one or more such back end, middleware, or front end components. The components of the system can be interconnected by any form or medium of digital data communication, e.g., a communication network. Examples of communication networks include a local area network (“LAN”) and a wide area network (“WAN”), e.g., the Internet.
The computing system can include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other.
While this specification contains many specific implementation details, these should not be construed as limitations on the scope of what may be claimed, but rather as descriptions of features that may be specific to particular embodiments. Certain features that are described in this specification in the context of separate embodiments can also be implemented in combination in a single embodiment. Conversely, various features that are described in the context of a single embodiment can also be implemented in multiple embodiments separately or in any suitable sub-combination. Moreover, although features may be described above as acting in certain combinations and even initially claimed as such, one or more features from a claimed combination can in some cases be excised from the combination, and the claimed combination may be directed to a sub-combination or variation of a sub-combination.
Similarly, while operations are depicted in the drawings in a particular order, this should not be understood as requiring that such operations be performed in the particular order shown or in sequential order, or that all illustrated operations be performed, to achieve desirable results. In certain circumstances, multitasking and parallel processing may be advantageous. Moreover, the separation of various system components in the embodiments described above should not be understood as requiring such separation in all embodiments, and it should be understood that the described program components and systems can generally be integrated together in a single software product or packaged into multiple software products.
Particular embodiments of the subject matter have been described. Other embodiments are within the scope of the following claims. For example, the actions recited in the claims can be performed in a different order and still achieve desirable results. As one example, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In certain implementations, multitasking and parallel processing may be advantageous. Other steps or stages may be provided, or steps or stages may be eliminated, from the described processes. Accordingly, other implementations are within the scope of the following claims.
The phraseology and terminology used herein is for the purpose of description and should not be regarded as limiting.
The term “approximately”, the phrase “approximately equal to”, and other similar phrases, as used in the specification and the claims (e.g., “X has a value of approximately Y” or “X is approximately equal to Y”), should be understood to mean that one value (X) is within a predetermined range of another value (Y). The predetermined range may be plus or minus 20%, 10%, 5%, 3%, 1%, 0.1%, or less than 0.1%, unless otherwise indicated.
The indefinite articles “a” and “an,” as used in the specification and in the claims, unless clearly indicated to the contrary, should be understood to mean “at least one.” The phrase “and/or,” as used in the specification and in the claims, should be understood to mean “either or both” of the elements so conjoined, i.e., elements that are conjunctively present in some cases and disjunctively present in other cases. Multiple elements listed with “and/or” should be construed in the same fashion, i.e., “one or more” of the elements so conjoined. Other elements may optionally be present other than the elements specifically identified by the “and/or” clause, whether related or unrelated to those elements specifically identified. Thus, as a non-limiting example, a reference to “A and/or B”, when used in conjunction with open-ended language such as “comprising” can refer, in one embodiment, to A only (optionally including elements other than B); in another embodiment, to B only (optionally including elements other than A); in yet another embodiment, to both A and B (optionally including other elements); etc.
As used in the specification and in the claims, “or” should be understood to have the same meaning as “and/or” as defined above. For example, when separating items in a list, “or” or “and/or” shall be interpreted as being inclusive. i.e., the inclusion of at least one, but also including more than one, of a number or list of elements, and, optionally, additional unlisted items. Only terms clearly indicated to the contrary, such as “only one of or “exactly one of,” or, when used in the claims, “consisting of,” will refer to the inclusion of exactly one element of a number or list of elements. In general, the term “or” as used shall only be interpreted as indicating exclusive alternatives (i.e. “one or the other but not both”) when preceded by terms of exclusivity, such as “either,” “one of,” “only one of,” or “exactly one of.” “Consisting essentially of,” when used in the claims, shall have its ordinary meaning as used in the field of patent law.
As used in the specification and in the claims, the phrase “at least one,” in reference to a list of one or more elements, should be understood to mean at least one element selected from any one or more of the elements in the list of elements, but not necessarily including at least one of each and every element specifically listed within the list of elements and not excluding any combinations of elements in the list of elements. This definition also allows that elements may optionally be present other than the elements specifically identified within the list of elements to which the phrase “at least one” refers, whether related or unrelated to those elements specifically identified. Thus, as a non-limiting example, “at least one of A and B” (or, equivalently, “at least one of A or B,” or, equivalently “at least one of A and/or B”) can refer, in one embodiment, to at least one, optionally including more than one, A, with no B present (and optionally including elements other than B); in another embodiment, to at least one, optionally including more than one, B, with no A present (and optionally including elements other than A); in yet another embodiment, to at least one, optionally including more than one, A, and at least one, optionally including more than one, B (and optionally including other elements); etc.
The use of “including,” “comprising,” “having,” “containing,” “involving,” and variations thereof, is meant to encompass the items listed thereafter and additional items.
Use of ordinal terms such as “first,” “second,” “third,” etc., in the claims to modify a claim element does not by itself connote any priority, precedence, or order of one claim element over another or the temporal order in which acts of a method are performed. Ordinal terms are used merely as labels to distinguish one claim element having a certain name from another element having a same name (but for use of the ordinal term), to distinguish the claim elements.
The present application is a continuation of U.S. patent application Ser. No. 16/688,647, filed Nov. 19, 2019, which is a continuation of U.S. patent application Ser. No. 16/170,680, filed Oct. 25, 2018 and titled “Systems and Methods for Remote Detection of Software Through Browser Webinjects,” the entirety of which are incorporated herein by reference.
Number | Name | Date | Kind |
---|---|---|---|
5867799 | Lang et al. | Feb 1999 | A |
6016475 | Miller et al. | Jan 2000 | A |
6792401 | Nigro et al. | Sep 2004 | B1 |
7062572 | Hampton | Jun 2006 | B1 |
D525264 | Chotai | Jul 2006 | S |
D525629 | Chotai | Jul 2006 | S |
7100195 | Underwood | Aug 2006 | B1 |
7194769 | Lippmann et al. | Mar 2007 | B2 |
7290275 | Baudoin et al. | Oct 2007 | B2 |
D604740 | Matheny | Nov 2009 | S |
7650570 | Torrens | Jan 2010 | B2 |
7747778 | King | Jun 2010 | B1 |
7748038 | Olivier et al. | Jun 2010 | B2 |
7827607 | Sobel et al. | Nov 2010 | B2 |
D630645 | Tokunaga | Jan 2011 | S |
7971252 | Lippmann et al. | Jun 2011 | B2 |
D652048 | Joseph | Jan 2012 | S |
D667022 | LoBosco | Sep 2012 | S |
8370933 | Buckler | Feb 2013 | B1 |
8429630 | Nickolov et al. | Apr 2013 | B2 |
D682287 | Cong | May 2013 | S |
D688260 | Pearcy | Aug 2013 | S |
8504556 | Rice et al. | Aug 2013 | B1 |
8505094 | Xuewen | Aug 2013 | B1 |
D691164 | Lim | Oct 2013 | S |
D694252 | Helm | Nov 2013 | S |
D694253 | Helm | Nov 2013 | S |
8584233 | Yang | Nov 2013 | B1 |
8621621 | Burns et al. | Dec 2013 | B1 |
8661146 | Alex et al. | Feb 2014 | B2 |
D700616 | Chao | Mar 2014 | S |
8677481 | Lee | Mar 2014 | B1 |
8752183 | Heiderich | Jun 2014 | B1 |
8825662 | Kingman et al. | Sep 2014 | B1 |
8949988 | Adams | Feb 2015 | B2 |
D730918 | Park | Jun 2015 | S |
9053210 | Elnikety | Jun 2015 | B2 |
9075990 | Yang | Jul 2015 | B1 |
D740847 | Yampolskiy et al. | Oct 2015 | S |
D740848 | Bolts | Oct 2015 | S |
D741351 | Kito | Oct 2015 | S |
D746832 | Pearcy | Jan 2016 | S |
9241252 | Dua et al. | Jan 2016 | B2 |
9244899 | Greenbaum | Jan 2016 | B1 |
9294498 | Yampolskiy et al. | Mar 2016 | B1 |
D754690 | Park | Apr 2016 | S |
D754696 | Follett | Apr 2016 | S |
D756371 | Bertnick | May 2016 | S |
D756372 | Bertnick | May 2016 | S |
D756392 | Yun | May 2016 | S |
D759084 | Yampolskiy et al. | Jun 2016 | S |
D759689 | Olson | Jun 2016 | S |
9372994 | Yampolskiy et al. | Jun 2016 | B1 |
9373144 | Ng et al. | Jun 2016 | B1 |
D760782 | Kendler | Jul 2016 | S |
9384206 | Bono et al. | Jul 2016 | B1 |
9401926 | Dubow et al. | Jul 2016 | B1 |
9407658 | Kuskov et al. | Aug 2016 | B1 |
9420049 | Talmor | Aug 2016 | B1 |
9424333 | Bisignani | Aug 2016 | B1 |
9479526 | Yang | Oct 2016 | B1 |
D771695 | Yampolskiy et al. | Nov 2016 | S |
D772276 | Yampolskiy et al. | Nov 2016 | S |
9501647 | Yampolskiy et al. | Nov 2016 | B2 |
D773507 | Sagrillo | Dec 2016 | S |
D775635 | Raji | Jan 2017 | S |
D776136 | Chen | Jan 2017 | S |
D776153 | Yampolskiy et al. | Jan 2017 | S |
D777177 | Chen | Jan 2017 | S |
9560072 | Xu | Jan 2017 | B1 |
D778927 | Bertnick | Feb 2017 | S |
D778928 | Bertnick | Feb 2017 | S |
D779512 | Kimura | Feb 2017 | S |
D779514 | Baris | Feb 2017 | S |
D779531 | List | Feb 2017 | S |
D780770 | Sum | Mar 2017 | S |
D785009 | Lim | Apr 2017 | S |
D785010 | Bachman | Apr 2017 | S |
D785016 | Berwick | Apr 2017 | S |
9620079 | Curtis | Apr 2017 | B2 |
D787530 | Huang | May 2017 | S |
D788128 | Wada | May 2017 | S |
9641547 | Yampolskiy et al. | May 2017 | B2 |
9646110 | Byrne | May 2017 | B2 |
D789947 | Sun | Jun 2017 | S |
D789957 | Wu | Jun 2017 | S |
D791153 | Rice | Jul 2017 | S |
D791834 | Eze | Jul 2017 | S |
D792427 | Weaver | Jul 2017 | S |
D795891 | Kohan | Aug 2017 | S |
D796523 | Bhandari | Sep 2017 | S |
D801989 | Iketsuki | Nov 2017 | S |
D803237 | Wu | Nov 2017 | S |
D804528 | Martin | Dec 2017 | S |
D806735 | Olsen | Jan 2018 | S |
D806737 | Chung | Jan 2018 | S |
D809523 | Lipka | Feb 2018 | S |
D812633 | Saneii | Mar 2018 | S |
D814483 | Gavaskar | Apr 2018 | S |
D815119 | Chalker | Apr 2018 | S |
D815148 | Martin | Apr 2018 | S |
D816105 | Rudick | Apr 2018 | S |
D816116 | Selassie | Apr 2018 | S |
9954893 | Zhao | Apr 2018 | B1 |
D817970 | Chang | May 2018 | S |
D817977 | Kato | May 2018 | S |
D819687 | Yampolskiy et al. | Jun 2018 | S |
10044750 | Livshits et al. | Aug 2018 | B2 |
10079854 | Scott | Sep 2018 | B1 |
10142364 | Baukes et al. | Nov 2018 | B2 |
10185924 | McClintock et al. | Jan 2019 | B1 |
10217071 | Mo et al. | Feb 2019 | B2 |
10230753 | Yampolskiy et al. | Mar 2019 | B2 |
10230764 | Ng et al. | Mar 2019 | B2 |
10235524 | Ford | Mar 2019 | B2 |
10257219 | Geil et al. | Apr 2019 | B1 |
10305854 | Alizadeh-Shabdiz et al. | May 2019 | B2 |
10331502 | Hart | Jun 2019 | B1 |
10339321 | Tedeschi | Jul 2019 | B2 |
10339484 | Pai et al. | Jul 2019 | B2 |
10348755 | Shavell et al. | Jul 2019 | B1 |
10412083 | Zou et al. | Sep 2019 | B2 |
10469515 | Helmsen et al. | Nov 2019 | B2 |
10491619 | Yampolskiy et al. | Nov 2019 | B2 |
10491620 | Yampolskiy et al. | Nov 2019 | B2 |
10521583 | Bagulho Monteiro Pereira | Dec 2019 | B1 |
D880512 | Greenwald et al. | Apr 2020 | S |
10776483 | Bagulho Monteiro Pereira | Sep 2020 | B2 |
20010044798 | Nagral et al. | Nov 2001 | A1 |
20020083077 | Vardi | Jun 2002 | A1 |
20020133365 | Grey et al. | Sep 2002 | A1 |
20020164983 | Raviv et al. | Nov 2002 | A1 |
20030050862 | Bleicken et al. | Mar 2003 | A1 |
20030123424 | Jung | Jul 2003 | A1 |
20030187967 | Walsh et al. | Oct 2003 | A1 |
20040003284 | Campbell et al. | Jan 2004 | A1 |
20040010709 | Baudoin et al. | Jan 2004 | A1 |
20040024859 | Bloch et al. | Feb 2004 | A1 |
20040098375 | DeCarlo | May 2004 | A1 |
20040133561 | Burke | Jul 2004 | A1 |
20040133689 | Vasisht | Jul 2004 | A1 |
20040193907 | Patanella | Sep 2004 | A1 |
20040193918 | Green et al. | Sep 2004 | A1 |
20040199791 | Poletto et al. | Oct 2004 | A1 |
20040199792 | Tan et al. | Oct 2004 | A1 |
20040221296 | Ogielski et al. | Nov 2004 | A1 |
20040250122 | Newton | Dec 2004 | A1 |
20040250134 | Kohler et al. | Dec 2004 | A1 |
20050066195 | Jones | Mar 2005 | A1 |
20050071450 | Allen et al. | Mar 2005 | A1 |
20050076245 | Graham et al. | Apr 2005 | A1 |
20050080720 | Betz et al. | Apr 2005 | A1 |
20050108415 | Turk et al. | May 2005 | A1 |
20050131830 | Juarez et al. | Jun 2005 | A1 |
20050138413 | Lippmann et al. | Jun 2005 | A1 |
20050160002 | Roetter et al. | Jul 2005 | A1 |
20050234767 | Bolzman et al. | Oct 2005 | A1 |
20050278726 | Cano | Dec 2005 | A1 |
20060036335 | Banter | Feb 2006 | A1 |
20060107226 | Matthews | May 2006 | A1 |
20060173992 | Weber et al. | Aug 2006 | A1 |
20060212925 | Shull et al. | Sep 2006 | A1 |
20060253581 | Dixon et al. | Nov 2006 | A1 |
20070016948 | Dubrovsky et al. | Jan 2007 | A1 |
20070067845 | Wiemer et al. | Mar 2007 | A1 |
20070113282 | Ross | May 2007 | A1 |
20070143851 | Nicodemus et al. | Jun 2007 | A1 |
20070179955 | Croft et al. | Aug 2007 | A1 |
20070198275 | Malden et al. | Aug 2007 | A1 |
20070214151 | Thomas et al. | Sep 2007 | A1 |
20070282730 | Carpenter et al. | Dec 2007 | A1 |
20080017526 | Prescott et al. | Jan 2008 | A1 |
20080033775 | Dawson et al. | Feb 2008 | A1 |
20080047018 | Baudoin et al. | Feb 2008 | A1 |
20080091834 | Norton | Apr 2008 | A1 |
20080140495 | Bhamidipaty et al. | Jun 2008 | A1 |
20080140728 | Fraser et al. | Jun 2008 | A1 |
20080162931 | Lord et al. | Jul 2008 | A1 |
20080172382 | Prettejohn | Jul 2008 | A1 |
20080208995 | Takahashi et al. | Aug 2008 | A1 |
20080209565 | Baudoin et al. | Aug 2008 | A2 |
20080222287 | Bahl et al. | Sep 2008 | A1 |
20080262895 | Hofmeister et al. | Oct 2008 | A1 |
20090044272 | Jarrett | Feb 2009 | A1 |
20090064337 | Chien | Mar 2009 | A1 |
20090094265 | Vlachos et al. | Apr 2009 | A1 |
20090125427 | Atwood et al. | May 2009 | A1 |
20090132861 | Costa et al. | May 2009 | A1 |
20090161629 | Purkayastha et al. | Jun 2009 | A1 |
20090193054 | Karimisetty et al. | Jul 2009 | A1 |
20090216700 | Bouchard et al. | Aug 2009 | A1 |
20090265787 | Baudoin et al. | Oct 2009 | A9 |
20090293128 | Lippmann et al. | Nov 2009 | A1 |
20090299802 | Brennan | Dec 2009 | A1 |
20090300768 | Krishnamurthy et al. | Dec 2009 | A1 |
20090319420 | Sanchez et al. | Dec 2009 | A1 |
20090323632 | Nix | Dec 2009 | A1 |
20090328063 | Corvera et al. | Dec 2009 | A1 |
20100017880 | Masood | Jan 2010 | A1 |
20100042605 | Cheng et al. | Feb 2010 | A1 |
20100057582 | Arfin | Mar 2010 | A1 |
20100186088 | Banerjee | Jul 2010 | A1 |
20100205042 | Mun | Aug 2010 | A1 |
20100218256 | Thomas et al. | Aug 2010 | A1 |
20100262444 | Atwal et al. | Oct 2010 | A1 |
20100275263 | Bennett et al. | Oct 2010 | A1 |
20100281124 | Westman et al. | Nov 2010 | A1 |
20100281151 | Ramankutty et al. | Nov 2010 | A1 |
20110137704 | Mitra | Jun 2011 | A1 |
20110145576 | Bettan | Jun 2011 | A1 |
20110185403 | Dolan et al. | Jul 2011 | A1 |
20110213742 | Lemmond et al. | Sep 2011 | A1 |
20110219455 | Bhagwan et al. | Sep 2011 | A1 |
20110231395 | Vadlamani | Sep 2011 | A1 |
20110239300 | Klein et al. | Sep 2011 | A1 |
20110282997 | Prince | Nov 2011 | A1 |
20110296519 | Ide et al. | Dec 2011 | A1 |
20120036263 | Madden et al. | Feb 2012 | A1 |
20120089745 | Turakhia | Apr 2012 | A1 |
20120158725 | Molloy et al. | Jun 2012 | A1 |
20120166458 | Laudanski et al. | Jun 2012 | A1 |
20120198558 | Liu et al. | Aug 2012 | A1 |
20120215892 | Wanser | Aug 2012 | A1 |
20120255027 | Kanakapura et al. | Oct 2012 | A1 |
20120291129 | Shulman et al. | Nov 2012 | A1 |
20130014253 | Vivian et al. | Jan 2013 | A1 |
20130055386 | Kim | Feb 2013 | A1 |
20130060351 | Imming et al. | Mar 2013 | A1 |
20130080505 | Nielsen et al. | Mar 2013 | A1 |
20130086521 | Grossele | Apr 2013 | A1 |
20130086687 | Chess | Apr 2013 | A1 |
20130091574 | Howes et al. | Apr 2013 | A1 |
20130124644 | Hunt et al. | May 2013 | A1 |
20130124653 | Vick et al. | May 2013 | A1 |
20130142050 | Luna | Jun 2013 | A1 |
20130173791 | Longo | Jul 2013 | A1 |
20130227078 | Wei et al. | Aug 2013 | A1 |
20130263270 | Cote | Oct 2013 | A1 |
20130291105 | Zheng | Oct 2013 | A1 |
20130298244 | Kumar et al. | Nov 2013 | A1 |
20130305368 | Ford | Nov 2013 | A1 |
20130333038 | Chien | Dec 2013 | A1 |
20130347116 | Flores | Dec 2013 | A1 |
20140006129 | Heath | Jan 2014 | A1 |
20140019196 | Wiggins | Jan 2014 | A1 |
20140052998 | Bloom | Feb 2014 | A1 |
20140108474 | David et al. | Apr 2014 | A1 |
20140114755 | Mezzacca | Apr 2014 | A1 |
20140114843 | Klein et al. | Apr 2014 | A1 |
20140130158 | Wang et al. | May 2014 | A1 |
20140146370 | Banner et al. | May 2014 | A1 |
20140189098 | MaGill et al. | Jul 2014 | A1 |
20140204803 | Nguyen et al. | Jul 2014 | A1 |
20140244317 | Roberts et al. | Aug 2014 | A1 |
20140283068 | Call et al. | Sep 2014 | A1 |
20140288996 | Rence et al. | Sep 2014 | A1 |
20140304816 | Klein et al. | Oct 2014 | A1 |
20140334336 | Chen et al. | Nov 2014 | A1 |
20140337633 | Yang et al. | Nov 2014 | A1 |
20140344332 | Giebler | Nov 2014 | A1 |
20150033331 | Stern et al. | Jan 2015 | A1 |
20150033341 | Schmidtler et al. | Jan 2015 | A1 |
20150052607 | Al Hamami | Feb 2015 | A1 |
20150074579 | Gladstone et al. | Mar 2015 | A1 |
20150081860 | Kuehnel et al. | Mar 2015 | A1 |
20150088968 | Wei | Mar 2015 | A1 |
20150156084 | Kaminsky et al. | Jun 2015 | A1 |
20150180883 | Aktas et al. | Jun 2015 | A1 |
20150248280 | Pillay | Sep 2015 | A1 |
20150261955 | Huang et al. | Sep 2015 | A1 |
20150264061 | Ibatullin et al. | Sep 2015 | A1 |
20150288706 | Marshall | Oct 2015 | A1 |
20150288709 | Singhal et al. | Oct 2015 | A1 |
20150310188 | Ford et al. | Oct 2015 | A1 |
20150310213 | Ronen et al. | Oct 2015 | A1 |
20150317672 | Espinoza et al. | Nov 2015 | A1 |
20150347756 | Hidayat | Dec 2015 | A1 |
20150350229 | Mitchell | Dec 2015 | A1 |
20150381649 | Schultz et al. | Dec 2015 | A1 |
20160036849 | Zakian | Feb 2016 | A1 |
20160065613 | Cho et al. | Mar 2016 | A1 |
20160088015 | Sivan | Mar 2016 | A1 |
20160119373 | Fausto et al. | Apr 2016 | A1 |
20160140466 | Sidebottom | May 2016 | A1 |
20160147992 | Zhao et al. | May 2016 | A1 |
20160162602 | Bradish et al. | Jun 2016 | A1 |
20160171415 | Yampolskiy et al. | Jun 2016 | A1 |
20160173522 | Yampolskiy et al. | Jun 2016 | A1 |
20160182537 | Tatourian | Jun 2016 | A1 |
20160189301 | Ng et al. | Jun 2016 | A1 |
20160191554 | Kaminsky | Jun 2016 | A1 |
20160205126 | Boyer et al. | Jul 2016 | A1 |
20160212101 | Reshadi | Jul 2016 | A1 |
20160241560 | Reshadi | Aug 2016 | A1 |
20160248797 | Yampolskiy et al. | Aug 2016 | A1 |
20160253500 | Alme et al. | Sep 2016 | A1 |
20160259945 | Yampolskiy et al. | Sep 2016 | A1 |
20160337387 | Hu et al. | Nov 2016 | A1 |
20160344769 | Li | Nov 2016 | A1 |
20160344801 | Akkarawittayapoom | Nov 2016 | A1 |
20160364496 | Li | Dec 2016 | A1 |
20160373485 | Kamble | Dec 2016 | A1 |
20170048267 | Yampolskiy et al. | Feb 2017 | A1 |
20170063901 | Muddu et al. | Mar 2017 | A1 |
20170104783 | Vanunu | Apr 2017 | A1 |
20170161253 | Silver | Jun 2017 | A1 |
20170161409 | Martin | Jun 2017 | A1 |
20170223002 | Sabin | Aug 2017 | A1 |
20170236078 | Rasumov | Aug 2017 | A1 |
20170237764 | Rasumov | Aug 2017 | A1 |
20170264623 | Ficarra | Sep 2017 | A1 |
20170279843 | Schultz et al. | Sep 2017 | A1 |
20170300911 | Alnajem | Oct 2017 | A1 |
20170316324 | Barrett et al. | Nov 2017 | A1 |
20170318045 | Johns | Nov 2017 | A1 |
20170324555 | Wu | Nov 2017 | A1 |
20170324766 | Gonzalez | Nov 2017 | A1 |
20170337487 | Nock et al. | Nov 2017 | A1 |
20180013716 | Connell et al. | Jan 2018 | A1 |
20180103043 | Kupreev et al. | Apr 2018 | A1 |
20180121659 | Sawhney | May 2018 | A1 |
20180123934 | Gissing et al. | May 2018 | A1 |
20180124110 | Hunt et al. | May 2018 | A1 |
20180139180 | Napchi et al. | May 2018 | A1 |
20180157468 | Stachura | Jun 2018 | A1 |
20180322584 | Crabtree et al. | Nov 2018 | A1 |
20180336348 | Ng | Nov 2018 | A1 |
20180337938 | Kneib et al. | Nov 2018 | A1 |
20180337941 | Kraning et al. | Nov 2018 | A1 |
20180365519 | Pollard et al. | Dec 2018 | A1 |
20180375896 | Wang et al. | Dec 2018 | A1 |
20190034845 | Mo et al. | Jan 2019 | A1 |
20190089711 | Faulkner | Mar 2019 | A1 |
20190098025 | Lim | Mar 2019 | A1 |
20190140925 | Pon et al. | May 2019 | A1 |
20190147378 | Mo et al. | May 2019 | A1 |
20190215331 | Anakata et al. | Jul 2019 | A1 |
20190303574 | Lamay et al. | Oct 2019 | A1 |
20190379632 | Dahlberg et al. | Dec 2019 | A1 |
20190392252 | Fighel et al. | Dec 2019 | A1 |
20200053127 | Brotherton et al. | Feb 2020 | A1 |
20200074084 | Dorrans et al. | Mar 2020 | A1 |
Number | Date | Country |
---|---|---|
WO-2017142694 | Aug 2017 | WO |
WO-2019023045 | Jan 2019 | WO |
Entry |
---|
U.S. Appl. No. 15/271,655 Published as: US2018/0083999, Self-Published Security Risk Management, filed Sep. 21, 2016. |
U.S. Appl. No. 15/377,574 U.S. Pat. No. 9,705,932, Methods and Systems for Creating, De-Duplicating, and Accessing Data Using an Object Storage System, filed Dec. 13, 2016. |
U.S. Appl. No. 14/021,585 U.S. Pat. No. 9,438,615 Published as: US2015/0074579, Security Risk Management, filed Sep. 9, 2013. |
U.S. Appl. No. 15/216,955 Published as: US2016/0330231, Methods for Using Organizational Behavior for Risk Ratings, filed Jul. 22, 2016. |
U.S. Appl. No. 15/239,063 Published as: US2017/0093901, Security Risk Management, filed Aug. 17, 2016. |
U.S. Appl. No. 16/405,121 Published as: US2019/0260791, Methods for Using Organizational Behavior for Risk Ratings, filed May 7, 2019. |
U.S. Appl. No. 13/240,572 Published as: US2016/0205126, Information Technology Security Assessment System, filed Sep. 22, 2011. |
U.S. Appl. No. 14/944,484 U.S. Pat. No. 9,973,524 Published as: US2016/0323308, Information Technology Security Assessment System, filed Nov. 18, 2015. |
U.S. Appl. No. 15/142,677 U.S. Pat. No. 9,830,569 Published as: US/2016/0239772, Security Assesment Using Service Provider Digital Asset Information, filed Apr. 29, 2016. |
U.S. Appl. No. 15/134,845 U.S. Pat. No. 9,680,858, Annotation Platform for a Security Risk System, filed Apr. 21, 2016. |
U.S. Appl. No. 15/044,952 Published as: US2017/0236077, Relationships Among Technology Assets and Services and the Entities Resposibile for Them, filed Feb. 16, 2016. |
U.S. Appl. No. 15/089,375 U.S. Pat. No. 10,176,445 Published as: US2017/0236079, Relationships Among Technology Assets and Services and the Entities Responsible for Them, filed Apr. 1, 2016. |
U.S. Appl. No. 29/598,298 U.S. Pat. No. D. 835,631, Computer Display Screen with Graphical User Interface, filed Mar. 24, 2017. |
U.S. Appl. No. 29/598,299 U.S. Pat. No. D. 818,475, Computer Display with Security Ratings Graphical User Interface, filed Mar. 24, 2017. |
U.S. Appl. No. 29/599,622, Computer Display with Security Ratings Graphical User Interface, filed Apr. 5, 2017. |
U.S. Appl. No. 29/599,620, Computer Display with Security Ratings Graphical User Interface, filed Apr. 5, 2017. |
U.S. Appl. No. 16/015,686, Methods for Mapping IP Addresses and Domains to Organizations Using User Activity Data, filed Jun. 22, 2018. |
U.S. Appl. No. 16/543,075, Methods for Mapping IP Addresses and Domains to Organizations Using User Activity Data, filed Aug. 16, 2019. |
U.S. Appl. No. 16/738,825, Methods for Mapping IP Addresses and Domains to Organizations Using User Activity Data, filed Jan. 9, 2020. |
U.S. Appl. No. 15/918,286, Correlated Risk in Cybersecurity, filed Mar. 12, 2018. |
U.S. Appl. No. 16/292,956, Correlated Risk in Cybersecurity, filed May 5, 2019. |
U.S. Appl. No. 16/795,056, Correlated Risk in Cybersecurity, filed Feb. 19, 2020. |
U.S. Appl. No. 16/170,680, Systems and Methods for Remote Detection of Software Through Browser Webinjects, filed Oct. 25, 2018. |
U.S. Appl. No. 16/688,647, Systems and Methods for Remote Detection of Software Through Browser Webinjects, filed Nov. 19, 2019. |
U.S. Appl. No. 15/954,921, Systems and Methods for External Detection of Misconfigured Systems, filed Apr. 17, 2018. |
U.S. Appl. No. 16/549,764, Systems and Methods for Inferring Entity Relationships Via Network Communications of Users or User Devices, filed Aug. 23, 2019. |
U.S. Appl. No. 16/787,650, Systems and Methods for Inferring Entity Relationships Via Network Communications of Users or User Devices, filed Feb. 11, 2020. |
U.S. Appl. No. 16/583,991, Systems and Methods for Network Asset Discovery and Association Thereof With Entities, filed Sep. 26, 2019. |
U.S. Appl. No. 29/666,942, Computer Display With Graphical User Interface, filed Oct. 17, 2018. |
U.S. Appl. No. 16/360,641, Systems and Methods for Forecasting Cybersecurity Ratings Based on Event-Rate Scenarios, filed Mar. 21, 2019. |
U.S. Appl. No. 16/514,771, Systems and Methods for Generating Security Improvement Plans for Entities, filed Jul. 17, 2019. |
U.S. Appl. No. 16/922,273, Systems and Methods for Generating Security Improvement Plans for Entities, filed Jul. 7, 2020. |
U.S. Appl. No. 29/677,306, Computer Display With Corporate Hierarchy Graphical User Interface Computer Display With Corporate Hierarchy Graphical User Interface, filed Jan. 18, 2019. |
U.S. Appl. No. 16/775,840, Systems and Methods for Assessing Cybersecurity State of Entities Based on Computer Network Characterization, filed Jan. 29, 2020. |
U.S. Appl. No. 16/779,437, Systems and Methods for Rapidly Generating Security Ratings, filed Jan. 31, 2020. |
U.S. Appl. No. 16/802,232, Systems and Methods for Improving a Security Profile of an Entity Based on Peer Security Profiles, filed Feb. 26, 2020. |
U.S. Appl. No. 16/942,452, Systems and Methods for Improving a Security Profile of an Entity Based on Peer Security Profiles, filed Jul. 29, 2020. |
U.S. Appl. No. 29/725,724, Computer Display With Risk Vectors Graphical User Interface, filed Feb. 26, 2020. |
U.S. Appl. No. 29/736,641, Computer Display With Peer Analytics Graphical User Interface, filed Jun. 2, 2020. |
U.S. Appl. No. 16/884,607, Systems and Methods for Managing Cybersecurity Alerts, filed May 27, 2020. |
U.S. Appl. No. 15/271,655, the Office Actions dated Feb. 21, 2017 and Aug. 18, 2017. |
U.S. Appl. No. 15/377,574, now U.S. Pat. No. 9,705,932, the Office Action dated Mar. 2, 2017 and the Notice of Allowance dated Jun. 1, 2017. |
U.S. Appl. No. 14/021,585, now U.S. Pat. No. 9,438,615, the Office Action dated Mar. 11, 2016 and the Notice of Allowance dated Aug. 9, 2016. |
U.S. Appl. No. 15/216,955, now U.S. Pat. No. 10,326,786, the Office Actions dated Nov. 4, 2016, Mar. 9, 2017, Jun. 6, 2017, Dec. 5, 2017, and Aug. 29, 2018, and the Notice of Allowance dated Feb. 6, 2019. |
U.S. Appl. No. 15/239,063, now U.S. Pat. No. 10,341,370, the Office Action dated Mar. 21, 2018 and the Notice of Allowance dated Jan. 14, 2019. |
U.S. Appl. No. 16/405,121, the Office Actions dated Aug. 1, 2019 and Nov. 21, 2019 and the Notices of Allowance dated May 22, 2020 and Jul. 10, 2020. |
U.S. Appl. No. 13/240,572, the Office Actions dated Nov. 21, 2013, Jun. 16, 2014, Feb. 27, 2015, Jun. 3, 2015, Oct. 26, 2015, Mar. 10, 2016 Feb. 13, 2017; and the Notice of Allowance dated Jun. 1, 2020. |
U.S. Appl. No. 14/944,484, now U.S. Pat. No. 9,973,524, the Office Actions dated Mar. 11, 2016, Jul. 5, 2016, and Jan. 17, 2017 and the Notice of Allowance dated Oct. 20, 2017. |
U.S. Appl. No. 15/142,677, now U.S. Pat. No. 9,830,569, the Office Actions dated Jul. 26, 2016, and Apr. 24, 2017 and the Notice of Allowance dated Oct. 11, 2017. |
U.S. Appl. No. 15/134,845, now U.S. Pat. No. 9,680,858, the Office Actions dated Jul. 19, 2016 and Jan. 26, 2017, and the Notices of Allowance dated Apr. 27, 2017 and May 9, 2017. |
U.S. Appl. No. 15/044,952, the Office Actions dated Jul. 8, 2019 and Feb. 21, 2020. |
U.S. Appl. No. 15/089,375, now U.S. Pat. No. 10,176,445, the Office Actions dated Sep. 9, 2016, May 17, 2017, and Nov. 17, 2017 and the Notice of Allowance dated Aug. 9, 2018. |
U.S. Appl. No. 29/598,298, now U.S. Pat. No. D. 835,631, the Notice of Allowance dated Aug. 15, 2018. |
U.S. Appl. No. 29/598,299, now U.S. Pat. No. D. 818,475, the Notice of Allowance dated Jan. 2, 2018. |
U.S. Appl. No. 29/599,622, now U.S. Pat. No. D. 847,169, the Notice of Allowance dated Dec. 11, 2018. |
U.S. Appl. No. 29/599,620, now U.S. Pat. No. D. 846,562, the Office Action dated May 3, 2018, the Notice of Allowance dated Nov. 27, 2018. |
U.S. Appl. No. 16/015,686, now U.S. Pat. No. 10,425,380, the Office Action dated Nov. 16, 2018 and the Notice of Allowance dated May 10, 2019. |
U.S. Appl. No. 16/543,075, the Notice of Allowance dated Sep. 25, 2019. |
U.S. Appl. No. 16/738,825, the Office Actions dated Jul. 8, 2019 and Feb. 21, 2020. |
U.S. Appl. No. 15/918,286, now U.S. Pat. No. 10,257,219, the Office Action dated Aug. 7, 2018 and the Notice of Allowance dated Nov. 29, 2018. |
U.S. Appl. No. 16/292,956, the Office Action dated Jul. 10, 2019 and the Notices of Allowance dated Jan. 8, 2020 and Jan. 27, 2020. |
U.S. Appl. No. 16/795,056, the Office Action dated May 1, 2020. |
U.S. Appl. No. 16/170,680, the Office Action dated Mar. 26, 2019; the Notices of Allowance dated Oct. 29, 2019 and Aug. 27, 2019. |
U.S. Appl. No. 16/688,647, the Office Action dated Jan. 29, 2020; the Notice of Allowance dated May 12, 2020. |
U.S. Appl. No. 15/954,921, the Office Actions dated Sep. 4, 2018, Jan. 3, 2019, Aug. 19, 2019, and Dec. 5, 2019; Advisory Action dated Mar. 3, 2020, and the Notice of Allowance dated Jul. 7, 2020. |
U.S. Appl. No. 16/787,650, the Notice of Allowance dated Apr. 7, 2020. |
U.S. Appl. No. 16/583,991, the Office Action dated Jan. 13, 2020. |
U.S. Appl. No. 29/666,942, the Notice of Allowance dated Apr. 30, 2020. |
U.S. Appl. No. 16/360,641, the Office Action dated Aug. 7, 2019 and Feb. 20, 2020. |
U.S. Appl. No. 16/514,771, the Office Action dated Dec. 4, 2019; the Notice of Allowance dated Mar. 18, 2020. |
U.S. Appl. No. 29/677,306, the Notice of Allowance dated Aug. 20, 2020. |
U.S. Appl. No. 16/775,840, the Notice of Allowance dated May 19, 2020. |
U.S. Appl. No. 16/779,437, the Notice of Allowance dated Aug. 12, 2020. |
U.S. Appl. No. 16/802,232, the Notice of Allowance dated Apr. 24, 2020. |
“About Neo4j,” 1 page. |
“Amazon Mechanical Turk,” accessed on the internet at https://www.mturk.com/; 7 pages. |
“An Executive View of IT Governance,” IT Governance Institute, 2009, 32 pages. |
“Assessing Risk in Turbulent Times,” A Workshop for Information Security Executives, Glassmeyter/McNamee Center for Digital Strategies, Tuck School of Business at Dartmouth, Institute for Information Infrastructure Protection, 2009, 17 pages. |
“Assuring a Trusted and Resilient Information and Communications Infrastructure,” Cyberspace Policy Review, May 2009, 76 pages. |
“Computer Network Graph,” http://www.opte.org; 1 page. |
“Creating Transparency with Palantir,” accessed on the internet at https://www.youtube.com/watch?v=8cbGChfagUA; Jul. 5, 2012; 1 page. |
“Master Security Criteria,” Version 3.0, BITS Financial Services Security Laboratory, Oct. 2001, 47 pages. |
“Neo4j (neo4j.com),” accessed on the internet at https://web.archive.org/web/20151220150341/http://neo4j.com:80/developer/guide-data-visualization/; Dec. 20, 2015; 1 page. |
“Palantir.com,” accessed on the internet at http://www.palantir.com/; Dec. 2015; 2 pages. |
“Report on Controls Placed in Operation and Test of Operating Effectiveness,” EasCorp, Jan. 1 through Dec. 31, 2008, prepared by Crowe Horwath, 58 pages. |
“Shared Assessments: Getting Started,” BITS, 2008, 4 pages. |
“Twenty Critical Controls for Effective Cyber Defense: Consensus Audit,” Version 2.3, Nov. 13, 2009, retrieved on Apr. 9, 2010 from http://www.sans.org/critical-security-controls/print.php., 52 pages. |
2009 Data Breach Investigations Report, study conducted by Verizon Business RISK Team, 52 pages. |
Application as filed, and pending claims of U.S. Appl. No. 13/240,572 as of Nov. 18, 2015, 45 pages. |
Artz, Michael Lyle, “NetSPA: A Network Security Planning Architecture,” Massachusetts Institute of Technology, May 24, 2002, 97 pages. |
Bhilare et al., “Protecting Intellectual Property and Sensitive Information in Academic Campuses from Trusted Insiders: Leveraging Active Directory”, SIGUCC, Oct. 2009 (5 pages). |
BitSight, “Cyber Security Myths Versus Reality: How Optimism Bias Contributes to Inaccurate Perceptions of Risk”, Jun. 2015, Dimensional Research, pp. 1-9. |
Borgatti, et al., “On Social Network Analysis in a Supply Chain Context,” Journal of Supply Chain Management; 45(2): 5-22; Apr. 2009, 18 pages. |
Boyer, Stephen, et al., Playing with Blocks: SCAP-Enable Higher-Level Analyses, MIT Lincoln Laboratory, 5th Annual IT Security Automation Conference, Oct. 26-29, 2009, 35 pages. |
Browne, Niall, et al., “Shared Assessments Program AUP and SAS70 Frequently Asked Questions,” BITS, 4 pages. |
Buckshaw, Donald L., “Use of Decision Support Techniques for Information System Risk Management,” submitted for publication in Wiley's Encyclopedia of Quantitative Risk Assessment in Jan. 2007, 11 pages. |
Buehler, Kevin S., et al., “Running with risk,” The McKinsey Quarterly, No. 4, 2003, pp. 40-49. |
Carstens, et al., “Modeling Company Risk and Importance in Supply Graphs,” European Semantic Web Conference 2017: The Semantic Web pp. 18-31. |
Chu, Matthew, et al., “Visualizing Attack Graphs, Reachability, and Trust Relationships with Navigator,” MIT Lincoln Library, VizSEC '10, Ontario, Canada, Sep. 14, 2010, 12 pages. |
Chuvakin, “SIEM: Moving beyond compliance”, RSA White Paper (2010) (16 pages). |
Computer Network Graph-Bees, http://bioteams.com/2007/04/30/visualizing_complex_networks.html, date accessed Sep. 28, 2016, 2 pages. |
Crowther, Kenneth G., et al., “Principles for Better Information Security through More Accurate, Transparent Risk Scoring,” Journal of Homeland Security and Emergency Management, vol. 7, Issue 1, Article 37, 2010, 20 pages. |
Davis, Lois M., et al., “The National Computer Security Survey (NCSS) Final Methodology,” Technical report prepared for the Bureau of Justice Statistics, Safety and Justice Program, RAND Infrastructure, Safety and Environment (ISE), 2008, 91 pages. |
Dillon-Merrill, PhD., Robin L, et al., “Logic Trees: Fault, Success, Attack, Event, Probability, and Decision Trees,” Wiley Handbook of Science and Technology for Homeland Security, 13 pages. |
Dun & Bradstreet, The DUNSRight Quality Process: Power Behind Quality Information, 24 pages. |
Edmonds, Robert, “ISC Passive DNS Architecture”, Internet Systems Consortium, Inc., Mar. 2012, 18 pages. |
Equifax Inc. Stock Report, Standard & Poor's, Jun. 6, 2009, 8 pages. |
Hachem, Sara; Toninelli, Alessandra; Pathak, Animesh; Issany, Valerie. Policy-Based Access Control in Mobile Social Ecosystems. 2011 IEEE International Symposium on Policies for Distributed Systems and Networks (POLICY). Http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=5976796. 8 pages. |
Hacking Exposed 6, S. McClure et al., copyright 2009, 37 pages. |
Ingols, Kyle, et al., “Modeling Modern Network Attacks and Countermeasures Using Attack Graphs,” MIT Lincoln Laboratory, 16 pages. |
Ingols, Kyle, et al., “Practical Attack Graph Generation for Network Defense,” MIT Lincoln Library, IEEE Computer Society, Proceedings of the 22nd Annual Computer Security Applications Conference (ACSAC'06), 2006, 10 pages. |
Ingols, Kyle, et al., “Practical Experiences Using SCAP to Aggregate CND Data,” MIT Lincoln Library, Presentation to NIST SCAP Conference, Sep. 24, 2008, 59 pages. |
Jean, “Cyber Security: How to use graphs to do an attack analysis,” accessed on the internet at https://linkurio.us/blog/cyber-security-use-graphs-attack-analysis/; Aug. 2014, 11 pages. |
Jin et al, “Identifying and tracking suspicious activities through IP gray space analysis”, MineNet, Jun. 12, 2007 (6 pages). |
Johnson, Eric, et al., “Information Risk and the Evolution of the Security Rating Industry,” Mar. 24, 2009, 27 pages. |
Joslyn, et al., “Massive Scale Cyber Traffic Analysis: A Driver for Graph Database Research,” Proceedings of the First International Workshop on Graph Data Management Experience and Systems (GRADES 2013), 6 pages. |
KC Claffy, “Internet measurement and data analysis: topology, workload, performance and routing statistics,” accessed on the Internet at http://www.caida.org/publications/papers/1999/Nae/Nae.html., NAE '99 workshop, 1999, 22 pages. |
Li et al., “Finding the Linchpins of the Dark Web: a Study on Topologically Dedicated Hosts on Malicious Web Infrastructures”, IEEE, 2013 (15 pages). |
Lippmann, Rich, et al., NetSPA: a Network Security Planning Architecture, MIT Lincoln Laboratory, 11 pages. |
Lippmann, Richard, et al., “Validating and Restoring Defense in Depth Using Attack Graphs,” MIT Lincoln Laboratory, 10 pages. |
Lippmann, RP., et al., “An Annotated Review of Papers on Attack Graphs,” Project Report IA-1, Lincoln Laboratory, Massachusetts Institute of Technology, Mar. 31, 2005, 39 pages. |
Lippmann, RP., et al., “Evaluating and Strengthening Enterprise Network Security Using Attack Graphs,” Project Report IA-2, MIT Lincoln Laboratory, Oct. 5, 2005, 96 pages. |
MaxMind, https://www.maxmind.com/en/about-maxmind, https://www.maxmind.com/en/geoip2-isp-database, date accessed Sep. 28, 20116, 3 pages. |
Method Documentation, CNSS Risk Assessment Tool Version 1.1, Mar. 31, 2009, 24 pages. |
Moradi, et al., “Quantitative Models for Supply Chain Management,” IGI Global, 2012, 29 pages. |
Netcraft, www.netcraft.com, date accessed Sep. 28, 2016, 2 pages. |
NetScanTools Pro, http://www.netscantools.com/nstpromain.html, date accessed Sep. 28, 2016, 2 pages. |
Network Security Assessment, C. McNab, copyright 2004, 13 pages. |
Noel, et al., “Big-Data Architecture for Cyber Attack Graphs, Representing Security Relationships in NoSQL Graph Databases,” The MITRE Corporation, 2014, 6 pages. |
Nye, John, “Avoiding Audit Overlap,” Moody's Risk Services, Presentation, Source Boston, Mar. 14,2008, 19 pages. |
Pending claims for U.S. Appl. No. 14/021,585, as of Apr. 29, 2016, 2 pages. |
Pending claims for U.S. Appl. No. 14/021,585, as of Nov. 18, 2015, 6 pages. |
U.S. Appl. No. 13/240,572 and pending claims as of Mar. 22, 2016, 10 pages. |
U.S. Appl. No. 13/240,572 as of Oct. 7, 2015, application as filed and pending claims, 45 pages. |
U.S. Appl. No. 14/021,585 and pending claims as of Mar. 22, 2016, 2 pages. |
U.S. Appl. No. 14/021,585 as of Oct. 7, 2015 and application as filed, 70 pages. |
U.S. Appl. No. 14/944,484 and pending claims as of Mar. 22, 2016, 4 pages. |
U.S. Appl. No. 61/386,156 as of Oct. 7, 2015. 2 pages. |
Application as filed and pending claims for U.S. Appl. No. 13/240,572 as of Apr. 29, 2016, 46 pages. |
Application as filed and pending claims for U.S. Appl. No. 14/944,484 as of Apr. 29, 2016, 4 pages. |
Paxson, Vern, “How the Pursuit of Truth Led Me to Selling Viagra,” EECS Department, University of California, International Computer Science Institute, Lawrence Berkeley National Laboratory, Aug. 13, 2009, 68 pages. |
Proposal and Award Policies and Procedures Guide, Part I—Proposal Preparation & Submission Guidelines GPG, The National Science Foundation, Feb. 2009, 68 pages. |
Provos et al., “The Ghost in the Browser Analysis of Web-based Malware”, 2007 (9 pages). |
Rare Events, Oct. 2009, Jason, The MITRE Corporation, Oct. 2009, 104 pages. |
Report to the Congress on Credit Scoring and Its Effects on the Availability and Affordability of Credit, Board of Governors of the Federal Reserve System, Aug. 2007, 304 pages. |
RFC 1834, https://tools.ietf.org/html/rfc1834, date accessed Sep. 28, 2016, 7 pages. |
RFC 781, https://tools.ietf.org/html/rfc781, date accessed Sep. 28, 2016, 3 pages. |
RFC 950, https://tools.ietf.org/html/rfc950, date accessed Sep. 28, 2016, 19 pages. |
RFC 954, https://tools.ietf.org/html/rfc954, date accessed Sep. 28, 2016, 5 pages. |
SamSpade Network Inquiry Utility, https://www.sans.org/reading-room/whitepapers/tools/sam-spade-934, date accessed Sep. 28, 2016, 19 pages. |
SBIR Phase I: Enterprise Cyber Security Scoring, CyberAnalytix, LLC, http://www.nsf.gov/awardsearch/showAward. do?AwardNumber=I013603, Apr. 28, 2010, 2 pages. |
Security Warrior, Cyrus Peikari, Anton, Chapter 8: Reconnaissance, 6 pages. |
Snort Intrusion Monitoring System, http://archive.oreilly.com/pub/h/1393, date accessed Sep. 28, 2016, 3 pages. |
Srivastava, Divesh; Velegrakis, Yannis. Using Queries to Associate Metadata with Data. IEEE 23rd International Conference on Data Engineering. Pub. Date: 2007. http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=4221823, 3 pages. |
Stone-Gross, Brett, et al., “FIRE: Finding Rogue Networks,” 10 pages. |
Taleb, Nassim N., et al., “The Six Mistakes Executives Make in Risk Management,” Harvard Business Review, Oct. 2009, 5 pages. |
The CIS Security Metrics vI.0.0, The Center for Internet Security, May 11, 2009, 90 pages. |
The Fair Credit Reporting Act (FCRA) of the Federal Trade Commission (FTC), Jul. 30, 2004, 86 pages. |
The Financial Institution Shared Assessments Program, Industry Positioning and Mapping Document, BITS, Oct. 2007, 44 pages. |
Wagner, et al., “Assessing the vulnerability of supply chains using graph theory,” Int. J. Production Economics 126 (2010) 121-129. |
Wikipedia, https://en.wikipedia.org/wiki/Crowdsourcing, date accessed Sep. 28, 2016, 25 pages. |
Williams, Leevar, et al., “An Interactive Attack Graph Cascade and Reachability Display,” MIT Lincoln Laboratory, 17 pages. |
Williams, Leevar, et al., “GARNET: A Graphical Attack Graph and Reachability Network Evaluation Tool,” MIT Lincoln Library, VizSEC 2009, pp. 44-59. |
Seneviratne et al., “SSIDs in the Wild: Extracting Semantic Information from WiFi SSIDs” HAL archives-ouvertes.fr, HAL Id: hal-01181254, Jul. 29, 2015, 5 pages. |
Search Query Report form IP.com (performed Apr. 27, 2020), 5 pages. |
Camelo et al., “CONDENSER: A Graph-Based Approach for Detecting Botnets,” AnubisNetworks R&D, Amadora, Portugal, 8 pages. |
Camelo, “Botnet Cluster Identification,” Sep. 2014, 2 pages. |
Azman, Mohamed et al. Wireless Daisy Chain and Tree Topology Networks for Smart Cities. 2019 IEEE International Conference on Electrical, Computer and Communication Technologies (ICECCT). https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber= 8869252 (Year: 2019). |
Basinya, Evgeny A.; Yushmanov, Anton A. Development of a Comprehensive Security System. 2019 Dynamics of Systems, Mechanisms and Machines (Dynamics). https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8944700 (Year: 2019). |
Luo, Hui; Henry, Paul. A Secure Public Wireless LAN Access Technique That Supports Walk-Up Users. GLOBECOM '03. IEEE Global Telecommunications Conference. https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber= 1258471 (Year: 2003). |
Seigneur et al., A Survey of Trust and Risk Metrics for a BYOD Mobile Worker World: Third International Conference on Social Eco-Informatics, 2013, 11 pages. |
“Agreed Upon Procedures,” Version 4.0, BITS, The Financial Institution Shared Assessments Program, Assessment Guide, Sep. 2008, 56 pages. |
“Plugging the Right Holes,” Lab Notes, MIT Lincoln Library, Posted Jul. 2008, retrieved Sep. 14, 2010 from http://www.II.miLedufpublicationsflabnotesfpluggingtherightho! . . . , 2 pages. |
Computer Network Graph-Univ. of Michigan, http://people.cst.cmich.edu/liao1q/research.shtml, date accessed Sep. 28, 2016, 5 pages. |
The Dun & Bradstreet Corp. Stock Report, Standard & Poor's, Jun. 6, 2009, 8 pages. |
Gephi (gephi.org), accessed on the internet at https://web.archive.org/web/20151216223216/https://gephi.org/; Dec. 16, 2015; 1 page. |
Mile 2 CPTE Maltego Demo, accessed on the internet at https://www.youtube.com/watch?v=o2oNKOUzPOU; Jul. 12, 2012; 1 page. |
“Palantir Cyber: Uncovering malicious behavior at petabyte scale,” accessed on the internet at https://www.youtube.com/watch?v= EhYezV06EE; Dec. 21, 2012; 1 page. |
Gundert, Levi, “Big Data in Security—Part III: Graph Analytics,” accessed on the Internet at https://blogs.cisco.com/security/big-data-in-security-part-iii-graph-analytics; Cisco Blog, Dec. 2013, 8 pages. |
“Maltego XL,” accessed on the Internet at https://www.paterva.com/web7/buy/maltegoclients/maltego-xl.php, 5 pages. |
Massimo Candela, “Real-time BGP Visualisation with BGPlay,” accessed on the Internet at https://labs.ripe.net/Members/massimo_candela/real-time-bgp-visualisationwith-bgplay), Sep. 30, 2015, 8 pages. |
“Rapid7 Nexpose Vulnerability Scanner,” accessed on the internet at https://www.rapid7.com/products/nexpose/downlad/, 3 pages. |
“Tenable Nessus Network Vulnerability Scanner,” accessed on the internet at https://www.tenable.com/products/nessus/nessus-professional; 13 paqes. |
McNab, “Network Security Assessment,” copyright 2004, 56 pages. |
Gilgur, et al., “Percentile-Based Approach to Forecasting Workload Growth” Proceedings of CMG'15 Performance and Capacity International Conference by the Computer Measurement Group. No. 2015 (Year:2015). |
Number | Date | Country | |
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20210004457 A1 | Jan 2021 | US |
Number | Date | Country | |
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Parent | 16688647 | Nov 2019 | US |
Child | 17000135 | US | |
Parent | 16170680 | Oct 2018 | US |
Child | 16688647 | US |