Increasingly, modern malware families (e.g., gozi, cidox, or upatre) rely on some form of domain generation algorithm (DGA) in order to complicate the detection and recovery procedures associated with malware, thereby prolonging malware infections on enterprise and consumer network computing systems. For example, malicious actors may utilize DGAs to generate previously unknown domain names that are difficult to proactively detect or sinkhole and which may consequently increase potential financial losses caused by downtime associated with malware infected systems.
Traditional DGA detection methods often utilize lexical and statistical models for detecting DGA generated domain names containing non-standard characters and/or nonsensical words or phrases. However, these traditional methods may fail to detect domain names generated by modern DGAs that are associated with malware but which are formed from English language wordlists and thus appear to be associated with legitimate computer processes.
As will be described in greater detail below, the instant disclosure describes various systems and methods for detecting malware infections associated with domain generation algorithms.
In one example, a computer-implemented method for detecting malware infections associated with domain generation algorithms (DGAs) may include (i) receiving one or more domain names in a cluster of failed domain name system (DNS) requests and telemetry data from a client device, (ii) generating a classification model based on a group of unrelated features associated with the DGAs, (iii) performing an analysis of the failed DNS requests and the telemetry data by applying the classification model to identify domain names associated with malicious activity including utilization of the DGAs, based on the unrelated features, (iv) identifying the domain names associated with the malicious activity based on the analysis, and (v) performing a security action, based on the domain names, that protects against infection by malware associated with the malicious activity.
In some examples, the classification model may be generated by (i) generating a statistical model including features associated with generic behavior patterns of DGAs (ii) generating a network model including features associated with a timing for failed DNS requests made by the DGAs, (iii) generating a lexical model including features associated with one or more n-grams, (iv) generating a local model including features associated with traffic generated from the client device, (v) generating a global model including features associated with entity-based patterns of domain generation algorithms, and (vi) generating a database query model (e.g., a WHOIS model) for querying features associated with known domain name data. In some embodiments, the features associated with the generic behavior patterns may include (i) a limited set of top level domains (TLDs) utilized by the DGAs, (ii) a distribution pattern of TLDs utilized in the DGAs, and/or (iii) a restricted number of domain levels utilized in a set of domains generated by the DGAs in a cluster.
In some examples, the features associated with the timing for the failed DNS requests may include (i) a continuous generation of non-repeating invalid domain names that ceases upon generating an existing domain name and/or (ii) a time gap between successive failed DNS requests that follows a detectable pattern. In some embodiments, the features associated with the traffic generated from the client device may include data identifying a parent process executing on the client device and/or other telemetry data on the client device. In other embodiments, the features associated with the entity-based patterns may include common patterns associated with DNS requests generated by large entities.
In some examples, the computer-implemented may further include (i) filtering potential false positives from an output of the classification model by whitelisting DNS request patterns determined to be non-malicious, (ii) adjusting the classification model based on the filtered output, and (iii) retraining the classification model based on at least one of feedback data and quality control activity. In some embodiments, the security action may include wherein the security module performs the security action by providing an alert to a malware threat protection service for protecting against malware threats on additional client devices in a network.
In some examples, the telemetry data may include (i) lexical data, (ii) statistical data, (iii) network data, (iv) local data, (v) global data, and (vi) domain name database (e.g., WHOIS) data. In some embodiment, the classification model may be a heuristic model or a machine-learning model.
In one embodiment, a system for detecting malware infections associated with domain generation algorithms (DGAs) may include at least one physical processor and physical memory that includes multiple modules and computer-executable instructions that, when executed by the physical processor, cause the physical processor to (i) receive, by a receiving module, one or more domain names in a cluster of failed domain name system (DNS) requests and telemetry data from a client device, (ii) generate, by a generating module, a classification model based on a group of unrelated features associated with the DGAs, (iii) perform, by an analysis module, an analysis of the failed DNS requests and the telemetry data by applying the classification model to identify domain names associated with malicious activity including utilization of the DGAs, based on the unrelated features, (iv) identify, by an identification module, the domain names associated with the malicious activity based on the analysis, and (v) perform, by a security module, a security action that protects against infection by malware associated with the malicious activity.
In some examples, the above-described method may be encoded as computer-readable instructions on a non-transitory computer-readable medium. For example, a computer-readable medium may include one or more computer-executable instructions that, when executed by at least one processor of a computing device, may cause the computing device to (i) receive one or more domain names in a cluster of failed domain name system (DNS) requests and telemetry data from a client device, (ii) generate a classification model based on a group of unrelated features associated with domain generation algorithms (DGAs), (iii) perform an analysis of the failed DNS requests and the telemetry data by applying the classification model to identify domain names associated with malicious activity including utilization of the DGAs, based on the unrelated features, (iv) identify the domain names associated with the malicious activity based on the analysis, and (v) perform a security action that protects against infection by malware associated with the malicious activity.
Features from any of the embodiments described herein may be used in combination with one another in accordance with the general principles described herein. These and other embodiments, features, and advantages will be more fully understood upon reading the following detailed description in conjunction with the accompanying drawings and claims.
The accompanying drawings illustrate a number of example embodiments and are a part of the specification. Together with the following description, these drawings demonstrate and explain various principles of the instant disclosure.
Throughout the drawings, identical reference characters and descriptions indicate similar, but not necessarily identical, elements. While the example embodiments described herein are susceptible to various modifications and alternative forms, specific embodiments have been shown by way of example in the drawings and will be described in detail herein. However, the example embodiments described herein are not intended to be limited to the particular forms disclosed. Rather, the instant disclosure covers all modifications, equivalents, and alternatives falling within the scope of the appended claims.
The present disclosure is generally directed to systems and methods for detecting malware infections associated with domain generation algorithms (DGAs). As will be described in greater detail below, by generating a heuristic or machine learning classification model based on multiple unrelated features associated with domain generation algorithms such as generic behavior patterns, n-grams, the timing of failed DNS requests, client data traffic, global entity-based patterns, and WHOIS data, the systems and methods described herein may enable the detection of malware infections associated with domain names generated by DGAs and that are associated with malicious activity, network. By utilizing the classification model in this way, the systems and methods described herein may enable the detection of malware infections associated with DGAs that would otherwise be undetected by traditional methods relying solely on lexical or linguistic analyses.
Moreover, the systems and methods described herein may improve computing device security by protecting computing devices from being infected by malware attacks associated with malicious activity after detecting the presence of DGAs. In some examples, the systems and methods may provide DGA generated domain names to a malware threat protection service for subsequent identification and/or removal from a computing device.
The following will provide, with reference to
For example, and as will be described in greater detail below, one or more of modules 102 may represent modules stored and configured to run on one or more computing devices, such as the devices illustrated in
As illustrated in
As illustrated in
As illustrated in
Example system 100 in
For example, receiving module 104 may receive, from client device 206, failed DNS requests 124 including one or more domain names 212 and telemetry data 125. Next, generating module 106 may generate classification model 126 including unrelated features 214 (an optionally using data from domain name databases 218) associated with DGA behavior. Then, analysis module 108 may apply classification model 126 to analyze failed DNS requests 124 and telemetry data 125, based on unrelated features 214. Next, identification module 110 may identify domain names associated with malicious activity 128 (e.g., from among domain names 212) based on the analysis. Finally, security module 112 may perform one or more security actions 216, based on the domain names, protecting against infection by malware associated with the malicious activity.
Computing device 202 generally represents any type or form of computing device capable of reading computer-executable instructions. In some examples, computing device 202 may be a security server configured to detect malware infections on endpoint devices in a network. Additional examples of computing device 202 include, without limitation, application servers, web servers, storage servers, and/or database servers configured to run certain software applications and/or provide various security, web, storage, and/or database services. Although illustrated as a single entity in
Client device 206 generally represents any type or form of computing device capable of reading computer-executable instructions. In some embodiments, client device 206 may represent an endpoint device capable of initiating multiple DNS requests for domain names and communicating telemetry data over network 204. Additional examples of client device 206 include, without limitation, laptops, tablets, desktops, servers, cellular phones, Personal Digital Assistants (PDAs), multimedia players, embedded systems, wearable devices (e.g., smart watches, smart glasses, etc.), smart vehicles, smart packaging (e.g., active or intelligent packaging), gaming consoles, so-called Internet-of-Things devices (e.g., smart appliances, etc.), variations or combinations of one or more of the same, and/or any other suitable computing device.
Network 204 generally represents any medium or architecture capable of facilitating communication or data transfer. In one example, network 204 may facilitate communication between computing device 202 and client device 206. In this example, network 204 may facilitate communication or data transfer using wireless and/or wired connections. Examples of network 204 include, without limitation, an intranet, a Wide Area Network (WAN), a Local Area Network (LAN), a Personal Area Network (PAN), the Internet, Power Line Communications (PLC), a cellular network (e.g., a Global System for Mobile Communications (GSM) network), portions of one or more of the same, variations or combinations of one or more of the same, and/or any other suitable network.
As illustrated in
Receiving module 104 may receive failed DNS requests 124 and telemetry data 125 in a variety of ways. In some examples, receiving module 104 may request data describing a cluster (e.g., a cluster 122 shown in
At step 304, one or more of the systems described herein may generate a classification model based on a group of unrelated features associated with DGAs. For example, generating module 106 may, as part of computing device 202 in
The term “classification model,” as used herein, generally refers to a heuristic or machine learning based classifier that operates with features constructed based on clusters of failed DNS requests and telemetry data received from single entities (e.g., endpoint devices) as inputs and that are utilized for detecting malware infections associated with DGAs. In some examples, the classification model may be updated by tweaking the heuristic classifier and/or training (or retraining) the machine learning based classifier. As described herein, the classification model may be configured to identify domain names generated by DGAs that may be associated with malicious activity, based on evidence that a majority of DGAs utilized by malware authors typically generate a certain amount of non-repeating invalid domain names within a relatively short period of time until they reach ones that actually exist and belong to the malware authors.
The term “unrelated features,” as used herein, generally refers to any number or group of features associated with the generation of domain names by DGAs. For example, a group of unrelated features may include statistical data, lexical data, network timing data (e.g., timing data associated with failed DNS requests), data associated with a parent process generating network traffic and/or other telemetry data on a client device, global data describing large entity pattern-based behavior associated with domain name generation, and/or WHOIS data such as time-to-live (TTL) data, a number of associated IP addresses, domain name registrant information, and derived metadata such as a number of associated domains, their average TTL, etc.
The term “domain names associated with malicious activity,” as used herein, generally refers to domain names generated by DGAs according to known behavior utilized by malware authors. In some examples, malware may be configured to utilize DGAs that periodically generate large numbers of domain names on infected computing devices that may be used to communicate with malware command and control servers. The infected computing devices may then receive malware updates or commands utilizing the domain names.
Generating module 106 may generate classification model 126 in a variety of ways. In some examples, generating module 106 may be configured to generate a number of components, based on unrelated features 214, that are utilized by classification model 126. An example classification model 126 generated by generating module 106 is described in greater detail below with respect to
Turning now to
In some examples, network model 425 may be based on network side information represented in the form of timing for failed DNS requests. Within classification model 126, network model 425 may be configured to identify certain properties or features that all DGAs generally need to implement to effectively achieve their goals. These properties or features may include, without limitation, non-repeating invalid (e.g., non-existing) domain names 430 and time between successive failed DNS requests 435. For example, non-repeating invalid domain names 430 may represent the large number of non-repeating domain names typically generated by DGAs until a successful one is identified, after which the generation of domain names halts. Time between successive failed DNS requests 435 may represent a time gap between DNS requests that follows a detectable pattern associated with DGAs as all non-existing domains are being processed and resolved at approximately equal periods of time.
In some examples, lexical model 440, which may include n-gram classifier 445, may be configured to check whether a domain name string matches letter distributions common for a particular language (e.g., English). In some examples, local model 450 may be based on local information received from entities (e.g., endpoint devices such as client device 206 in
In some examples, global model 460 may be based on global entity-oriented features associated with DNS request patterns common only for large entities such as organizations, industries or even countries but which may be uncommon for DNS request patterns associated with endpoint devices. Within classification model 126, global model 460 may be configured to identify information or features associated with large entity DNS request patterns 465.
Returning to
Analysis module 108 may apply classification model 126 in a variety of ways. In some examples, analysis module 108 may apply a combination of the features in statistical model 405, network model 425, lexical model 440, local model 450, global model 460, and database query (e.g., WHOIS) model 470 to identify domain names associated with malicious activity 128.
At step 308, one or more of the systems described herein may identify domain names associated with malicious activity 128 based on the analysis performed at step 306. For example, identification module 110 may, as part of computing device 202 in
Identification module 110 may identify domain names associated with malicious activity 128 in a variety of ways. For example, from an output of classification model 126, identification module 110 may identify domain names associated with malicious activity 128 based on failed DNS requests 124 and/or telemetry data 125 conforming to behavior patterns associated with DGAs. In some embodiments, the behavior patterns may include data corresponding to a limited set of TLDs, a distribution of TLDs differing from corresponding values generally used over the Internet, consistent (e.g., 2 or 3) number of domain levels, the generation of a large number of non-repeating invalid/non-existing domain names, time gaps between requests following a detectable pattern, a parent process associated with generating the requests, other telemetry data (e.g., previous domains accessed by a client device and associated telemetry), DNS request patterns only associated with large entities, and WHOIS data.
At step 310, one or more of the systems described herein may perform a security action, based on the domain names associated with the malicious activity, that protects against infection by malware associated with malicious activity including the utilization of DGAs. For example, security module 112 may, as part of computing device 202 in
Security module 112 may be configured to perform a number of security actions 216 to protect against infection by malware. In some examples, security module 112 may provide an alert to a malware threat protection service for protecting against malware threats on client devices in a network. For example, security module 112 may generate an alert identifying domain names associated with malicious activity 128 as an indicator that client device 206 is compromised by malware responsible for their generation (e.g., the malware utilizes one or more DGAs that generated domain names associated with malicious activity 128).
As illustrated in
The term “false positive,” as used herein, generally refers to failed DNS requests that may exhibit behavior associated with the generation of domain names by DGAs, but which are in fact not. For example, multiple failed DNS requests associated with electronic mail mass mailings including one or more e-mail addresses associated with invalid domain names or multiple failed DNS requests made from a website (e.g., an advertising website) to multiple domains (one or more of which may be invalid) may appear to be DGAs generating large numbers of domain names but are in fact not associated with malicious activity involving the use of DGAs. For example, a non-malicious invalid domain name may be associated with an outdated e-mail address or a previously valid website address that is currently inactive (e.g., the website is down due to an outage or other problem).
Filtering module 114 may filter false positives in a variety of ways. In some examples, filtering module 114 may whitelist non-malicious DNS request patterns and/or associated telemetry generating invalid domain names.
At step 504, one or more of the systems described herein may adjust the classification model based on the filtered output. For example, filtering module 114 may, as part of computing device 202 in
Filtering module 114 may adjust the output of classification model 126 in a variety of ways. In some examples, filtering module 114 may utilize the non-malicious DNS request patterns and/or associated telemetry to train a heuristic or machine learning model representing classification model 126 to ignore failed DNS request patterns corresponding to non-malicious DNS request patterns generating invalid domain names.
At step 506, one or more of the systems described herein may retrain the classification model based on feedback/quality control activity. For example, filtering module 114 may, as part of computing device 202 in
As explained in connection with method 300 above, the systems and methods described provide for detecting malware infections associated with DGAs. By applying a heuristic or machine learning based classifier model to a combination of unrelated types of telemetry and metadata, the model may be utilized to identify domain names associated with malicious activity with a high degree of precision as compared with conventional detection methods. The model may operate with features constructed based on clusters of failed DNS requests received from single entities (e.g., end user machines) as an input. The model may perform the clustering of DNS requests by grouping them according to timing and traffic origin. The model may rely on the fact that a majority of domain generation algorithms are designed to generate a certain amount of non-repeating invalid domain names within a relatively short period of time before reaching valid existing domain names belonging to malware authors. The base of the model may include a lexical model part based on statistical analysis, machine learning, or an n-gram classifier that functions to generate features representing differences between domain names generated by DGAs and generic entries found in-the-wild. The model may utilize statistical data powered by generic behavior patterns that malicious actors tend to follow when developing DGAs. The model may further utilize network side information represented in the form of timing properties for failed DNS requests known to be implemented by DGAs. The model may also utilize local side information from entities generating traffic under suspicion. The model may also use may utilize global entity-oriented features responsible for finding patterns common only for particular larger entries such as organizations, industries, or even countries. Finally, the model may use WHOIS and/or historical information including time-to-live (TTL) data, a number of associated IP addresses, domain name registrant information, and derived metadata such as a number of associated domains, their average TTL, etc. Additionally, the model may utilize various methods for filtering out false positives to further improve the detection results.
Computing system 610 broadly represents any single or multi-processor computing device or system capable of executing computer-readable instructions. Examples of computing system 610 include, without limitation, workstations, laptops, client-side terminals, servers, distributed computing systems, handheld devices, or any other computing system or device. In its most basic configuration, computing system 610 may include at least one processor 614 and a system memory 616.
Processor 614 generally represents any type or form of physical processing unit (e.g., a hardware-implemented central processing unit) capable of processing data or interpreting and executing instructions. In certain embodiments, processor 614 may receive instructions from a software application or module. These instructions may cause processor 614 to perform the functions of one or more of the example embodiments described and/or illustrated herein.
System memory 616 generally represents any type or form of volatile or non-volatile storage device or medium capable of storing data and/or other computer-readable instructions. Examples of system memory 616 include, without limitation, Random Access Memory (RAM), Read Only Memory (ROM), flash memory, or any other suitable memory device. Although not required, in certain embodiments computing system 610 may include both a volatile memory unit (such as, for example, system memory 616) and a non-volatile storage device (such as, for example, primary storage device 632, as described in detail below). In one example, one or more of modules 102 from
In some examples, system memory 616 may store and/or load an operating system 640 for execution by processor 614. In one example, operating system 640 may include and/or represent software that manages computer hardware and software resources and/or provides common services to computer programs and/or applications on computing system 610. Examples of operating system 640 include, without limitation, LINUX, JUNOS, MICROSOFT WINDOWS, WINDOWS MOBILE, MAC OS, APPLE'S 10S, UNIX, GOOGLE CHROME OS, GOOGLE'S ANDROID, SOLARIS, variations of one or more of the same, and/or any other suitable operating system.
In certain embodiments, example computing system 610 may also include one or more components or elements in addition to processor 614 and system memory 616. For example, as illustrated in
Memory controller 618 generally represents any type or form of device capable of handling memory or data or controlling communication between one or more components of computing system 610. For example, in certain embodiments memory controller 618 may control communication between processor 614, system memory 616, and I/O controller 620 via communication infrastructure 612.
I/O controller 620 generally represents any type or form of module capable of coordinating and/or controlling the input and output functions of a computing device. For example, in certain embodiments I/O controller 620 may control or facilitate transfer of data between one or more elements of computing system 610, such as processor 614, system memory 616, communication interface 622, display adapter 626, input interface 630, and storage interface 634.
As illustrated in
As illustrated in
Additionally or alternatively, example computing system 610 may include additional I/O devices. For example, example computing system 610 may include I/O device 636. In this example, I/O device 636 may include and/or represent a user interface that facilitates human interaction with computing system 610. Examples of I/O device 636 include, without limitation, a computer mouse, a keyboard, a monitor, a printer, a modem, a camera, a scanner, a microphone, a touchscreen device, variations or combinations of one or more of the same, and/or any other I/O device.
Communication interface 622 broadly represents any type or form of communication device or adapter capable of facilitating communication between example computing system 610 and one or more additional devices. For example, in certain embodiments communication interface 622 may facilitate communication between computing system 610 and a private or public network including additional computing systems. Examples of communication interface 622 include, without limitation, a wired network interface (such as a network interface card), a wireless network interface (such as a wireless network interface card), a modem, and any other suitable interface. In at least one embodiment, communication interface 622 may provide a direct connection to a remote server via a direct link to a network, such as the Internet. Communication interface 622 may also indirectly provide such a connection through, for example, a local area network (such as an Ethernet network), a personal area network, a telephone or cable network, a cellular telephone connection, a satellite data connection, or any other suitable connection.
In certain embodiments, communication interface 622 may also represent a host adapter configured to facilitate communication between computing system 610 and one or more additional network or storage devices via an external bus or communications channel. Examples of host adapters include, without limitation, Small Computer System Interface (SCSI) host adapters, Universal Serial Bus (USB) host adapters, Institute of Electrical and Electronics Engineers (IEEE) 1394 host adapters, Advanced Technology Attachment (ATA), Parallel ATA (PATA), Serial ATA (SATA), and External SATA (eSATA) host adapters, Fibre Channel interface adapters, Ethernet adapters, or the like. Communication interface 622 may also allow computing system 610 to engage in distributed or remote computing. For example, communication interface 622 may receive instructions from a remote device or send instructions to a remote device for execution.
In some examples, system memory 616 may store and/or load a network communication program 638 for execution by processor 614. In one example, network communication program 638 may include and/or represent software that enables computing system 610 to establish a network connection 642 with another computing system (not illustrated in
Although not illustrated in this way in
As illustrated in
In certain embodiments, storage devices 632 and 633 may be configured to read from and/or write to a removable storage unit configured to store computer software, data, or other computer-readable information. Examples of suitable removable storage units include, without limitation, a floppy disk, a magnetic tape, an optical disk, a flash memory device, or the like. Storage devices 632 and 633 may also include other similar structures or devices for allowing computer software, data, or other computer-readable instructions to be loaded into computing system 610. For example, storage devices 632 and 633 may be configured to read and write software, data, or other computer-readable information. Storage devices 632 and 633 may also be a part of computing system 610 or may be a separate device accessed through other interface systems.
Many other devices or subsystems may be connected to computing system 610. Conversely, all of the components and devices illustrated in
The computer-readable medium containing the computer program may be loaded into computing system 610. All or a portion of the computer program stored on the computer-readable medium may then be stored in system memory 616 and/or various portions of storage devices 632 and 633. When executed by processor 614, a computer program loaded into computing system 610 may cause processor 614 to perform and/or be a means for performing the functions of one or more of the example embodiments described and/or illustrated herein. Additionally or alternatively, one or more of the example embodiments described and/or illustrated herein may be implemented in firmware and/or hardware. For example, computing system 610 may be configured as an Application Specific Integrated Circuit (ASIC) adapted to implement one or more of the example embodiments disclosed herein.
Client systems 710, 720, and 730 generally represent any type or form of computing device or system, such as example computing system 610 in
As illustrated in
Servers 740 and 745 may also be connected to a Storage Area Network (SAN) fabric 780. SAN fabric 780 generally represents any type or form of computer network or architecture capable of facilitating communication between a plurality of storage devices. SAN fabric 780 may facilitate communication between servers 740 and 745 and a plurality of storage devices 790(1)-(N) and/or an intelligent storage array 795. SAN fabric 780 may also facilitate, via network 750 and servers 740 and 745, communication between client systems 710, 720, and 730 and storage devices 790(1)-(N) and/or intelligent storage array 795 in such a manner that devices 790(1)-(N) and array 795 appear as locally attached devices to client systems 710, 720, and 730. As with storage devices 760(1)-(N) and storage devices 770(1)-(N), storage devices 790(1)-(N) and intelligent storage array 795 generally represent any type or form of storage device or medium capable of storing data and/or other computer-readable instructions.
In certain embodiments, and with reference to example computing system 610 of
In at least one embodiment, all or a portion of one or more of the example embodiments disclosed herein may be encoded as a computer program and loaded onto and executed by server 740, server 745, storage devices 760(1)-(N), storage devices 770(1)-(N), storage devices 790(1)-(N), intelligent storage array 795, or any combination thereof. All or a portion of one or more of the example embodiments disclosed herein may also be encoded as a computer program, stored in server 740, run by server 745, and distributed to client systems 710, 720, and 730 over network 750.
As detailed above, computing system 610 and/or one or more components of network architecture 700 may perform and/or be a means for performing, either alone or in combination with other elements, one or more steps of an example method for detecting malware infections associated with domain generation algorithms.
While the foregoing disclosure sets forth various embodiments using specific block diagrams, flowcharts, and examples, each block diagram component, flowchart step, operation, and/or component described and/or illustrated herein may be implemented, individually and/or collectively, using a wide range of hardware, software, or firmware (or any combination thereof) configurations. In addition, any disclosure of components contained within other components should be considered example in nature since many other architectures can be implemented to achieve the same functionality.
In some examples, all or a portion of example system 100 in
In various embodiments, all or a portion of example system 100 in
According to various embodiments, all or a portion of example system 100 in
In some examples, all or a portion of example system 100 in
In addition, all or a portion of example system 100 in
In some embodiments, all or a portion of example system 100 in
According to some examples, all or a portion of example system 100 in
The process parameters and sequence of steps described and/or illustrated herein are given by way of example only and can be varied as desired. For example, while the steps illustrated and/or described herein may be shown or discussed in a particular order, these steps do not necessarily need to be performed in the order illustrated or discussed. The various example methods described and/or illustrated herein may also omit one or more of the steps described or illustrated herein or include additional steps in addition to those disclosed.
While various embodiments have been described and/or illustrated herein in the context of fully functional computing systems, one or more of these example embodiments may be distributed as a program product in a variety of forms, regardless of the particular type of computer-readable media used to actually carry out the distribution. The embodiments disclosed herein may also be implemented using software modules that perform certain tasks. These software modules may include script, batch, or other executable files that may be stored on a computer-readable storage medium or in a computing system. In some embodiments, these software modules may configure a computing system to perform one or more of the example embodiments disclosed herein.
In addition, one or more of the modules described herein may transform data, physical devices, and/or representations of physical devices from one form to another. Additionally or alternatively, one or more of the modules recited herein may transform a processor, volatile memory, non-volatile memory, and/or any other portion of a physical computing device from one form to another by executing on the computing device, storing data on the computing device, and/or otherwise interacting with the computing device.
The preceding description has been provided to enable others skilled in the art to best utilize various aspects of the example embodiments disclosed herein. This example description is not intended to be exhaustive or to be limited to any precise form disclosed. Many modifications and variations are possible without departing from the spirit and scope of the instant disclosure. The embodiments disclosed herein should be considered in all respects illustrative and not restrictive. Reference should be made to the appended claims and their equivalents in determining the scope of the instant disclosure.
Unless otherwise noted, the terms “connected to” and “coupled to” (and their derivatives), as used in the specification and claims, are to be construed as permitting both direct and indirect (i.e., via other elements or components) connection. In addition, the terms “a” or “an,” as used in the specification and claims, are to be construed as meaning “at least one of.” Finally, for ease of use, the terms “including” and “having” (and their derivatives), as used in the specification and claims, are interchangeable with and have the same meaning as the word “comprising.”