Patent Grant
11128649
The present disclosure relates generally to computer and data network security. More particularly, the present disclosure relates to, but is not limited to, electronic mail (“email”) security.
Email has become a relatively common means of communication among individuals. Among its advantages, email is relatively convenient, fast, and cost-effective compared to traditional mail.
Unfortunately, the features that make email popular also lead to its abuse. For example, some parties have resorted to mass emailings of advertisements or other unsolicited email over the Internet. These mass emails, which are also referred to as “spam emails” or simply “spam,” are sent to computer users regardless of whether they asked for them or not.
Besides spam, other malicious attacks relating to email are of serious concern in relation to email security. For example, email account compromised (EAC) related attacks may originate in a malware infection which, unknowing to the user, compromises the user's email account. The EAC related attack may then utilize the compromised email account to further perform or propagate the attack.
One embodiment disclosed relates to a system for detecting anomalous messaging, discovering compromised accounts, and generating responses to threatened attacks. The system utilizes API commands and log forwarding for interaction and communication between a messaging and account hunting platform, other hunting platforms, an action center, and a security operations center. Another embodiment relates to a method of, and system for, performing a complete root cause analysis. Another embodiment relates to a method of, and system for, anomaly discovery which may advantageously utilize reference data to correlate different anomalies for reporting as a single incident.
These and other features of the present invention will be readily apparent to persons of ordinary skill in the art upon reading the entirety of this disclosure, which includes the accompanying drawings and claims.
The use of the same reference label in different drawings indicates the same or like components.
Current email security solutions generally aim to prevent an external attack from getting through to its defenses. These defenses typically include scanning for malicious signatures. They may also include utilization of reputational data for IP (internet protocol) addresses and analysis of the behavior of the email sender. However, as disclosed herein, these defenses are not particularly effective on attacks that originate internally from an existing malware infection, such as, for example, EAC related attacks.
The presently-disclosed solution advantageously improves email security in the event that the attack originates from within the protected network. For example, the attack may come from a user account of the protected network, where the user account is compromised due to a prior malware infection. The presently-disclosed solution has capability to detect and respond to abnormal behavior or unusual events pertaining to email messaging and account activities when such a legitimate user account has been so compromised. Furthermore, the presently-disclosed solution not only improves email security in the event that the attack originates from within the protected network (attacks that originate internally from EAC), but it also improves email security in the event that sophisticated phishing techniques are used in emails originating externally from attackers directly or EAC in other companies.
In an exemplary implementation, the presently-disclosed solution may use machine learning to perform anomaly detection using a baseline. The baseline may be established through continuous learning of normal and abnormal behaviors. The intelligence from machine learning provides a clearer view of the scope of impact of the attack and so enables a more effective response to the attack. Advantageously, the presently-disclosed solution improves the timeliness and effectiveness of protection against such attacks. Furthermore, the presently-disclosed solution provides a security operations center with insight into their organization's behavior.
Computer
Referring now to
The computer system 100 is a particular machine as programmed with one or more software modules, comprising instructions stored non-transitory in the main memory 108 for execution by the processor 101 to cause the computer system 100 to perform corresponding programmed steps. An article of manufacture may be embodied as computer-readable storage medium including instructions that when executed by the processor 101 cause the computer system 100 to be operable to perform the functions of the one or more software modules.
In the example of
Such a computer system 100 may be used to implement various devices and apparatus in the systems depicted in
System for Threat Hunting and Response
The messaging and account hunting platform 210 may receive email messages 202 and account activities data 204 via a data pipe 212. The data pipe 212 may pass the messages 202 and account activities 204 data to the triggering system 215. The data pipe 212 may also store metadata from the email messages 202 and account activities data 204 in metastore (data storage for metadata) 214. The messaging and account hunting platform 210 may further include a log forwarding module 220, a user interface (UI) 222 to control and monitoring purposes, and an application programming interface (API) 224 for communicating API commands.
The triggering system 215 may apply three different types of triggers: activity triggers 216-A, content triggers 216-C, and relationship triggers 216-R. Each trigger may comprise a machine learning (ML) model which is trained by a machine learning process. Exemplary implementations of activity, content and relationship triggers are described below in relation to Tables 1, 2 and 3, respectively.
The output of the triggering system 215 (i.e. the “activated” triggers due to satisfaction of triggering conditions) may be provided to the alert system 217. Each time a trigger is so activated, information about the activated trigger may be sent without delay to the alert system 217.
The alert system 217 may apply trigger-based rules that determine the issuance of contextual alerts. The contextual alerts may be targeted at different stages of an attack and/or at different types of attack. Exemplary contextual alerts which may be generated are described below in relation to Table 4.
The messaging security manager 250 may include an action center 252 that receives the alert output (i.e. information about the issued contextual alerts) from the alert system 217 of the messaging and account hunting platform 210 (for example, via the log forwarding shown in
In an exemplary implementation, the actions may be communicated or initiated by way of API commands. Exemplary API commands, including those for searching, mitigation and remediation tasks, are described below in relation to
Activity Triggers
The activity triggers 216-A generally utilize account activity related data sources, such as message logs and active directory logs, as data input sources. In an exemplary implementation, a local normal behavioral statistical model may be trained and utilized for each private network being protected. In addition, a global normal behavioral statistical model may be trained and utilized across multiple private networks being protected. The activity triggers 216-A may be utilized to detect attacks which are relatively straightforward.
Exemplary activity triggers 216-A are shown in Table 1, for example. The activity triggers have arbitrary labels beginning with “A”. Other activity triggers 216-A in addition to those in Table 1 may also be implemented.
The activity trigger A1 uses the “from” header and the received timestamp of the email message. Using this information, the activity trigger A1 is triggered when the “From” domain is new in past N days (where N is a number which may be set). This determination is made under a global consensus in that information from various private networks may be used to make the determination.
The activity trigger A2 uses an embedded URL within the email message and the received timestamp of the email message. The embedded URL may be normalized before being used. Using this information, the activity trigger A2 is triggered when the embedded URL (or the domain of the embedded URL) in is new or low prevalence or found in a web reputation service database. This determination is made under a global consensus in that information from various private networks may be used to make the determination.
The activity trigger A3 uses basic active directory (AD) information, one or more AD event codes of using a hacking tool, and the machine name. Using this information, the activity trigger A3 is triggered when an account has activity traces indicating the usage of hacking tools.
The activity trigger A4 uses the email subject, email body (structural metadata), an embedded URL, the received timestamp, and the recipient domain. The embedded URL may be normalized before being used. Using this information, the activity trigger A4 is triggered when in the last N minutes (where N is a number which may be set), similar emails have been received by multiple accounts in the private network.
The activity trigger A5 uses the sender account and the sent timestamp. Using this information, the activity trigger A5 is triggered when an account has sent a lot of email messages in the last N minutes (where N is a number which may be set).
The activity trigger A6 uses the logon timestamp and the logon IP address and its geolocation. Using this information, the activity trigger A6 is triggered when an account has logon activities coming from multiple geolocations is a short period of time.
The activity trigger A7 also uses the logon timestamp and the logon IP address and its geolocation. Using this information, the activity trigger A7 is triggered when an account has logon activities coming from a new geolocation.
The activity trigger A8 uses the BCC field in the email header and metadata which identifies content forwarding. Using this information, the activity trigger A8 is triggered when an email forwards content using the BCC method.
Content Triggers
The content triggers 216-C generally utilize message content as a data input source. In an exemplary implementation, user-style patterns may be generated and utilized for select accounts (i.e. for accounts deemed important) for each private network being protected. The content triggers 216-C may be utilized to protect the attacks of business email compromise (BEC) and targeted phishing (spear-phishing) attacks. In addition, the content triggers 216-C may also be utilized as a part of the triggers whose combination can help identify email account compromised (EAC) attacks.
Exemplary content triggers 216-C are shown in Table 2, for example. The content triggers shown have arbitrary labels beginning with “C”. Other content triggers 216-C in addition to those in Table 2 may also be implemented. Although not always listed in Table 2, the relevant data for the content triggers 216-C generally includes some content of the message body and/or its attachments, if any.
The content trigger C1 uses predetermined potential phishing keywords (in addition to content in the message body). Using this information, the content trigger C1 is triggered when an embedded URL in the body of the message contains one or more of the phishing keywords.
The content trigger C2 uses predetermined intention keywords. Using this information, the content trigger C2 is triggered when an embedded URL in the body of the message contains one or more of the intention keywords.
The content trigger C3 uses a hash of the HTML tag structure of the email body, a hash of a targeted phishing template, and the domain of the “From” address of the message. Using this information, the content trigger C3 is triggered to indicate similarity with a phishing template when the HTML tag structure of the email body is similar to the known phishing template and the “From” address belongs to a well-known (famous) website (for example, that of a large financial, government or commercial institution).
The content trigger C4 uses the sender account, the email attachment and the sent timestamp. Using this information, the content trigger C4 is triggered when the sent emails in a predetermined period of time contains local host information (for example, hosts or a password file in Linux).
The content trigger C5 uses the sender account and metadata on the writing style of the email content from the sender account. Using this information, the content trigger C5 is triggered when an account sent an email that does not match the account's writing style.
The content trigger C6 uses the sender account and metadata on the topics and/or intentions of the email content from the sender account. Using this information, the content trigger C6 is triggered when an account sent an email with new topics and/or intentions.
The content trigger C7 uses the sender account and the sent timestamp. Using this information, the content trigger C6 is triggered when an account sent emails at a time at which the account never sent emails before.
The content trigger C8 uses the sender account, the email subject, metadata on the writing style of the email content from the sender account, and writing style data of BEC and/or phishing emails. Using this information, the content trigger C8 is triggered when an account sent an email with a writing style similar to that of the BEC or phishing emails.
Relationship Triggers
The relationship triggers 216-R generally utilize the sender and recipient accounts of a message as data input sources. In an exemplary implementation, unique relationship triggers may be generated for each private network being protected. The relationship triggers 216-R may be utilized to detect abnormal connected behaviors when new relationships are built.
Exemplary relationship triggers 216-R are shown in Table 3, for example. The relationship triggers shown have identifying labels beginning with “R”. Other relationship triggers 216-R may also be implemented.
The relationship trigger R1 uses the sender account, the recipient account, the file type of an attachment to the message, and the sent and received timestamps. Using this information, the relationship trigger R1 is triggered when the attachment file type is new to the recipient (or new to the private network of the recipient) in the past N days, where N is a predetermined number of days.
The relationship trigger R2 uses the sender account, the recipient account, a flag indicating whether the message travels external to the private network, and the sent and received timestamps. Using this information, the relationship trigger R2 is triggered when the distance to an external recipient from an internal sender exceeds a predetermined threshold (i.e. when the external recipient is too far away from the sender account).
Similarly, the relationship trigger R3 uses the sender account, the recipient account, a flag indicating whether the message travels external to the private network, and the sent and received timestamps. Using this information, the relationship trigger R3 is triggered when the distance to an internal recipient from an external sender exceeds a predetermined threshold (i.e. when the internal recipient is too far away from the sender account).
Contextual Alerts
The alert system 217 generates contextual alerts (“smart alerts” in
Exemplary contextual alerts are shown in Table 4, for example. The contextual alerts shown have identifying labels beginning with “CA”. Other contextual alerts may also be implemented.
The contextual alert CA1 relates to the “cast the net” stage of an attack. The combination of triggers which activates CA1 includes activity triggers A1, A2 and A4 and either content trigger C1 or C2. The contextual alert CA1 indicates that, in a short period of time, many accounts in the same private network received similar emails which contain new “From” domains and the URLs embedded in the email body are new/low-prevalence and contain typical phishing/intention keywords.
The contextual alert CA2 also relates to the “cast the net” stage of an attack. The combination of triggers which activates CA2 includes R1, and either R2 or R3, and either A1 or A2. The contextual alert CA2 indicates that an account received an email with either an attachment whose filetype has not been seen before, and the sender has a far relationship to the recipient, and either the “From” domain is new or an embedded URL or domain in the email body is new or low prevalence or identified as malicious by a web reputation service.
The contextual alert CA3 relates to the information stealing stage of an attack. The combination of triggers which activates CA3 includes either A5 or A8, and any one (or more) of A6, A7, C4, R2, and R3. The contextual alert CA3 indicates that an account sent out a large number of emails or sent out emails using the BCC method. In addition, the account logs in from multiple geolocations in a short period of time, or logs in from a new geolocation, or sends out emails with local host information, or sends out emails to a recipient that has a very distant relationship to the account.
The contextual alert CA4 relates to the detection of a potential email account compromised (EAC) type of attack. The combination of triggers which activates CA4 includes any two (or more) of C5, C6, C7 and C8.
The contextual alert CA5 also relates to the detection of a potential EAC type of attack. The combination of triggers which activates CA5 includes A7 and any one (or more) of C5, C6, C7 and C8.
The contextual alert CA6 relates to the detection of a potential active directory (AD) type of attack. The A3 trigger activates CA6.
The contextual alert CA7 relates to multiple attack stages. The combination of triggers which activates CA7 includes any activity (A) trigger and any content (C) trigger and any relationship (R) trigger.
API Commands
As shown, the sweeping/hunting tasks 310 may include: RCA (root cause analysis) scan 400; search by electronic mail metadata 500; search by account 600; and search by triggers 700. The mitigation tasks 320 may include: open quarantine; purge relevant emails; and account access restriction. The remediation tasks 330 may include: pattern update; policy update; and machine learning (ML) model update. Finally, the management tasks 340 may include: exception list management; important account management; and critical asset management.
The outputs of the RCA scan may comprise, for example, an RCA report and an impact scope. The RCA report may include, for example, emails which match the search inputs, timestamps of those emails, accounts which sent or received the emails, and the relationship between the emails and accounts in the report. The impact scope may include other accounts which received or sent emails similar to those in the RCA report (as determined using relationship triggers) and AD activities and propagation scope (as determined using activity triggers). The impact scope may also include suspicious objects.
The outputs of the search by email metadata may comprise, for example, matched emails and their metadata, trigger information, data relating to any suspicious object in the matched emails, and contextual (smart or intelligent) alerts. The relevant emails and their metadata may include, for example, the raw email messages, their subjects, their received and sent timestamps, and the senders and recipients. The trigger information may include, for example, the triggered triggers of each email and account. The suspicious object data may include, for example, the name and secure hash of an attachment, an embedded URL, or a name of an account which sent or received the matched emails. The contextual (smart or intelligent) alerts may include those alerts which involve the matched emails or related accounts.
The outputs of the search by account may comprise, for example, relevant emails (and their metadata) which were sent or received by the matched account(s), trigger information, data relating to any suspicious object in the emails, contextual (smart or intelligent) alerts, and information (or a hint) as to whether or not the account is already compromised. The trigger information may include, for example, the triggered triggers of the matched accounts and each of the relevant emails. The suspicious object data may include, for example, the name and secure hash of an attachment or an embedded URL from the relevant emails. The contextual (smart or intelligent) alerts may include those alerts which involve the matched account(s) or relevant emails.
The outputs of the search by trigger may comprise, for example, emails and/or accounts which match the triggers, trigger information, data relating to any suspicious object in the emails, and contextual (smart or intelligent) alerts. The trigger information may include, for example, the triggered triggers of each relevant account and email. The suspicious object data may include, for example, the name and secure hash of an attachment or an embedded URL from the emails, and an account name. The contextual (smart or intelligent) alerts may include those alerts which involve the relevant account(s) or email(s).
As depicted, an RCA scan performed by the Messaging and Account Hunting Platform 210 may generate 81A a root cause account (for example, Account #A) and may also generate 81B an impact scope, which may include further accounts (for example, Account #B and Account #C). The root cause account information may be sent 82 automatically (by email, for example) to the SOC team 280 and also to a backend controller 815 of the backend system 810. The impact scope, along with information on suspicious objects, may be sent 83 automatically to other hunting platforms 230.
The other hunting platforms 230 may use the information (83) to further identify any undiscovered threats or adjust the malicious level of previously discovered suspicious threats. For example, the other hunting platforms 230 may search through stored data (such as logs and stored files, for example) of the accounts to detect threat-related files, threat-related URLs, and threat-related active directory (AD) accounts, for example.
The detected impact information 84 may be returned from the other hunting platforms 230 to the messaging and account hunting platform 210 and also to the backend controller 815. The messaging and account hunting platform 210 may use the detected impact information 84 as the search criteria for root cause analysis or impact scope determination. The backend controller 815 may analyze the detected information from the other hunting platforms 230, along with the root cause account information from the messaging and account hunting platform 210.
The analysis by the backend controller 815 may utilize a logic-based procedure and/or artificial intelligence to generate a “complete” (i.e. verified and elaborated upon) root cause analysis 85 which includes the account that distributed the attack and other accounts affected by the attack. The complete root cause analysis 85 may be sent from the backend controller 815 via an SOC tool 270 to the SOC team 280. The SOC team 280 may then use an SOC tool 270 to initiate mitigation and remediation tasks by sending out appropriate API commands.
As depicted, an anomaly discovery in the form of a contextual alert may be generated 91 by the Alert System 217 of the Messaging and Account Hunting Platform 210. The contextual alert may include information on a compromised source account and on further compromised accounts in an impact scope (for example, Compromised Account #A and Compromised Account #B). In addition to compromised accounts, the contextual alert may also include further information on emails found to be using sophisticated phishing techniques to conduct attacks. As depicted, notification of these compromised accounts and the further information may be provided by the Messaging and Account Hunting Platform 210 via an SOC tool 270 to the SOC team 280. In addition, the system 900 shown in
The other threat hunting platforms 230 may perform searches to generate 93 reference information which may be sent to the Messaging and Account Hunting Platform 210 and also to the backend controller 815. In some cases, the backend controller 815 may use the reference information to correlate different anomaly discoveries so as to identify a single incident causing the anomalies. In such a case, the backend controller 815 may send notification to report 94 the incident via an SOC tool 270 to the SOC team 280. The SOC team 280 may then use an SOC tool 270 to initiate mitigation and remediation tasks by sending out appropriate API commands.
In the present disclosure, numerous specific details are provided, such as examples of apparatus, components, and methods, to provide a thorough understanding of embodiments of the invention. Persons of ordinary skill in the art will recognize, however, that the invention can be practiced without one or more of the specific details. In other instances, well-known details are not shown or described to avoid obscuring aspects of the invention.
While specific embodiments of the present invention have been provided, it is to be understood that these embodiments are for illustration purposes and not limiting. Many additional embodiments will be apparent to persons of ordinary skill in the art reading this disclosure.
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