Threat detection platforms for detecting, characterizing, and remediating email-based threats in real time
Conventional email filtering services are not suitable for recognizing sophisticated malicious emails, and therefore may allow sophisticated malicious emails to reach inboxes by mistake. Introduced here are threat detection platforms designed to take an integrative approach to detecting security threats. For example, after receiving input indicative of an approval from an individual to access past email received by employees of an enterprise, a threat detection platform can download past emails to build a machine learning (ML) model that understands the norms of communication with internal contacts (e.g., other employees) and/or external contacts (e.g., vendors). By applying the ML model to incoming email, the threat detection platform can identify security threats in real time in a targeted manner.
1. A method comprising:
establishing, via an application programming interface, a connection with a storage medium that includes a series of past communications received by an employee of an enterprise;
downloading, via the application programming interface, a first portion of the series of past communications corresponding to a first interval of time into a local processing environment;
building a machine learning (ML) model for at least one of the employee or the enterprise by providing the first portion of the series of past communications to the ML model as training data;
receiving, via the application programming interface, a communication addressed to the employee; and
establishing whether the communication represents a security risk by applying the ML model to the communication, wherein an output produced by the ML model upon being applied to the communication indicates a score associated with a deviation of an assigned behavioral trait determined from the communication, wherein the assigned behavioral trait includes at least one of: (1) a frequency with which the employee uses a sender identity, (2) a frequency with which the employee uses a sender email address, or (3) a combination of the frequencies with which the employee uses the sender identity and the sender email address.
2. The method of claim 1 , further comprising:
receiving input indicative of an approval from an administrator associated with the enterprise to access the series of past communications;
wherein said establishing is performed in response to receiving the input.
3. The method of claim 1 , wherein the series of past communications includes multiple emails that were delivered to the employee.
4. The method of claim 1 , further comprising:
examining each past communication in the first portion of the series of past communications to establish a set of attributes; and
providing the set of attributes derived from the first portion of the series of past communications to the ML model as training data.
5. The method of claim 1 , further comprising:
determining, by the ML model, that the communication represents a security risk; and
characterizing the security risk along multiple dimensions.
6. The method of claim 5 , wherein the multiple dimensions include:
an attacked party,
an attack vector,
an impersonated party,
an impersonation strategy, and
an attack goal.
7. The method of claim 1 , wherein the storage medium is a computer server managed by an entity other than the enterprise.
8. The method of claim 1 , wherein the first portion of the series of past communications includes all emails received by the employee
during the first interval of time.
9. The method of claim 1 , further comprising:
downloading, via the application programming interface, a second portion of the series of past communications corresponding to a second interval of time that precedes the first interval of time into the local processing environment; and
establishing whether any emails received during the second interval of time represent security risks by applying the ML model to the second portion of the series of past communications.
10. The method of claim 1 , further comprising:
examining the communication to establish multiple attributes; and
generating a statistical profile for the communication, wherein the statistical profile includes a risk score for each pair of attributes included in the multiple attributes, each risk score being based on riskiness of historical communications involving the corresponding pair of attributes.
11. The method of claim 1 , further comprising:
examining each past communication downloaded into the local processing environment to identify a sender identity and a sender email address; and
populating entries in a database such that sender identities are associated with the corresponding sender email addresses as identified in the past communications.
12. The method of claim 11 , further comprising:
receiving an email addressed to the employee;
examining the email to establish a sender identity and a sender email address; and
determining whether the email represents a security threat based on whether the sender identity and the sender email address as identified in the email match an entry in the database.
13. The method of claim 1 , further comprising:
responsive to a determination that the communication is indicative of an abnormal communication activity, uploading information related to the communication to a federated database used to protect multiple enterprises against security threats.
14. A system comprising:
a processor configured to:
establish, via an application programming interface, a connection with a storage medium that includes a series of past communications received by an employee of an enterprise;
download, via the application programming interface, a first portion of the series of past communications corresponding to a first interval of time into a local processing environment;
build a machine learning (ML) model for at least one of the employee or the enterprise by providing the first portion of the series of past communications to the ML model as training data;
receive, via the application programming interface, a communication addressed to the employee; and
establish whether the communication represents a security risk by applying the ML model to the communication, wherein an output produced by the ML model upon being applied to the communication indicates a score associated with a deviation of an assigned behavioral trait determined from the communication, wherein the assigned behavioral trait includes at least one of: (1) a frequency with which the employee uses a sender identity, (2) a frequency with which the employee uses a sender email address, or (3) a combination of the frequencies with which the employee uses the sender identity and the sender email address; and
a memory coupled to the processor and configured to provide the processor with instructions.
15. The system of claim 14 , wherein the processor is further configured to:
receive input indicative of an approval from an administrator associated with the enterprise to access the series of past communications;
wherein said establishing is performed in response to receiving the input.
16. The system of claim 14 , wherein the series of past communications includes multiple emails that were delivered to the employee.
17. The system of claim 14 , wherein the processor is further configured to:
examine each past communication in the first portion of the series of past communications to establish a set of attributes; and
provide the set of attributes derived from the first portion of the series of past communications to the ML model as training data.
18. The system of claim 14 , wherein the processor is further configured to:
determine, by the ML model, that the communication represents a security risk; and
characterize the security risk along multiple dimensions.
19. The system of claim 18 , wherein the multiple dimensions include:
an attacked party,
an attack vector,
an impersonated party,
an impersonation strategy, and
an attack goal.
20. The system of claim 14 , wherein the storage medium is a computer server managed by an entity other than the enterprise.
21. The system of claim 14 , wherein the first portion of the series of past communications includes all emails received by the employee during the first interval of time.
22. The system of claim 14 , wherein the processor is further configured to:
download, via the application programming interface, a second portion of the series of past communications corresponding to a second interval of time that precedes the first interval of time into the local processing environment; and
establish whether any emails received during the second interval of time represent security risks by applying the ML model to the second portion of the series of past communications.
23. The system of claim 14 , wherein the processor is further configured to:
examine the communication to establish multiple attributes; and
generate a statistical profile for the communication, wherein the statistical profile includes a risk score for each pair of attributes included in the multiple attributes, each risk score being based on riskiness of historical communications involving the corresponding pair of attributes.
24. The system of claim 14 , wherein the processor is further configured to:
examine each past communication downloaded into the local processing environment to identify a sender identity and a sender email address; and
populate entries in a database such that sender identities are associated with the corresponding sender email addresses as identified in the past communications.
25. The system of claim 24 , wherein the processor is further configured to:
receive an email addressed to the employee;
examine the email to establish a sender identity and a sender email address; and
determine whether the email represents a security threat based on whether the sender identity and the sender email address as identified in the email match an entry in the database.
26. The system of claim 14 , wherein the processor is further configured to:
responsive to a determination that the communication is indicative of an abnormal communication activity, upload information related to the communication to a federated database used to protect multiple enterprises against security threats.