Abstract: Techniques for detecting anomalous network traffic are disclosed. In one particular embodiment, the techniques may be realized as a method for detecting anomalous network traffic comprising the steps of receiving a list including a plurality of processes and, for each process, a list of approved types of network traffic; monitoring network traffic of each process on the list of processes; upon detecting network traffic for a process on the list of processes, determining that the type of network traffic detected is not on the list of approved types for that process; and identifying the process as infected based on determining that the type of network traffic detected is not on the list of approved types for that process.

Abstract: A computer-implemented method for detecting malicious use of digital certificates may include determining that a digital certificate is invalid. The method may further include locating, within the invalid digital certificate, at least one field that was previously identified as being useful in distinguishing malicious use of invalid certificates from benign use of invalid certificates. The method may also include determining, based on analysis of information from the field of the invalid digital certificate, that the invalid digital certificate is potentially being used to facilitate malicious communications. The method may additionally include performing a security action in response to determining that the invalid digital certificate is potentially being used to facilitate malicious communications. Various other methods, systems, and computer-readable media are disclosed.

Abstract: Techniques for detecting anomalous network traffic are disclosed. In one particular embodiment, the techniques may be realized as a method for detecting anomalous network traffic comprising the steps of receiving a list including a plurality of processes and, for each process, a list of approved types of network traffic; monitoring network traffic of each process on the list of processes; upon detecting network traffic for a process on the list of processes, determining that the type of network traffic detected is not on the list of approved types for that process; and identifying the process as infected based on determining that the type of network traffic detected is not on the list of approved types for that process.

Abstract: Techniques for detecting malicious code are disclosed. In one particular embodiment, the techniques may be realized as a method for detecting malicious code comprising the steps of identifying a query to a domain name service that resolves to a local address; identifying the process that originated the identified query; and designating the identified process as infected based on identifying the process as having originated the query that resolved to a local address.

Abstract: A computer-implemented method for using event-correlation graphs to detect attacks on computing systems may include (1) detecting a suspicious event involving a first actor within a computing system, (2) constructing an event-correlation graph that includes a first node that represents the first actor, a second node that represents a second actor, and an edge that interconnects the first node and the second node and represents a suspicious event involving the first actor and the second actor, (3) calculating, based at least in part on the additional suspicious event, an attack score for the event-correlation graph, (4) determining that the attack score is greater than a predetermined threshold, and (5) determining, based at least in part on the attack score being greater than the predetermined threshold, that the suspicious event may be part of an attack on the computing system. Various other methods, systems, and computer-readable media are also disclosed.

Abstract: A computer-implemented method for using event-correlation graphs to detect attacks on computing systems may include (1) detecting a suspicious event involving a first actor within a computing system, (2) constructing an event-correlation graph that includes a first node that represents the first actor, a second node that represents a second actor, and an edge that interconnects the first node and the second node and represents a suspicious event involving the first actor and the second actor, (3) calculating, based at least in part on the additional suspicious event, an attack score for the event-correlation graph, (4) determining that the attack score is greater than a predetermined threshold, and (5) determining, based at least in part on the attack score being greater than the predetermined threshold, that the suspicious event may be part of an attack on the computing system. Various other methods, systems, and computer-readable media are also disclosed.