Abstract: An instruction sequence detection system is trained to detect instruction sequences of interest, such as threats by malicious computer data. Training includes distilling the characteristics of known instruction sequences of interest (e.g., intrusion by computer viruses, exploits, worms, or the like) into a set of meta-expressions. At run-time, the instruction sequence detection system combines the minimal set of meta-expressions with efficient computer algorithms for evaluating meta-expressions to detect known instruction sequences of interest, as well as their unknown variants, among an unknown set of instruction sequences. The instruction sequence detection system may provide an appropriate response upon the detection of instruction sequences of interest.

Abstract: A network activity detection system is trained to detect network activities of interest such as threats by malicious computer data. The training involves distilling the characteristics of known network activities of interest (e.g., intrusion by computer viruses, exploits, worms, or the like) into a minimal set of meta-expressions. At run-time, the network activity detection system combines the minimal set of meta-expressions with efficient computer algorithms for evaluating meta-expressions to detect known network activities of interest, as well as their unknown variants, among an unknown set of network activity. The network activity detection system may produce appropriate responses upon the detection of network activities of interest.

Abstract: Systems and methods for detecting a visual characteristic of interest within an image are disclosed. An example method involves obtaining an image that includes at least one pixel representing a visual characteristic of interest, creating a first sequence and a second sequence of bitwise data from values associated with the pixel, and converting these bitwise sequences into a first sequence of integers and a second sequence of integers. Using a distance function, a similarity metric is determined between the first sequence of integers and the second sequence of integers. Based on the similarity metric, a third sequence of integers is created and stored. The third sequence of integers can be used to facilitate the identification of the visual characteristic of interest in other images.

Abstract: A digital security threat management system is disclosed. The system detects the presence of a computing system, on a network, that has been compromised by an undetected and/or unknown digital security threat. The digital security threat management system recognizes characteristic emanations from a computer system that has been compromised. Because the characteristic emanations that result from a known threat can be the same as the characteristic emanations that result from an undetected and/or unknown threat, the digital security threat management system can learn to detect a computing system that has been compromised by an unknown threat if the security threat management system recognizes characteristic emanations from a previous attack, based on a known threat, of the computing system. In this way, the system can detect the presence of a compromised computing system, even if the cause of the compromise remains undetected and/or unknown. Appropriate remedial action may be taken upon detection.

Abstract: Network activity detectors, such as firewalls, communicate with one another to form a Unified Threat Management System. A first network activity detector sends a request for configuration settings to a second network activity detector. The second network activity detector sends a set of configuration settings in response to the request. The configuration settings include information for detecting digital security threats and/or for responding to detected digital security threats. In this way, configuration settings are propagated from one network activity detector to another so that network activity detectors within a UTMS system are configured consistently, e.g., have up-to-date information for detecting and/or responding to digital security threats.

Abstract: A digital security threat management system is disclosed. The system detects the presence of a computing system, on a network, that has been compromised by an undetected and/or unknown digital security threat. The digital security threat management system recognizes characteristic emanations from a computer system that has been compromised. Because the characteristic emanations that result from a known threat can be the same as the characteristic emanations that result from an undetected and/or unknown threat, the digital security threat management system can learn to detect a computing system that has been compromised by an unknown threat if the security threat management system recognizes characteristic emanations from a previous attack, based on a known threat, of the computing system. In this way, the system can detect the presence of a compromised computing system, even if the cause of the compromise remains undetected and/or unknown. Appropriate remedial action may be taken upon detection.

Abstract: A digital security threat management system is disclosed. The system detects the presence of a computing system, on a network, that has been compromised by an undetected and/or unknown digital security threat. The digital security threat management system recognizes characteristic emanations from a computer system that has been compromised. Because the characteristic emanations that result from a known threat can be the same as the characteristic emanations that result from an undetected and/or unknown threat, the digital security threat management system can learn to detect a computing system that has been compromised by an unknown threat if the security threat management system recognizes characteristic emanations from a previous attack, based on a known threat, of the computing system. In this way, the system can detect the presence of a compromised computing system, even if the cause of the compromise remains undetected and/or unknown. Appropriate remedial action may be taken upon detection.

Abstract: A network activity detection system is trained to detect network activities of interest such as threats by malicious computer data. The training involves distilling the characteristics of known network activities of interest (e.g., intrusion by computer viruses, exploits, worms, or the like) into a minimal set of meta-expressions. At run-time, the network activity detection system combines the minimal set of meta-expressions with efficient computer algorithms for evaluating meta-expressions to detect known network activities of interest, as well as their unknown variants, among an unknown set of network activity. The network activity detection system may produce appropriate responses upon the detection of network activities of interest.

Abstract: A network activity detection system is trained to detect network activities of interest such as threats by malicious computer data. The training involves distilling the characteristics of known network activities of interest (e.g., intrusion by computer viruses, exploits, worms, or the like) into a minimal set of meta-expressions. At run-time, the network activity detection system combines the minimal set of meta-expressions with efficient computer algorithms for evaluating meta-expressions to detect known network activities of interest, as well as their unknown variants, among an unknown set of network activity. The network activity detection system may produce appropriate responses upon the detection of network activities of interest.