Abstract: Methods, media, and systems for securing communications between a first node and a second node are provided. In some embodiments, methods for securing communication between a first node and a second node are provided. The methods comprising: receiving at least one model of behavior of the second node at the first node; and authorizing the first node to receive traffic from the second node based on the difference between the at least one model of behavior of the second node and at least one model of behavior of the first node.

Abstract: Systems, methods, and media for outputting a dataset based upon anomaly detection are provided. In some embodiments, methods for outputting a dataset based upon anomaly detection: receive a training dataset having a plurality of n-grams, which plurality includes a first plurality of distinct training n-grams each being a first size; compute a first plurality of appearance frequencies, each for a corresponding one of the first plurality of distinct training n-grams; receive an input dataset including first input n-grams each being the first size; define a first window in the input dataset; identify as being first matching n-grams the first input n-grams in the first window that correspond to the first plurality of distinct training n-grams; compute a first anomaly detection score for the input dataset using the first matching n-grams and the first plurality of appearance frequencies; and output the input dataset based on the first anomaly detection score.

Abstract: Systems and methods for correlating and distributing intrusion alert information among collaborating computer systems are provided. These systems and methods provide an alert correlator and an alert distributor that enable early signs of an attack to be detected and rapidly disseminated to collaborating systems. The alert correlator utilizes data structures to correlate alert detections and provide a mechanism through which threat information can be revealed to other collaborating systems. The alert distributor uses an efficient technique to group collaborating systems and then pass data between certain members of those groups according to a schedule. In this way data can be routinely distributed without generating excess traffic loads.

Abstract: Systems, methods, and media for outputting data based on anomaly detection are provided. In some embodiments, a method for outputting data based on anomaly detection is provided, the method comprising: receiving, using a hardware processor, an input dataset; identifying grams in the input dataset that substantially include distinct byte values; creating an input subset by removing the identified grams from the input dataset; determining whether the input dataset is likely to be anomalous based on the identified grams, and determining whether the input dataset is likely to be anomalous by applying the input subset to a binary anomaly detection model to check for an n-gram in the input subset; and outputting the input dataset based on the likelihood that the input dataset is anomalous.

Abstract: Methods, media, and systems for securing communications between a first node and a second node are provided. In some embodiments, methods for securing communication between a first node and a second node are provided. The methods comprising: receiving at least one model of behavior of the second node at the first node; and authorizing the first node to receive traffic from the second node based on the difference between the at least one model of behavior of the second node and at least one model of behavior of the first node.

Abstract: Systems, methods, and media for outputting a dataset based upon anomaly detection are provided. In some embodiments, methods for outputting a dataset based upon anomaly detection: receive a training dataset having a plurality of n-grams, which plurality includes a first plurality of distinct training n-grams each being a first size; compute a first plurality of appearance frequencies, each for a corresponding one of the first plurality of distinct training n-grams; receive an input dataset including first input n-grams each being the first size; define a first window in the input dataset; identify as being first matching n-grams the first input n-grams in the first window that correspond to the first plurality of distinct training n-grams; compute a first anomaly detection score for the input dataset using the first matching n-grams and the first plurality of appearance frequencies; and output the input dataset based on the first anomaly detection score.

Abstract: Methods, media, and systems for securing communications between a first node and a second node are provided. In some embodiments, methods for securing communication between a first node and a second node are provided. The methods comprising: receiving at least one model of behavior of the second node at the first node; and authorizing the first node to receive traffic from the second node based on the difference between the at least one model of behavior of the second node and at least one model of behavior of the first node.

Abstract: Methods, media, and systems for securing communications between a first node and a second node are provided. In some embodiments, methods for securing communication between a first node and a second node are provided. The methods comprising: receiving at least one model of behavior of the second node at the first node; and authorizing the first node to receive traffic from the second node based on the difference between the at least one model of behavior of the second node and at least one model of behavior of the first node.

Abstract: Systems, methods, and media for outputting a dataset based upon anomaly detection are provided. In some embodiments, methods for outputting a dataset based upon anomaly detection: receive a training dataset having a plurality of n-grams, which plurality includes a first plurality of distinct training n-grams each being a first size; compute a first plurality of appearance frequencies, each for a corresponding one of the first plurality of distinct training n-grams; receive an input dataset including first input n-grams each being the first size; define a first window in the input dataset; identify as being first matching n-grams the first input n-grams in the first window that correspond to the first plurality of distinct training n-grams; compute a first anomaly detection score for the input dataset using the first matching n-grams and the first plurality of appearance frequencies; and output the input dataset based on the first anomaly detection score.

Abstract: Methods, media, and systems for securing communications between a first node and a second node are provided. In some embodiments, methods for securing communication between a first node and a second node are provided. The methods comprising: receiving at least one model of behavior of the second node at the first node; and authorizing the first node to receive traffic from the second node based on the difference between the at least one model of behavior of the second node and at least one model of behavior of the first node.

Abstract: Systems and methods for correlating and distributing intrusion alert information among collaborating computer systems are provided. These systems and methods provide an alert correlator and an alert distributor that enable early signs of an attack to be detected and rapidly disseminated to collaborating systems. The alert correlator utilizes data structures to correlate alert detections and provide a mechanism through which threat information can be revealed to other collaborating systems. The alert distributor uses an efficient technique to group collaborating systems and then pass data between certain members of those groups according to a schedule. In this way data can be routinely distributed without generating excess traffic loads.

Abstract: Systems and methods for correlating and distributing intrusion alert information among collaborating computer systems are provided. These systems and methods provide an alert correlator and an alert distributor that enable early signs of an attack to be detected and rapidly disseminated to collaborating systems. The alert correlator utilizes data structures to correlate alert detections and provide a mechanism through which threat information can be revealed to other collaborating systems. The alert distributor uses an efficient technique to group collaborating systems and then pass data between certain members of those groups according to a schedule. In this way data can be routinely distributed without generating excess traffic loads.

Abstract: Systems, methods, and media for outputting data based on anomaly detection are provided. In some embodiments, a method for outputting data based on anomaly detection is provided, the method comprising: receiving, using a hardware processor, an input dataset; identifying grams in the input dataset that substantially include distinct byte values; creating an input subset by removing the identified grams from the input dataset; determining whether the input dataset is likely to be anomalous based on the identified grams, and determining whether the input dataset is likely to be anomalous by applying the input subset to a binary anomaly detection model to check for an n-gram in the input subset; and outputting the input dataset based on the likelihood that the input dataset is anomalous.

Abstract: Systems, methods, and media for outputting data based on anomaly detection are provided. In some embodiments, a method for outputting data based on anomaly detection is provided, the method comprising: receiving, using a hardware processor, an input dataset; identifying grams in the input dataset that substantially include distinct byte values; creating an input subset by removing the identified grams from the input dataset; determining whether the input dataset is likely to be anomalous based on the identified grams, and determining whether the input dataset is likely to be anomalous by applying the input subset to a binary anomaly detection model to check for an n-gram in the input subset; and outputting the input dataset based on the likelihood that the input dataset is anomalous.

Abstract: Systems, methods, and media for outputting data based on anomaly detection are provided. In some embodiments, a method for outputting data based on anomaly detection is provided, the method comprising: receiving, using a hardware processor, an input dataset; identifying grams in the input dataset that substantially include distinct byte values; creating an input subset by removing the identified grams from the input dataset; determining whether the input dataset is likely to be anomalous based on the identified grams, and determining whether the input dataset is likely to be anomalous by applying the input subset to a binary anomaly detection model to check for an n-gram in the input subset; and outputting the input dataset based on the likelihood that the input dataset is anomalous.

Abstract: Systems, methods, and media for outputting a dataset based upon anomaly detection are provided. In some embodiments, methods for outputting a dataset based upon anomaly detection: receive a training dataset having a plurality of n-grams, which plurality includes a first plurality of distinct training n-grams each being a first size; compute a first plurality of appearance frequencies, each for a corresponding one of the first plurality of distinct training n-grams; receive an input dataset including first input n-grams each being the first size; define a first window in the input dataset; identify as being first matching n-grams the first input n-grams in the first window that correspond to the first plurality of distinct training n-grams; compute a first anomaly detection score for the input dataset using the first matching n-grams and the first plurality of appearance frequencies; and output the input dataset based on the first anomaly detection score.

Abstract: Systems and methods provide an alert correlator and an alert distributor that enable early signs of an attack to be detected and rapidly disseminated to collaborating systems. The alert correlator utilizes data structures to correlate alert detections and provide a mechanism through which threat information can be revealed to other collaborating systems. The alert distributor uses an efficient technique to group collaborating systems and then pass data between certain members of those groups according to a schedule. In this way data can be routinely distributed without generating excess traffic loads.

Abstract: Systems, methods, and media for outputting data based on anomaly detection are provided. In some embodiments, methods for outputting data based on anomaly detection include: receiving a known-good dataset; storing distinct n-grams from the known-good dataset to form a binary anomaly detection model; receiving known-good new n-grams; computing a rate of receipt of distinct n-grams in the new n-grams; determining whether further training of the anomaly detection model is necessary based on the rate of receipt on distinct n-grams; using the binary anomaly detection model to determine whether an input dataset contains an anomaly; and outputting the input dataset based on whether the input dataset contains an anomaly.

Abstract: Systems and methods for correlating and distributing intrusion alert information among collaborating computer systems are provided. These systems and methods provide an alert correlator and an alert distributor that enable early signs of an attack to be detected and rapidly disseminated to collaborating systems. The alert correlator utilizes data structures to correlate alert detections and provide a mechanism through which threat information can be revealed to other collaborating systems. The alert distributor uses an efficient technique to group collaborating systems and then pass data between certain members of those groups according to a schedule. In this way data can be routinely distributed without generating excess traffic loads.

Abstract: Systems, methods, and media for outputting a dataset based upon anomaly detection are provided. In some embodiments, methods for outputting a dataset based upon anomaly detection: receive a training dataset having a plurality of n-grams, which plurality includes a first plurality of distinct training n-grams each being a first size; compute a first plurality of appearance frequencies, each for a corresponding one of the first plurality of distinct training n-grams; receive an input dataset including first input n-grams each being the first size; define a first window in the input dataset; identify as being first matching n-grams, the first input n-grams in the first window that correspond to the first plurality of distinct training n-grams; compute a first anomaly detection score for the input dataset using the first matching n-grams and the first plurality of appearance frequencies; and output the input dataset based on the first anomaly detection score.