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: A method, apparatus, and medium are provided for tracing the origin of network transmissions. Connection records are maintained at computer system for storing source and destination addresses. The connection records also maintain a statistical distribution of data corresponding to the data payload being transmitted. The statistical distribution can be compared to that of the connection records in order to identify the sender. The location of the sender can subsequently be determined from the source address stored in the connection record. The process can be repeated multiple times until the location of the original sender has been traced.

Abstract: A system and methods for detecting intrusions in the operation of a computer system comprises a sensor configured to gather information regarding the operation of the computer system, to format the information in a data record having a predetermined format, and to transmit the data in the predetermined data format. A data warehouse is configured to receive the data record from the sensor in the predetermined data format and to store the data in a SQL database. A detection model generator is configured to request data records from the data warehouse in the predetermined data format, to generate an intrusion detection model based on said data records, and to transmit the intrusion detection model to the data warehouse according to the predetermined data format. A detector is configured to receive a data record in the predetermined data format from the sensor and to classify the data record in real-time as one of normal operation and an attack based on said intrusion detection model.

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: A method for unsupervised anomaly detection, which are algorithms that are designed to process unlabeled data. Data elements are mapped to a feature space which is typically a vector space d. Anomalies are detected by determining which points lies in sparse regions of the feature space. Two feature maps are used for mapping data elements to a feature apace. A first map is a data-dependent normalization feature map which we apply to network connections. A second feature map is a spectrum kernel which we apply to system call traces.

Abstract: A system and methods of detecting an occurrence of a violation of an email security policy of a computer system. A model relating to the transmission of prior emails through the computer system is defined which is derived from statistics relating to the prior emails. For selected emails to be analyzed, statistics concerning the selected email are gathered. Such statistics may refer to the behavior or other features of the selected emails, attachments to emails, or email accounts. The determination of whether a violation of an email security policy has occurred is performed by applying the model of prior email transmission to the statistics relating to the selected email. The model may be statistical or probabilistic. A model of prior email transmission may include grouping email recipients into cliques. A determination of a violation of a security policy may occur if email recipients for a particular email are in more than one clique.

Abstract: Systems, methods, and media for detecting network anomalies are provided. In some embodiments, a training dataset of communication protocol messages having argument strings is received. The content and structure associated with each of the argument strings is determined and a probabilistic model is trained using the determined content and structure of each of the argument strings. A communication protocol message having an argument string that is transmitted from a first processor to a second processor across a computer network is received. The received communication protocol message is compared to the probabilistic model and then it is determined whether the communication protocol message is anomalous.

Abstract: A system and methods for detecting intrusions in the operation of a computer system comprises a sensor configured to gather information regarding the operation of the computer system, to format the information in a data record having a predetermined format, and to transmit the data in the predetermined data format. A data warehouse is configured to receive the data record from the sensor in the predetermined data format and to store the data in a SQL database. A detection model generator is configured to request data records from the data warehouse in the predetermined data format, to generate an intrusion detection model based on said data records, and to transmit the intrusion detection model to the data warehouse according to the predetermined data format. A detector is configured to receive a data record in the predetermined data format from the sensor and to classify the data record in real-time as one of normal operation and an attack based on said intrusion detection model.

Abstract: Systems, methods, and media for generating bait information for trap-based defenses are provided. In some embodiments, methods for generating bait information for trap-based defenses include: recording historical information of a network; translating the historical information; and generating bait information by tailoring the translated historical information.

Abstract: A system and methods for detecting intrusions in the operation of a computer system comprising a sensor configured to gather information regarding the operation of the computer system, to format the information in a data record, and to transmit the data record. A database is configured to receive the data record from the sensor and to store the data record. A detection model generator is configured to request data records from the database, to generate an intrusion detection model, and to transmit the intrusion detection model to the database. A detector is configured to receive a data record from the sensor and to classify the data record in real-time as one of normal operation and an attack. A data analysis engine is configured to request data records from the database and to perform a data processing function on the data records.

Abstract: Methods, media, and systems for detecting attack are provided. In some embodiments, the methods include: comparing at least part of a document to a static detection model; determining whether attacking code is included in the document based on the comparison of the document to the static detection model; executing at least part of the document; determining whether attacking code is included in the document based on the execution of the at least part of the document; and if attacking code is determined to be included in the document based on at least one of the comparison of the document to the static detection model and the execution of the at least part of the document, reporting the presence of an attack.

Abstract: Systems, methods, and media for detecting network anomalies are provided. In some embodiments, a training dataset of communication protocol messages having argument strings is received. The content and structure associated with each of the argument strings is determined and a probabilistic model is trained using the determined content and structure of each of the argument strings. A communication protocol message having an argument string that is transmitted from a first processor to a second processor across a computer network is received. The received communication protocol message is compared to the probabilistic model and then it is determined whether the communication protocol message is anomalous.

Abstract: Systems, methods, and media for generating bait information for trap-based defenses are provided. In some embodiments, methods for generating bait information for trap-based defenses include: recording historical information of a network; translating the historical information; and generating bait information by tailoring the translated historical information.

Abstract: Methods, media, and systems for detecting anomalous program executions are provided. In some embodiments, methods for detecting anomalous program executions are provided, comprising: executing at least a part of a program in an emulator; comparing a function call made in the emulator to a model of function calls for the at least a part of the program; and identifying the function call as anomalous based on the comparison. In some embodiments, methods for detecting anomalous program executions are provided, comprising: modifying a program to include indicators of program-level function calls being made during execution of the program; comparing at least one of the indicators of program-level function calls made in the emulator to a model of function calls for the at least a part of the program; and identifying a function call corresponding to the at least one of the indicators as anomalous based on the comparison.

Abstract: Methods, media, and systems for detecting attack are provided. In some embodiments, the methods include: comparing at least part of a document to a static detection model; determining whether attacking code is included in the document based on the comparison of the document to the static detection model; executing at least part of the document; determining whether attacking code is included in the document based on the execution of the at least part of the document; and if attacking code is determined to be included in the document based on at least one of the comparison of the document to the static detection model and the execution of the at least part of the document, reporting the presence of an attack.

Abstract: A method, apparatus and medium are provided for detecting anomalous payloads transmitted through a network. The system receives payloads within the network and determines a length for data contained in each payload. A statistical distribution is generated for data contained in each payload received within the network, and compared to a selected model distribution representative of normal payloads transmitted through the network. The model payload can be selected such that it has a predetermined length range that encompasses the length for data contained in the received payload. Anomalous payloads are then identified based on differences detected between the statistical distribution of received payloads and the model distribution. The system can also provide for automatic training and incremental updating of models.

Abstract: Methods, systems, and media for masquerade attack detection by monitoring computer user behavior are provided. In accordance with some embodiments, a method for detecting masquerade attacks is provided, the method comprising: monitoring a first plurality of user actions and access of decoy information in a computing environment; generating a user intent model for a category that includes at least one of the first plurality of user actions; monitoring a second plurality of user actions; comparing the second plurality of user actions with the user intent model by determining deviation from the generated user intent model; identifying whether the second plurality of user actions is a masquerade attack based at least in part on the comparison; and generating an alert in response to identifying that the second plurality of user actions is the masquerade attack and in response to determining that the second plurality of user actions includes accessing the decoy information in the computing environment.

Abstract: In accordance with some embodiments of the present invention, systems and methods that protect an application from attacks are provided. In some embodiments of the present invention, input from an input source, such as traffic from a communication network, can be routed through a filtering proxy that includes one or more filters, classifiers, and/or detectors. In response to the input passing through the filtering proxy to the application, a supervision framework monitors the input for attacks (e.g., code injection attacks). The supervision framework can provide feedback to tune the components of the filtering proxy.

Abstract: Methods, media, and systems for detecting an anomalous sequence of function calls are provided. The methods can include compressing a sequence of function calls made by the execution of a program using a compression model; and determining the presence of an anomalous sequence of function calls in the sequence of function calls based on the extent to which the sequence of function calls is compressed. The methods can further include executing at least one known program; observing at least one sequence of function calls made by the execution of the at least one known program; assigning each type of function call in the at least one sequence of function calls made by the at least one known program a unique identifier; and creating at least part of the compression model by recording at least one sequence of unique identifiers.

Abstract: Methods, media, and systems for detecting an anomalous sequence of function calls are provided. The methods can include compressing a sequence of function calls made by the execution of a program using a compression model; and determining the presence of an anomalous sequence of function calls in the sequence of function calls based on the extent to which the sequence of function calls is compressed. The methods can further include executing at least one known program; observing at least one sequence of function calls made by the execution of the at least one known program; assigning each type of function call in the at least one sequence of function calls made by the at least one known program a unique identifier; and creating at least part of the compression model by recording at least one sequence of unique identifiers.