Abstract: A system derives a reputation for a plurality of network addresses, the reputation of each network address determined by analyzing a plurality of high-level email features related to one or more emails originating from the network address. The plurality of high-level email features include domain registration analysis, hashed term frequency indexing, persistent communication, address age, correlation analysis, zombie detection, and hash vault matching.

Abstract: A computer network device receives a digital file and extracts a plurality of high level features from the file. The plurality of high level features are evaluated using a classifier to determine whether the file is benign or malicious. The file is forwarded to a requesting computer if the file is determined to be benign, and blocked if the file is determined to be malicious.

Abstract: Systems and methods for detecting a denial of service attack are disclosed. These may include receiving a plurality of web log traces from one of a plurality of web servers; extracting a first set of features from the plurality of web log traces; applying a first machine learning technique to the first set of features; producing a first plurality of user classifications for communication to the web server; extracting a second set of features from the plurality of web log traces; applying a second machine learning technique to the second set of features; producing a second plurality of user classification for communication to the web server; communicating the first plurality of user classifications to the web server based at least on the plurality of web log traces; and communicating the second plurality of user classifications to the web server based at least on the plurality of web log traces.

Abstract: Systems and methods for detecting a denial of service attack are disclosed. These may include receiving a plurality of web log traces from one of a plurality of web servers; extracting a first set of features from the plurality of web log traces; applying a first machine learning technique to the first set of features; producing a first plurality of user classifications for communication to the web server; extracting a second set of features from the plurality of web log traces; applying a second machine learning technique to the second set of features; producing a second plurality of user classification for communication to the web server; communicating the first plurality of user classifications to the web server based at least on the plurality of web log traces; and communicating the second plurality of user classifications to the web server based at least on the plurality of web log traces.

Abstract: Methods and systems for granular support vector machines. Granular support vector machines can randomly select samples of datapoints and project the samples of datapoints into a randomly selected subspaces to derive granules. A support vector machine can then be used to identify hyperplane classifiers respectively associated with the granules. The hyperplane classifiers can be used on an unknown datapoint to provide a plurality of predictions which can be aggregated to provide a final prediction associated with the datapoint.

Abstract: A system derives a reputation for a plurality of network addresses, the reputation of each network address determined by analyzing a plurality of high-level email features related to one or more emails originating from the network address. The plurality of high-level email features include domain registration analysis, hashed term frequency indexing, persistent communication, address age, correlation analysis, zombie detection, and hash vault matching.

Abstract: A computer network device receives a digital file and extracts a plurality of high level features from the file. The plurality of high level features are evaluated using a classifier to determine whether the file is benign or malicious. The file is forwarded to a requesting computer if the file is determined to be benign, and blocked if the file is determined to be malicious.

Abstract: Methods and systems for granular support vector machines. Granular support vector machines can randomly select samples of datapoints and project the samples of datapoints into a randomly selected subspaces to derive granules. A support vector machine can then be used to identify hyperplane classifiers respectively associated with the granules. The hyperplane classifiers can be used on an unknown datapoint to provide a plurality of predictions which can be aggregated to provide a final prediction associated with the datapoint.