Mcafee, Inc.

Yuanchen He

Yuchun Tang

Sven Krasser

Zhenyu Zhong

Dmitri Alperovitch

Jonathan Alexander Zdziarski

Mark Gilbert

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.

Malware detection via reputation system

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.

Reputation management for network content classification

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.

System and method for detection of denial of service attacks

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.

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Johns Creek, GA, United States of America

Yuanchen He