Invention Grant
US08762298B1 Machine learning based botnet detection using real-time connectivity graph based traffic features
有权
基于机器学习的僵尸网络检测使用基于实时连通图的流量特征
- Patent Title: Machine learning based botnet detection using real-time connectivity graph based traffic features
- Patent Title (中): 基于机器学习的僵尸网络检测使用基于实时连通图的流量特征
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Application No.: US12985263Application Date: 2011-01-05
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Publication No.: US08762298B1Publication Date: 2014-06-24
- Inventor: Supranamaya Ranjan , Joshua Robinson , Feilong Chen
- Applicant: Supranamaya Ranjan , Joshua Robinson , Feilong Chen
- Applicant Address: US CA Sunnyvale
- Assignee: Narus, Inc.
- Current Assignee: Narus, Inc.
- Current Assignee Address: US CA Sunnyvale
- Agency: Fernandez & Associates, LLP
- Main IPC: G06N5/02
- IPC: G06N5/02 ; G06N99/00 ; H04L1/00 ; H04L29/06

Abstract:
A method for identifying a botnet in a network, including analyzing historical network data using a pre-determined heuristic to determine values of a connectivity graph based feature in the historical network data, obtaining a ground truth data set having labels assigned to data units in the historical network data identifying known malicious nodes in the network, analyzing the historical network data and the ground truth data set using a machine learning algorithm to generate a model representing the labels as a function of the values of the connectivity graph based feature, analyzing real-time network data using the pre-determined heuristic to determine a value of the connectivity graph based feature for a data unit in the real-time network data, assigning a label to the data unit by applying the model to the value of the connectivity graph based feature, and categorizing the data unit as associated with the botnet based on the label.
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