Profiling Network Traffic Behavior for the purpose of Anomaly-based Intrusion Detection

被引：2

作者：

Gill, Manmeet Singh ^{[1
]}

Lindskog, Dale ^{[1
]}

Zavarsky, Pavol ^{[1
]}

机构：

[1] Concordia Univ Edmonton, Dept Informat Syst Secur & Assurance Management, Edmonton, AB, Canada

来源：

2018 17TH IEEE INTERNATIONAL CONFERENCE ON TRUST, SECURITY AND PRIVACY IN COMPUTING AND COMMUNICATIONS (IEEE TRUSTCOM) / 12TH IEEE INTERNATIONAL CONFERENCE ON BIG DATA SCIENCE AND ENGINEERING (IEEE BIGDATASE) | 2018年

关键词：

NIDS; normal and abnormal behavior; profiling baseline; threshold; statistical Analysis; data sets; features; anomaly detection;

D O I：

10.1109/TrustCom/BigDataSE.2018.00127

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

In this paper, we propose methods for profiling normal network traffic, methods that could be employed for the purpose of creating a baseline that would be used in the detection of threshold based anomalies in network traffic. This profiling is based on five proposed features of network traffic, and to illustrate, testing was done using recent and large data sets, and relying on various tools to statistically analyze network traffic. Although we have no pretensions of completeness, our results indicate that this is a promising approach to differentiate between normal and abnormal network traffic behavior, and therefore a promising contribution to anomaly based intrusion detection.

引用

页码：885 / 890

页数：6

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