Network anomaly detection using Two-dimensional Hidden Markov Model-based Viterbi algorithm

被引:8
|
作者
Alhaidari, Sulaiman [1 ]
Zohdy, Mohamed [1 ]
机构
[1] Oakland Univ, Sch Engn & Comp Sci, Rochester, MI 48309 USA
来源
2019 IEEE INTERNATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE TESTING (AITEST) | 2019年
关键词
Anomaly detection; Hidden Markov Model; Viterbi algorithm; NSL-KDD;
D O I
10.1109/AITest.2019.00-14
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Network anomaly detection has become very important area for both industrial application and academic research in the recent years. It is involved widely in a broad spectrum of domains and many research areas. Detecting anomalies in data is a crucial problem to diverse real world applications. In this paper, we propose a new approach using Two-dimensional Hidden Markov Model (HMM) based Viterbi algorithm, which is adopted as the anomaly detector. It is not only aiming to detect anomalous behaviors but also identifying the Intention behind them. Experimental results indicate that the approach is very effective for detecting the type as well as intention behind the anomalous behaviors.
引用
收藏
页码:17 / 18
页数:2
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