2013/09/19 by Jing Wang, Wang, Jing, Daniel Rossell +5
Computer Science · Mathematics · Physics and Astronomy · #Anomaly Detection Techniques and Applications #Complex Network Analysis Techniques #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Network Security and Intrusion Detection #Networking and Internet Architecture (cs.NI) #cs.LG #cs.NI #stat.ML
paper · pdf · doi:10.48550/arxiv.1309.4844
7 pages. 1 more figure than final CDC 2013 version
arxiv created 2013/09/19 · openalex publication_date 2013/09/19 · arxiv updated 2013/09/20 · openalex created_date 2016/06/24 · openalex updated_date 2026/07/28
We present five methods to the problem of network anomaly detection. These methods cover most of the common techniques in the anomaly detection field, including Statistical Hypothesis Tests (SHT), Support Vector Machines (SVM) and clustering analysis. We evaluate all methods in a simulated network that consists of nominal data, three flow-level anomalies and one packet-level attack. Through analyzing the results, we point out the advantages and disadvantages of each method and conclude that combining the results of the individual methods can yield improved anomaly detection results.