NDLI: Network Forensic Analysis Using Growing Hierarchical SOM

Content Provider	IEEE Xplore Digital Library
Author	Shin-Ying Huang Yennun Huang
Copyright Year	2013
Description	Author affiliation: Acad. Sinica, Res. Center for Inf. Technol. Innovation, Taipei, Taiwan (Shin-Ying Huang; Yennun Huang)
Abstract	IP flow analysis is an effective way of doing network forensic analysis which aims to detect attack patterns and identify attackers in a given network traffic data. For attacks such as Distributed Denial of Service (DDoS), efficiently identifying the botnet in time can be a challenge. Recently, the unsupervised learning methods such as the K-means, self-organizing map (SOM), and growing hierarchical self-organizing map (GHSOM) have been shown to be able to facilitate network anomaly detection. However, there is no study focusing on mining IP flows with high variability of attacks in an intuitive way. In this study, we leverage the visualization specialty of GHSOM to help analyze the static IP flow data, also called the network traces of victims, to identify any suspicious IP source. The GHSOM can generate a hierarchical architecture based on the input data and help reveal their inherent hierarchical relationships. For example, the geometric distances between each attack pattern and its descriptive information are revealed in the topological space which forms the signature of a botnet. The IP flows clustered by GHSOM are grouped with distinctive connecting features, and the differences between each cluster are visualized with sequential time stamps which can be used to portray various attack patterns, help explore the sources of attacks and understand the behaviors of attacks. The experimental results from real-world traffic data show that forensic analysis using GHSOM can efficiently identify several DDoS attack patterns and generate filtering rules for intrusion detection system (IDS) by further inspecting and book marking the suspicious IP sources.
Sponsorship	Toshiba
Starting Page	536
Ending Page	543
File Size	534310
Page Count	8
File Format	PDF
e-ISBN	9781479931422
DOI	10.1109/ICDMW.2013.66
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-12-07
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Filtering Forensics Neural networks Data visualization Telecommunication traffic Network anomaly detection Data clustering Telescopes Feature extraction Internet Network forensics IP networks
Content Type	Text
Resource Type	Article

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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