NDLI: Unusual internet traffic detection at network edge

Content Provider	IEEE Xplore Digital Library
Author	Bansal, Neha Kaushal, Rishabh
Copyright Year	2015
Description	Author affiliation: Department of Information Technology, Indira Gandhi Delhi Technical University for Women, India (Bansal, Neha; Kaushal, Rishabh)
Abstract	Network administrators ensure that all the users within network get fair share of bandwidth, any bandwidth limit violations is identified and provide some additional controls like denied access to particular websites, etc. To achieve this, network administrators monitor all the traffic between the LAN in campus-wide network and the outside Internet world. This monitoring is typically achieved by capturing and analyzing the traffic logs at the Proxy Server, installed between the LAN and the outside Internet. However, this monitoring is primarily statistical in nature and provides no significant actionable results. In our work we have made an attempt to provide a method for intelligent actionable information to network administrators by analyzing and predicting the Internet access behavior at network layer using machine learning algorithms. By network layer we mean that we focus on characterizing traffic at IP address level. For our study we have collected squid proxy server logs and performed analysis of various features of network traffic at network and user level. We estimate the most probable range of values for the various features and determine IP addresses deviating from the normal network access feature values. Thereafter, we have applied four different supervised machine learning algorithms on our labelled dataset and compared these algorithms on various classification matrices like TP, FP, TN and FN. Our results show that Decision Tree and Random Forest give an overall accuracy close to 95%, whereas Naive Bayes and SVM resulted in an overall accuracy of around 85%.
Starting Page	179
Ending Page	185
File Size	582759
Page Count	7
File Format	PDF
e-ISBN	9781467373098
DOI	10.1109/CoCoNet.2015.7411184
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-12-16
Publisher Place	India
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Squid Server Machine learning algorithms Telecommunication traffic Network Security Data Mining Internet Classification algorithms IP networks Servers Monitoring
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
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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