Collaborative Attack Generation And Detection Using Machine Learning Techniques

International Journal of Innovative Research in Technology

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ISSN: 2349-6002 ESTD Year: 2014

UGC approved journal no 47859

Collaborative Attack Generation And Detection Using Machine Learning Techniques
Author(s):
Naimesh Kame, Sambhav Rakhe, Gitesh Chaudhari, Akash Ajnadkar, Shraddha Khonde
Keywords:
KDD99, Collaborative, Novel dataset, Intrusion Detection System
Abstract
Intrusions on systems are attempts to gain access to unauthorized data with malicious intent. Intrusion Detection Systems (IDS) is a system that can detect and report such attacks. Intruders always try to evade IDS taking advantage of its impotence to detect novel attacks, combined attacks or collaborative attacks. Combined attacks are attacks on a system consisting of two or more attacks done iteratively in a loop that hide the signature of a single attack. Collaborative attacks are more sophisticated, intelligent and powerful attacks that possess the ability to merge different attacks in a single packet. These attacks can depict the behaviour of various attacks but a signature of none. Detection of such attacks is only possible with a novel IDS dataset. KDD-99 is the most common IDS dataset; we use the attacks and features available in this dataset to make our collaborative IDS dataset. We also present a host-based machine learning IDS for detecting the same.
Article Details
Unique Paper ID: 149783 Publication Volume & Issue: Volume 7, Issue 1 Page(s): 586 - 589
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