NDLI: A Two-Stage Process Based on Data Mining and Optimization to Identify False Positives and False Negatives Generated by Intrusion Detection Systems

Content Provider	IEEE Xplore Digital Library
Author	Fatma, H. Limam, M.
Copyright Year	2015
Description	Author affiliation: Dhofar Univ., Salalah, Oman (Limam, M.) \|\| ISG, Univ. of Tunis, Tunis, Tunisia (Fatma, H.)
Abstract	To ensure the protection of computer networks, an intrusion detection system (IDS) should be integrated in the security infrastructure. However, IDSs generate a high amount of false alerts exceeding the administrator ability for analysis and omit several attacks which can threaten the network security. In this paper, a two-stage process based on data mining and optimization is proposed having as input the outcome of multiple IDSs. In the first stage, for each IDS the set of elementary alerts is clustered to create a set of meta-alerts. Then, we remove false positives from the sets of meta-alerts using a binary optimization problem. In the second stage, we discard the meta-alerts generated by all IDSs and only those missed by one, two or most of them are left. This set is called the set of potential false negatives. In fact, at this level a meta-alerts fusion is performed to avoid the redundancy between meta-alerts collected from multiple IDSs. Finally, a binary classification algorithm is proposed to classify the potential false negatives either as real attacks or not. Experimental results show that our proposed process outperforms concurrent methods by significantly reducing the rate of false positives and false negatives.
Starting Page	308
Ending Page	311
File Size	497917
Page Count	4
File Format	PDF
e-ISBN	9781467386609
DOI	10.1109/CIS.2015.82
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-12-19
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	intrusion detection systems Redundancy Classification algorithms Security false negatives Optimization false positives Training Clustering algorithms binary optimization clustering binary classification Testing
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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