NDLI: Boosted Modified Probabilistic Neural Network (BMPNN) for Network Intrusion Detection

Content Provider	IEEE Xplore Digital Library
Author	Tich Phuoc Tran Jan, T.
Copyright Year	2006
Description	Author affiliation: Univ. of Technol., Sydney (Tich Phuoc Tran; Jan, T.)
Abstract	Most of the currently available network security techniques are not able to cope with the dynamic and increasingly complex nature of the attacks on distributed computer systems. An automated and adaptive defensive tool is imperative for computer networks. One of the emerging solutions for Network Security is the Intrusion Detection System (IDS). However, this technology still faces some challenges such as low detection rates, high false alarm rates and requirement of heavy computational power. To overcome these difficulties, this paper proposes an innovative Machine Learning algorithm called Boosted Modified Probabilistic Neural Network (BMPNN) which utilizes semi-parametric learning model and Adaptive boosting techniques to reduce learning bias and generalization variance in difficult classification. In this paper, BMPNN is implemented as a classifier to detect different types of network anomalies in the KDD-99 benchmark. Extensive experimental outcome indicates that the proposed BMPNN outperforms other state-of-the-art learning algorithms in terms of detection accuracy and model robustness at an affordable computational cost.
Starting Page	2354
Ending Page	2361
File Size	224881
Page Count	8
File Format	PDF
ISBN	0780394909
DOI	10.1109/IJCNN.2006.247058
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2006-07-16
Publisher Place	Canada
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Neural networks Intrusion detection Computer networks Face detection Machine learning Computer security Distributed computing Power system security Machine learning algorithms Power system modeling Generalization Variance Network Intrusion Detection Artificial Neural Network Learning Bias
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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