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General Information
    • ISSN: 1793-8244
    • Frequency: Quarterly
    • DOI: 10.18178/JACN
    • Editor-in-Chief: Dr. Ka Wai Gary Wong
    • Executive Editor: Ms. Julia S. Ma
    • Abstracting/ Indexing: EI (INSPEC, IET), Engineering & Technology Digital Library, DOAJ, Electronic Journals Library, Ulrich's Periodicals Directory, International Computer Science Digital Library (ICSDL), ProQuest, and Google Scholar.
    • E-mail: jacn@ejournal.net
Editor-in-chief
Dr. Ka Wai Gary Wong
Department of Mathematics and Information Technology The Hong Kong Institute of Education, Hong Kong
It's a honor to serve as the editor-in-chief of JACN. I'll work together with the editors and reviewers to help the journal progress
JACN 2014 Vol.2(4): 261-268 ISSN: 1793-8244
DOI: 10.7763/JACN.2014.V2.123

Improve Accuracy of Intrusion Detection System Using the Synthesis Computing Classifier Methodology

M. V. Siva Prasad and Ravi Gottipati
Abstract—Network security is the most critical part in organizations, social and enterprise systems. There are different levels in security. (The first level being preventions) The Most important level is Intrusion Detection System (IDS). IDS’s are responsible for monitoring security issues, network traffic etc., But it’s most essential task is detection of intrusions. Computing classifiers play a major role in all research areas including IDS. IDS have been achieved following different procedures and methods which have been projected in various research works. But, many of them aren't able to compute the problem with full accuracy assessment. So, main objective of my research work is to integrate numerous computing techniques into a categorized system, to detect and classify intrusions from usual activities based on the attack type in a computer network and try to improve the accuracy assessment. One of the most used mechanisms for creating rule sets is Decision-tree approach. Decision tree creations, using numerous algorithms, have been produced by different people. So far, the best algorithm is a C4.5/SEE 5.0. Using this algorithm constructs rule set 1. More than this a prominent approach to create rules other hand soft computing methods is Neural Network Theory and Fuzzy Logic. The next step, to create rule set 2, using both approaches are amalgamate and allocate. Final rule set derivation, in a process called Synthesis Computing Classifiers, can be done using Build decision boundary methodology based on the above rule set1 and set 2. The investigational result sets clearly show that the proposed new systems have achieved higher precision in identifying whether the records are normal or attack one.

Index Terms—Intrusion detection system (IDS), C4.5/SEE5.0, neural network theory, fuzzy logic, build decision boundary and synthesis computing classifiers.

M. V. Siva Prasad is with the Anurag Engineering College, Kodad 508206, India (e-mail: magantisivaprasad@gmail.com).
Ravi Gottipati is with the Tripod Technologies, Hyderabad 500082, India (e-mail: softtotime@gmail.com).

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Cite:M. V. Siva Prasad and Ravi Gottipati, "Improve Accuracy of Intrusion Detection System Using the Synthesis Computing Classifier Methodology," Journal of Advances in Computer Networks vol. 2, no. 4, pp. 261-268, 2014.

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