RETRACTED ARTICLE: A study on supervised machine learning algorithm to improvise intrusion detection systems for mobile ad hoc networks

Abstract

The security inside the network correspondence is a noteworthy concern. Being the way that information is considered as the profitable asset of an association, giving security against the intruders is exceptionally fundamental. Intrusion Detection Systems tries to recognize security assaults of intruders by researching a few information records saw in forms on the network. In this paper, Intrusion Detection Classification of attacks is done by NNIDS, TSVID and DF-IDS. The proposed algorithms are trained and tested using KDD Cup 1999 dataset . This paper has presented a novel method for an adaptive fault tolerant mobile agent based intrusion detection system. At first the classification of attacks is done by TSVID Classification algorithm. The TSVID makes use of RBF kernel and iterative learning mechanism. Next the classification is done by using NNIDS that makes use of neural network based approach. Advantages of NNIDS method is that it can successfully handle both qualitative, quantitative data, and it handles multiple criteria and easier to understand. Then, finally, the classification is done by using iterative learning mechanism and DF-IDS gives successful results of classification. The performances of the proposed algorithm are evaluated using the classification metrics such as detection rate and accuracy. Comparison graphs of attack detection rate and false-alarm rate reveals that the obtained results of anticipated methods achieve greater detection rate and less computational time for the classification of attacks and protocols. The proposed study is a classification based approach for combining several networks in intrusion detection systems. In evaluation of this model, it has been demonstrated that there is a significant improvement in real time performance without sacrificing efficiency.