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Intrusion Detection System, high false positive rate, false negative rate, K-Mean, Adaptive-SVM, NSL-KDD
Computer security plays an important role in everybody's life. Therefore, to protect the computer and sensitive information from the untrusted parties have great significance. Intrusion detection system helps us to detect these malicious activities and sends the reports to the administration. But there is a problem of high false positive rate and low false negative rate. To eliminate these problems, hybrid system is proposed which is divided into two main parts. First, cluster the data using K-Mean algorithm and second, is to classify the train data using Adaptive-SVM algorithm. The experiments is carried out to evaluate the performance of proposed system is on NSL-KDD dataset. The results of proposed system clearly give better accuracy and low false positive rule and high false negative rate.
Jasmeen K. Chahal, Amanjot Kaur,"A Hybrid Approach based on Classification and Clustering for Intrusion Detection System", International Journal of Mathematical Sciences and Computing(IJMSC), Vol.2, No.4, pp.34-40, 2016.DOI: 10.5815/ijmsc.2016.04.04
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