Intrusion detection mechanism with machine learning process A case study with FMIFSSVM, FLCFSSVM, misuses SVM, anomaly SVM and Bayesian methods

  • Authors

    • K V S S R Murthy
    • K V V Satyanarayana
    2018-03-18
    https://doi.org/10.14419/ijet.v7i2.7.10597
  • IDS, FMIFSSVM, FLCFSSVM, Misuse SVM, Percentage of Successful Prediction (PSP).

  • Abstract

    Today, there is a far reaching of Internet benefits everywhere throughout the world, numerous sorts and vast number of security dangers are expanding. Since it isn't in fact possible to assemble a framework without any vulnerability, Intrusion Detection System (IDS), which can successfully distinguish Intrusion, gets to have pulled in consideration. Intrusion detection can be characterized as the way toward distinguishing irregular, unauthorized or unapproved action that objective is to target a system and its assets. IDS plays a very important role for analyzing the network passage, also it assumes a key part to analyze the system activity log and each log is portrayed by huge arrangement of highlights and it requires tremendous computational preparing force and time for the characterization procedure. This work proposes filter based feature selection methods to predict intrusion with Feature based Mutual Information Feature Selection Support Vector Machine (FMIFSSVM), Feature based Liner Correlation Feature Selection Support Vector Machine (FLCFSSVM), misuses SVM, anomaly SVM and Bayesian methods. The performances of these methods are considered by using the intrusion detection calculation data set called Knowledge Discovery in Databases (KDD) cup 99. Detection Rate (DR), False Alarm Rate (FAR) and Percentage of Successful Prediction (PSP) are the major performance measures studied in this work.

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  • How to Cite

    V S S R Murthy, K., & V V Satyanarayana, K. (2018). Intrusion detection mechanism with machine learning process A case study with FMIFSSVM, FLCFSSVM, misuses SVM, anomaly SVM and Bayesian methods. International Journal of Engineering & Technology, 7(2.7), 277-283. https://doi.org/10.14419/ijet.v7i2.7.10597

    Received date: 2018-03-25

    Accepted date: 2018-03-25

    Published date: 2018-03-18