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Author Meeragandhi, G. ♦ Srivatsa, S. K.
Source CiteSeerX
Content type Text
File Format PDF
Subject Domain (in DDC) Computer science, information & general works ♦ Data processing & computer science
Subject Keyword Decision Rule ♦ Classifier Decision Tree ♦ Effective Network Intrusion Detection ♦ Intrusion Detection System ♦ Intrusion Behavior ♦ Performance Element ♦ Empirical Simulation Result ♦ Security Audit Data ♦ Important Open Problem ♦ Past Year ♦ Performance Task ♦ Information Society ♦ Noticeable Performance Improvement ♦ Decision Tabel ♦ Training Data ♦ Network Attack ♦ Related Application ♦ Discipline Draw ♦ Critical Component ♦ Vibrant Property ♦ Large Volume ♦ Huge Number ♦ Computational Character ♦ Network Intrusion Detection ♦ Knowledge Discovery Database ♦ Research Community ♦ Attack Category ♦ Machine Learning ♦ Algorithm Selection Model ♦ Evaluation Result ♦ Learning Method ♦ Computer Network ♦ Classification Model
Abstract In the era of information society, computer networks and their related applications are the emerging technologies. Network Intrusion Detection aims at distinguishing the behavior of the network. As the network attacks have increased in huge numbers over the past few years, Intrusion Detection System (IDS) is increasingly becoming a critical component to secure the network. Owing to large volumes of security audit data in a network in addition to intricate and vibrant properties of intrusion behaviors, optimizing performance of IDS becomes an important open problem which receives more and more attention from the research community. In this work, the field of machine learning attempts to characterize how such changes can occur by designing, implementing, running, and analyzing algorithms that can be run on computers. The discipline draws on ideas, with the goal of understanding the computational character of learning. Learning always occurs in the context of some performance task, and that a learning method should always be coupled with a performance element that uses the knowledge acquired during learning. In this research, machine learning is being investigated as a technique for making the selection, using as training data and their outcome. In this paper, we evaluate the performance of a set of classifier algorithms of rules (JRIP, Decision Tabel, PART, and OneR) and trees (J48, RandomForest, REPTree, NBTree). Based on the evaluation results, best algorithms for each attack category is chosen and two classifier algorithm selection models are proposed. The empirical simulation result shows the comparison between the noticeable performance improvements. The classification models were trained using the data collected from Knowledge Discovery Databases (KDD) for Intrusion
Educational Role Student ♦ Teacher
Age Range above 22 year
Educational Use Research
Education Level UG and PG ♦ Career/Technical Study