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
Detection. The trained models were then used for predicting the risk of the attacks in a web server environment or by any
network administrator or any Security Experts. The Prediction Accuracy of the Classifiers was evaluated using 10-fold Cross
Validation and the results have been compared to obtain the accuracy.
Keywords: - Classifier, Data mining, Decision Trees, Decision rules, Intrusion detection, KDD dataset, Machine learning,
Network security