Machine Learning Algorithms Performance Evaluation for Intrusion Detection

The steadily growing dependency over network environment introduces risk over information flow. The continuous use of various applications makes it necessary to sustain a level of security to establish safe and secure communication amongst the organizations and other networks that is under the threat of intrusions. The detection of Intrusion is the major research problem faced in the area of information security, the objective is to scrutinize threats or intrusions to secure information in the network Intrusion detection system (IDS) is one of the key to conquer against unfamiliar intrusions where intruders continuously modify their pattern and methodologies. In this paper authors introduces Intrusion detection system (IDS) framework that is deployed over KDD Cup۹۹ dataset by using machine learning algorithms as Support Vector Machine (SVM), Naïve Bayes and Random Forest for the purpose of improving the precision, accuracy and recall value to compute the best suited algorithm.

Keywords:

Intrusion Detection System , Naïve Bayes , Random forest , Support vector machine