Study on Decision Tree and KNN Algorithm for Intrusion Detection System
Ashwini Pathak, Sakshi Pathak, SGSITS, Indore
Abstract
With the increase in use of network technology and internet in today's world, cyber attacks and corruption of network protocols have become an inevitable part of the system. In order to tackle this, an efficient Intrusion Detection System (IDS) is required. IDS is a system that detects malicious activity by monitoring a system or a network. This paper focuses on implementing machine learning techniques -Decision Tree and KNN on IDS and evaluates the performance of both the techniques based on their accuracy. The performance is calculated after applying the Univariate feature selection technique with ANOVA(Analysis of Variance) and algorithms are executed on the NSL-KDD dataset. The performance of algorithms is calculated by metrics like accuracy, recall, precision and F-score. The two algorithms are compared on the basis of these performance metrics.