Network Anomaly Detection by Means of Machine Learning: Random Forest Approach with Apache Spark

Abstract

Nowadays the network security is a crucial issue and traditional intrusion detection systems are not a sufficient way. Hence the intelligent detection systems should have a major role in network security by taking into consideration to process the network big data and predict the anomalies behavior as fast as possible. In this paper, we implemented a well-known supervised algorithm Random Forest Classifier with Apache Spark on NSL-KDD dataset provided by the University of New Brunswick with the accuracy of 78.69% and 35.2% false negative ratio. Empirical results show this approach is well in order to use for intrusion detection system as well as we seeking the best number of trees to be used on Random Forest Classifier for getting higher accuracy and lower cost for the intrusion detection system.

Keywords

• Random Forest
• Network Security
• Anomaly Detection
• NSL-KDD
• Apache Spark
• Machine Learning
• Intrusion Detection Systems (IDS)