Intrusion Detection Using Rule-Based Machine Learning Algorithms

Denial of Service (DoS) attack is the main focus of many major companies. DoS can be used to occupy almost all the resources of the target machine which results in shut down of the machine or unable to process the request from a genuine user. The DoS attack is very easy to engineer. Nowadays, the DoS attacks are involving from simple to very complex and sophisticated once. This paper presents an approach to intrusion detection consists of data preprocessing, feature selection and rule-based classifiers. The feature selection uses information gain with ranker. The approach is implemented and tested with rule base classifiers on dataset of GoldenEye tool in CICIDS 2018. The analysis of rule base classifiers is done and compared with each other in terms of performance.