Anomaly Based Intrusion Detection through Efficient Machine Learning Model

Abstract

Machine learning is commonly utilised to construct an intrusion detection system (IDS) that automatically detects and classifies network intrusions and host-level threats. Malicious assaults change and occur in high numbers, needing a scalable solution. Cyber security researchers may use public malware databases for research and related work. No research has examined machine learning algorithm performance on publicly accessible datasets. Data and physical level security and analysis for Data protection have become more important as data volumes grow. IDSs collect and analyse data to identify system or network intrusions for data prevention. The amount, diversity, and speed of network data make data analysis to identify assaults challenging. IDS uses machine learning methods for precise and efficient development of data security mechanism. This work presented intrusion detection model using machine learning, which utilised feature extraction, feature selection and feature modelling for intrusion detection classifier.