Cyber intrusion detection using machine learning classification techniques

As the alarming growth of connectivity of computers and the significant number of computer-related applications increase in recent years, the challenge of fulfilling cyber-security is increasing consistently. It also needs a proper protection system for numerous cyberattacks. Thus, detecting inconsistency and attacks in a computer network and developing intrusion detection system (IDS) that performs a potential role for cyber-security. Artificial intelligence, particularly machine learning techniques, has been used to develop a useful data-driven intrusion detection system. In this paper, we employ various popular machine learning classification algorithms, namely Bayesian Network, Naive Bayes classifier, Decision Tree, Random Decision Forest, Random Tree, Decision Table, and Artificial Neural Network, to detect intrusions due to provide intelligent services in the domain of cyber-security. Finally, we test the effectiveness of various experiments on cyber-security datasets having several categories of cyber-attacks and evaluate the effectiveness of the performance metrics, precision, recall, f1-score, and accuracy.