Investigation of Machine Learning Algorithms for Intrusion Detection System in Cybersecurity

The proliferation in usage and complexity of modern communication and network systems, a large number of trustworthy online Services and systems have been deployed. Even so, cybersecurity threats are still growing. An Intrusion Detection System (IDS) play a vital role in ensuring the security of communication networks, and it is taken into account as the subsequent security gate after the firewall. The IDS informs the system or network administrator in order to take specific actions to evade the suspicious activities. Three significant contributions are made during the course of this research to illustrate the feasibility of these IDS approaches. In the first contribution, we investigate the effectiveness of using conventional machine learning techniques based intrusion detection systems. The second contribution proposes an ensemble learning algorithm for cybersecurity threat detection. The third contribution proposes a hybrid feature selection approach for improving network attack detection. All presented algorithms were evaluated on the recent public CICIDS2017 dataset, which consist of benign and the most cutting-edge common attacks, and compared with other approaches. This research considers several machine learning classifiers, and feature selection techniques in order to study their classification performance under attack over different metrics. The empirical results of the three implemented systems conclude that the chosen minimized features provide promising performance to develop IDS that is effective and efficient for network intrusion detection. Moreover, these models not only improves the classification accuracy but also reduces the false alarm rate in the classification of IDS attacks.