An intelligent cyber threat detection: A swarm-optimized machine learning approach

Cyber threats are an ongoing problem that is hard to prevent completely. This can occur for various reasons, but the main causes are the evolving techniques of hackers and the neglect of security measures when developing software or hardware. As a result, several countermeasures will need to be applied to mitigate these threats. Cyber-threat detection techniques can fulfill this role by utilizing different identification methods for various cyber threats. In this work, an intelligent cyber threat detection system employing a swarm-based machine learning approach is proposed. The approach involves using Harris Hawks Optimization (HHO) to enhance the Support Vector Machine (SVM) for improved threat detection through parameter tuning and feature weighting. Furthermore, various cyber-threat types have been considered, including Fake News, IoT Intrusion, Malicious URLs, Spam Emails, and Spam Websites. The proposed HHO-SVM has been compared to other approaches for detecting all these types collectively. The HHO-SVM outperforms all algorithms in most types (datasets). The proposed approach demonstrated the highest accuracy across seven datasets: FakeNews-1, FakeNews-2, FakeNews-3, IoT-ID, URL, SpamEmail-2, and SpamWebsites, achieving average accuracy of 68.251%, 68.729%, 79.049%, 95.254%, 100%, 96.681%, and 93.975%, respectively. Additionally, a thorough analysis of each cyber-threat type has been conducted to understand their characteristics and detection strategies.