Multi-Source Cyber Intrusion Detection Using Ensemble Machine Learning

• DOI: 10.3844/jcssp.2025.111.123
• Publication Date: 2025-01-02T09:26:30Z
• Authors: Taskeen Ali Khan; Sara Abbas; Biswaranjan Senapati; Manish Raj Anand; Muhammad Imran Gh...; Satyabrata Pradhan; Friban Almeida
• Keywords: Ensemble Learning

The increased usage of digital technologies across businesses has led to an increase in cybercrime. It is difficult for existing intrusion detection systems detect highly skilled hacking attempts; however, machine learning been suggested as a way around these drawbacks. purpose this study evaluate how well various algorithms identify and stop cyberattacks diverse network, system, application environments. goal the provide designers with thorough grasp benefits drawbacks using cyber detection. will also assist creating more reliable effective infrastructure. Metrics like accuracy, precision, recall, F1-score be used research assess models' performance. better safeguard enterprises' networks, systems, applications from by offering precise solutions. objective determine future areas learning-based cyberattack methods. People all over globe can now connect thanks cloud computing Internet Things computer security professionals utilize standard operating procedures proprietary software guarantee that evidence admissible court. In forensics, project revolutionary approach protecting data integrity identifying threats. best accuracy (97%), hazards, achieved Hybrid KNN-XGB, KNN-CBC, KNN-LGBM, KNN-HGBC KNN-GBC Boosted algorithms.