Intelligent Cyber Security Model for Intrusion Detection using Federated Machine Learning

Cyber attacks are constantly evolving, rendering conventional intrusion detec- tion systems ineffective against sophisticated adversarial threats. This paper pro- poses an Intelligent Cyber Security Model that utilizes Federated Machine Learning (FML) to enhance intrusion detection and incident response in dis- tributed environments. The proposed model evaluates the security of different machine learning algorithms against adversarial attacks, refines the feature extrac- tion process for malware classification, and implements a mechanism to differentiate between original and maliciously tampered data. Furthermore, the study explores the integration of FML to enable proactive incident handling while preserving data privacy across distributed nodes. Experimental analysis conducted on benchmark cybersecurity datasets demonstrates that the proposed model significantly improves intrusion detection accuracy and enhances resilience against adversarial attacks. The findings suggest that FML has the potential to revolutionize cybersecurity defenses without compromising sensitive information.