Real-Time Adaptive Framework for Behavioural Malware Detection in Evolving Threat Environments

Abstract

This research presents a novel real-time malware detection and mitigation system that employs behavioral analysis integrated with machine learning algorithms to combat sophisticated and previously unknown malware threats. Traditional signature- based detection methods demonstrate significant limitations in identifying zero-day attacks and advanced persistent threats that leverage polymorphic and metamorphic techniques. To address these challenges, this study develops a comprehensive system that continuously monitors system behavior patterns, analyzing deviations from established baselines to identify malicious activities in real-time.The proposed methodology implements a multi-layered approach combining dynamic behavioral monitoring with supervised and unsupervised machine learning models to establish normal system behavior profiles and detect anomalous patterns indicative of malware infiltration. Unlike conventional static analysis techniques, this behavioral-centric approach captures runtime characteristics including system call sequences, network communication patterns, file system modifications, and process execution behaviors. The system incorporates adaptive learning mechanisms that continuously refine detection models based on emerging threat patterns, thereby improving accuracy and reducing false positive rates over time. Comprehensive experimental validation across enterprise, personal computing, and critical infrastructure environments demonstrates the system's effectiveness in detecting and mitigating diverse malware variants, including advanced persistent threats, rootkits, ransomware, and fileless malware. Performance evaluation reveals significant improvements in detection speed, accuracy rates exceeding traditional signature-based methods, and robust mitigation capabilities that automatically trigger containment protocols upon threat identification. The results indicate that behavioral analysis coupled with machine learning provides a scalable, adaptive solution for modern cybersecurity challenges in increasingly complex digital ecosystems.This innovative approach represents a paradigm shift from reactive to proactive malware defense, offering enhanced protection against the evolving threat landscape while maintaining system performance and operational efficiency in diverse computing environments.