AI-BASED CYBER SECURITY ALGORITHM METHOD AND PROCESS: A COMPREHENSIVE FRAMEWORK FOR ENHANCED THREAT DETECTION AND MITIGATION
Keywords:
Artificial Intelligence, Cybersecurity, Machine Learning, Threat Detection, Anomaly Detection, Deep Learning, Network Security, Intrusion Detection, Zero-day Attacks, Cyber Defense.Abstract
The rapid evolution of cyber threats in the digital landscape necessitates advanced security mechanisms that can adapt and
respond to sophisticated attack vectors. This research presents a comprehensive framework for AI-based cybersecurity
algorithms that leverage machine learning techniques for enhanced threat detection, analysis, and mitigation. The proposed
methodology integrates deep learning models with traditional security protocols to create a robust defense system capable
of identifying zero-day attacks and advanced persistent threats. Through extensive analysis of secondary data from various
cybersecurity datasets and primary data collection from enterprise environments, this study demonstrates the effectiveness
of AI-driven security algorithms in reducing false positive rates by 78% and improving threat detection accuracy to 94.6%.
The research methodology employs a hybrid approach combining supervised and unsupervised learning techniques,
including neural networks, random forests, and anomaly detection algorithms. The findings reveal that AI-based
cybersecurity systems significantly outperform traditional rule-based security systems in terms of response time, accuracy,
and adaptability to emerging threats. The implementation of this framework in real-world scenarios