Cybersecurity Threat Intelligence Automated via Machine Learning: A System for Analyzing and Responding to Data in Real-Time
Shanmugam Muthu, Perumalsamy Deepalakshmi · 2025
Businesses are relentlessly bombarded with advanced and ever-evolving attacks in today's rapidly evolving cyber threat landscape. Predefined signatures and rule-based methods to traditional security approaches cannot keep pace with threats of today's magnitude and diversity. The focus of this study is an ML-based real-time threat assessment and response cybersecurity threat intelligence solution. Advanced machine learning algorithms, anomaly detection, and data mining are employed to analyze vast amounts of security information, identify new threats, and respond with instantaneous, effective defense. The system detects intrusion attempts, data exfiltration, and ransomware attacks by analyzing diverse streams of data from security appliances like network logs, endpoint behavior, and traffic patterns. Supervised and unsupervised learning discovers known attack patterns, and anomaly detection discovers new threats. The system learns from new data to detect new attack pathways without rule updates. An automatic response method can isolate compromised devices, ban malicious IP addresses, and warn security personnel in real time, decreasing reaction time and human interaction. Threat intelligence feeds add external context to the system's ability to identify advanced persistent threats (APTs) and other high-level cyberattacks, according to the research. Integration with threat-sharing systems keeps the system updated on threat intelligence, ensuring defensive measures are effective against developing TTPs. The suggested ML-based threat intelligence system speeds up and improves cyber threat detection and response, improving security and allowing organisations to scale their defences in a more complex cyber environment. This paper analyses the system's architecture, performance, and potential for future improvements, such as deep learning models, federated learning for privacy-preserving intelligence sharing, and complex decision-making automation. Machine learning and automation present a viable path towards flexible, scalable, and efficient cybersecurity threat intelligence systems.