Adaptive Cyber Threat Prediction Using Attack Graphs and Real-Time Machine Learning Integration

Registration ID: IJNRD_305348

Published ID: IJNRD2504151

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Deepak Kumar, Dr. Avinash Kumar Namdeo

Cybersecurity, Attack Graphs, Threat Intelligence, Machine Learning, Real-Time Security, Dijkstra’s Algorithm, Predictive Threat Modeling, Risk Prioritization.

Cyber threats, including zero-day exploits, APTs, and polymorphic malware, increasingly bypass traditional security measures. Static attack graphs, though useful for modeling attack paths, lack real-time adaptability and fail to integrate live threat intelligence. This research presents a real-time adaptive attack graph framework that leverages Dijkstra’s algorithm for attack path optimization, machine learning (ML) for predictive threat modeling, and live threat intelligence APIs (MITRE ATT&CK, CVE/NVD, VirusTotal) for dynamic updates. The proposed framework enhances cybersecurity by addressing key limitations: (1) improving real-time adaptability in attack graphs, (2) enhancing threat prediction accuracy, and (3) dynamically prioritizing risks. Experimental validation on real-world security logs demonstrates a 12.3% reduction in false negatives and an 11.3% increase in attack path prediction accuracy compared to traditional models. These results highlight the framework’s potential to shift cybersecurity from a reactive to a proactive stance, enabling organizations to predict, adapt, and mitigate cyber threats in real time.

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Deepak Kumar & Dr. Avinash Kumar Namdeo (April-2025). Adaptive Cyber Threat Prediction Using Attack Graphs and Real-Time Machine Learning Integration. INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 10(4), b288-b293. https://ijnrd.org/papers/IJNRD2504151.pdf

Volume 10 Issue 4, April-2025

Pages : b288-b293

Paper Reg. ID: IJNRD_305348

Published Paper Id: IJNRD2504151

Research Area: Science and Technology

Author Type: Indian Author

Country: South West, Delhi, India

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