Leveraging Large Language Models for Dynamic Scenario Building targeting Enhanced Cyber-threat Detection and Security Training
Charalampos Marantos, Spyridon Evangelatos, Eleni Veroni, George Lalas, Konstantinos Chasapas, Ioannis T. Christou, Pantelis Z. Lappas · 2024
As cybercrime is becoming increasingly sophisticated, effective cybersecurity is crucial to safeguard digital assets and protect critical infrastructures from emerging threats. Several security applications exploit recent advances in (Big) data analysis and Artificial Intelligence (AI) to prevent and respond to malicious activities. Towards this direction, supervised and unsupervised Machine Learning (ML) methods are used to detect anomalies or reveal patterns that may indicate potential threats. However, the successful implementation of these technologies requires security practitioners to undergo specialized training to fully understand and use AI-driven tools and data analytics. On the other hand, AI models themselves are vulnerable to a variety of cyber threats, which can compromise their training data and learning processes. To ensure the safe operation of these systems, especially when deployed in adversarial environments, it is crucial to create novel AI adversarial algorithms and models that are resilient against diverse security threats. This work presents a conceptual framework based on Large Language Models (LLMs) supported by a Multi-Agent layer for training of security practitioners in various advanced technologies and enhance ML models ability to detect and respond to emerging cyber threats effectively.