Adversarial Vulnerabilities in Large Language Models

Objectives to cover:

• Introduction: Overview of cybersecurity threats and the need for intelligent intrusion detection.

• Background and Motivation: Importance of AI and LLMs in enhancing security systems.

• Literature Review: Summary of existing IDS methods and AI-based approaches.

• Intrusion Detection Systems (IDS): Key concepts, types, and functions of IDS.

• AI in Cybersecurity: Role of machine learning and LLMs in threat detection.

• Proposed Framework Architecture: Design of the LLM-powered detection and response system.

• Autonomous Incident Response: Mechanisms for automated threat mitigation and response.

• Implementation and Evaluation: Dataset, experiments, simulation results, and performance metrics.

• Conclusion: Summary of contributions, findings, and future directions.

Reference:  IEEE Style