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@article{199464,
        author = {Kadhiravan EG and Dharshini L and Dharsni S and Hanushree L},
        title = {CYBER DEFENSE WITH SECURE LLM},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {11},
        pages = {15120-15124},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=199464},
        abstract = {The rapid growth of digital technologies and online systems has led to an increase in cybersecurity threats, making it difficult for individuals to understand and analyze attacks using traditional learning methods. This project proposes a cybersecurity simulation system called “Cyber Defense with Secure LLM” that automates the execution and analysis of common cyber attacks using Artificial Intelligence. The system performs attacks such as reconnaissance, brute-force login, SQL injection, and data theft in a controlled environment, and processes the results to extract meaningful insights. A locally deployed Large Language Model (LLM) is used to analyze the attack behavior and generate clear explanations for better understanding.
The proposed system identifies attack patterns, detects vulnerabilities, and determines root causes such as weak authentication, insecure database queries, misconfigurations, and data exposure risks. It also presents the analysis from both red team (attack perspective) and blue team (defense perspective), providing practical knowledge of how attacks occur and how they can be prevented. In addition, the system generates simple human-readable explanations and suggests effective countermeasures, reducing the need for expert guidance and improving learning efficiency.
A user-friendly web interface is developed to display logs, attack summaries, analysis results, and recommended solutions in an easy-to-understand format. The system also includes features such as a chat assistant for clearing doubts, a history module to review past activities, and an option to download a complete report in PDF format. By integrating cybersecurity concepts with Generative AI, the project enhances practical understanding, strengthens defensive awareness, and supports modern approaches to intelligent cyber defense systems.},
        keywords = {Cybersecurity Simulation, Large Language Models (LLMs), Attack Analysis, Red Teaming, Blue Team Defense, Automation.},
        month = {April},
        }