AI Powered Network Intrusion Detection System (N-IDS) FUSION SHIELD

Computer Engineering, Cyber Security

The fast-changing nature of cyber threats requires new ways to protect networks. This project focuses on creating an AI-powered Network Intrusion Detection System (NIDS) to improve traditional intrusion detection systems. By using machine learning (ML) and natural language processing (NLP), our NIDS aims to spot and handle potential intrusions in real-time. Our NIDS works by collecting and studying network logs from endpoint devices using a special log forwarder agent. These logs go to a centralized Security Information and Event Management (SIEM) server for detailed analysis with ML algorithms. By integrating with the OpenAI GPT-3 API, our system can offer detailed insights and useful information about detected intrusions. Key features of our NIDS include spotting anomalies in real-time, monitoring endpoints actively using custom Python scripts, and using AI to analyze and create detailed intrusion alerts. This report explains the design, implementation, challenges faced, and results achieved while making our AI-powered NIDS, showing how AI can change network security. Our project helps improve cybersecurity by showing how AI can adaptively detect and respond to threats. The results emphasize the importance of using AI in intrusion detection systems to fight ever-changing cyber threats effectively.

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