SentinelNet

This project introduces an advanced AI-powered solution designed to revolutionize router monitoring and cybersecurity. At its core, the system leverages the LLaMa 3:1B language model, a cutting-edge tool for real-time analysis of network traffic data. By utilizing machine learning, the system can automatically and accurately identify a wide range of network anomalies, including attacks, malfunctions, and irregular traffic patterns. This proactive detection ensures that security teams are alerted immediately to potential threats, reducing response times and improving overall system resilience. Key Features: AI-Driven Network Traffic Analysis: The LLaMa 3:1B language model is uniquely capable of understanding and analyzing complex network data. It processes vast amounts of traffic information to identify potential issues that may not be detectable through traditional monitoring methods. This allows for the detection of subtle patterns, emerging threats, and irregular activities in real-time. Automated Anomaly Detection: The system is built to recognize anomalies in network traffic without requiring constant manual oversight. It can detect a range of issues, from DDoS attacks to misconfigurations, ensuring that network administrators are immediately informed of anything out of the ordinary. This feature significantly reduces the chances of security breaches and network downtime.