LNN Algorithm for IOT Intrusion Detection

The growing use of Internet of Things (IoT) devices has expanded the cyber-attack surface, creating new challenges for intrusion detection. Traditional signature-based systems often fail against zero-day attacks and evolving threats. This project proposes a proactive cybersecurity model integrating Logical Neural Networks (LNN) with advanced preprocessing and real-time monitoring. By combining white box testing, enhanced threat hunting, and robust incident response, the system strengthens detection accuracy and resilience. The prototype framework captures network traffic, extracts critical features, and applies intelligent anomaly detection, achieving high precision with reduced false positives. Results demonstrate improved adaptability, scalability, and effectiveness for modern IoT environments.