AI-Based Intrusion Detection System

In conclusion, this paper presents an AI-Based Intrusion Detection System that enhances network security by combining machine learning techniques with anomaly detection mechanism. The proposed approach effectively overcomes the limitations of traditional intrusion detection systems by improving detection accuracy, reducing false positives, and ensuring faster response times. The integration of multiple components within a unified and modular architecture enables the system to operate efficiently while maintaining adaptability in dynamic network environments. The introduction of the Detection Efficiency Score (DES) provides a comprehensive evaluation framework that considers accuracy, false alarm rate, and response time together, offering a more realistic assessment of system performance. Experimental results demonstrate that the proposed model performs significantly better than conventional methods in detecting various types of cyber-attacks, including unknown and zero-day threats. Furthermore, the modular design of the system allows easy integration of new algorithms, datasets, and detection strategies, making it scalable and future ready. By focusing on behavioural analysis rather than relying solely on predefined signatures, the system can identify evolving attack patterns with greater reliability. Overall, this study highlights the growing importance of artificial intelligence in cybersecurity and provides a strong foundation for developing intelligent, adaptive, and efficient intrusion detection systems in the future

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