AI-Powered Intrusion Detection System

Abstract

With the rapid expansion of computer networks and internet-based services, ensuring network security has become a critical challenge. Commonly adopted network security measures, such as firewalls and signature-oriented intrusion detection systems, play a key role in defending against known threats, making them ineffective against evolving and previously unseen attacks. This paper presents an AI-powered Network Intrusion Detection System designed to enhance real-time threat detection in modern network environments. The proposed system integrates live packet monitoring using Scapy, detection of SYN flood attacks, and anomaly detection through the Kitsune ensemble autoencoder framework. The system is evaluated in a simulated environment using SYN flood and ICMP flood attacks. Experimental observations demonstrate improved detection accuracy, reduced false alerts, and efficient real-time performance. The results indicate that the proposed approach provides a practical and adaptive solution for securing contemporary networks.

Introduction

The text presents an AI-based Network Intrusion Detection System (NIDS) designed to address the growing complexity and security vulnerabilities of modern network infrastructures. Traditional security mechanisms such as firewalls and signature-based intrusion detection systems are limited in detecting hidden, novel, and zero-day attacks, while conventional anomaly-based systems often generate high false-positive rates. To overcome these issues, the proposed system integrates artificial intelligence and machine learning for adaptive and real-time threat detection.

The system captures live network traffic using the Scapy library and extracts traffic features through the Afterimage framework, which analyzes statistical patterns over multiple time windows. Known attacks such as SYN flood and ICMP flood are detected using threshold-based and behavioral analysis techniques. For unknown and anomalous threats, the Kitsune ensemble autoencoder framework is employed, enabling unsupervised anomaly detection by learning normal network behavior and identifying deviations through reconstruction errors.

Implemented in a Linux-based environment using Python, the system was evaluated under simulated attack scenarios. Experimental results show high detection accuracy for SYN flood and ICMP flood attacks while maintaining low false-positive rates and efficient real-time performance. Although enabling the NIDS introduces slight throughput overhead and increased delay during attacks, overall network performance remains within acceptable limits.

The results demonstrate that combining rule-based detection with ensemble autoencoder–based anomaly detection significantly improves detection reliability, adaptability, and efficiency. The proposed AI-based intrusion detection system effectively identifies both known and unknown cyber threats, making it suitable for practical deployment in dynamic network environments.

Conclusion

This paper presents an AI-powered network intrusion detection system that enhances security through intelligent anomaly detection and real-time traffic monitoring. By integrating SYN flood detection with ensemble autoencoder-based learning and ICMP flood analysis, the proposed system effectively addresses the limitations of traditional intrusion detection methods. The experimental results demonstrate improved detection accuracy, reduced false alerts, and efficient real-time performance. The proposed approach offers a scalable and practical solution for securing modern network infrastructures.

References

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Copyright

Copyright © 2025 Dr. Radha B. K., Rahul , Revanasiddappa , Rohit . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.