AI-Enhanced Network Intrusion Detection Using Python and Deep Packet Inspection
DOI:
https://doi.org/10.21590/ijtmh.2024100303Keywords:
Network Intrusion Detection, Deep Packet Inspection, NSL-KDD, Scapy, Python, CNN, False Positives, Network Security, AI-Based IDS, Packet ClassificationAbstract
Network security requires constant innovation to combat evolving threats. This research develops an AI-enhanced network intrusion detection system (NIDS) using Python and Deep Packet Inspection (DPI). Raw packet data was parsed using Scapy and classified using a convolutional neural network (CNN) model trained on the NSL-KDD dataset. The solution was integrated within a university network and evaluated for detection accuracy, precision, recall, and false positive rate. Results show a 94% detection accuracy and a 12% reduction in false positives compared to traditional rule-based systems. This paper further discusses implementation techniques, dataset preprocessing strategies, deployment constraints, and comparative analysis across detection approaches. Key contributions include practical pipeline integration, performance validation under real traffic conditions, and a roadmap for scalable NIDS using deep learning.
