Federated Learning Based Anomaly Network Intrusion Detection System Using RQA

• Author(s): Dheeraj Shetty; Gowtham H M; Hoysala K H; Dev Darshan C; Dr. Sheela Kathavate
• Paper ID: 1712702
• Page: 997-1004
• Published Date: 12-12-2025
• Published In: Iconic Research And Engineering Journals
• Publisher: IRE Journals
• e-ISSN: 2456-8880
• Volume/Issue: Volume 9 Issue 6 December-2025

Abstract

Modern cybersecurity environments face rapidly evolving threats that bypass conventional perimeter-based defenses and signature-only detection mechanisms. This paper presents a modular, multilayered Cyber Intrusion Detection System (Cyber IDS) designed to identify malicious activity through real-time packet sniffing, signature-based inspection, and anomaly detection techniques. By integrating a lightweight sniffer engine, recursive queue analysis (RQA), and dynamic rule-based signature matching, the system provides early detection of suspicious patterns, distributed attacks, and unauthorized access attempts. The methodology includes packet capture, feature extraction, behavioral scoring, and multi-stage detection logic supported by fast database lookup. Through empirical evaluation across diverse traffic profiles, the system demonstrates its capability to detect anomalies with improved precision and lower false positive rates compared to static signature-only IDS models. The proposed solution establishes a scalable foundation for continuous monitoring, adaptive threat detection, and rapid response in modern networked environments.

Keywords

Intrusion Detection, Packet Sniffing, Network Security, Anomaly Detection, Signature-based IDS, Cybersecurity Analytics

Citations

IRE Journals:
Dheeraj Shetty, Gowtham H M, Hoysala K H, Dev Darshan C, Dr. Sheela Kathavate "Federated Learning Based Anomaly Network Intrusion Detection System Using RQA" Iconic Research And Engineering Journals Volume 9 Issue 6 2025 Page 997-1004

IEEE:
Dheeraj Shetty, Gowtham H M, Hoysala K H, Dev Darshan C, Dr. Sheela Kathavate "Federated Learning Based Anomaly Network Intrusion Detection System Using RQA" Iconic Research And Engineering Journals, 9(6)