MULTI-LF: A Unified Continuous Learning Framework for Real-Time DDoS Detection in Multi-Environment Networks

• URL: http://arxiv.org/abs/2504.11575v1
• Date: Tue, 15 Apr 2025 19:44:53 GMT
• Title: MULTI-LF: A Unified Continuous Learning Framework for Real-Time DDoS Detection in Multi-Environment Networks
• Authors: Furqan Rustam, Islam Obaidat, Anca Delia Jurcut,
• Abstract summary: Existing AI-based detection systems struggle to adapt to new attack strategies and lack real-time attack detection capabilities.<n>This study proposes an online, continuous learning methodology for DDoS detection in M-En networks.
• Score: 1.5922526181364094
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Detecting Distributed Denial of Service (DDoS) attacks in Multi-Environment (M-En) networks presents significant challenges due to diverse malicious traffic patterns and the evolving nature of cyber threats. Existing AI-based detection systems struggle to adapt to new attack strategies and lack real-time attack detection capabilities with high accuracy and efficiency. This study proposes an online, continuous learning methodology for DDoS detection in M-En networks, enabling continuous model updates and real-time adaptation to emerging threats, including zero-day attacks. First, we develop a unique M-En network dataset by setting up a realistic, real-time simulation using the NS-3 tool, incorporating both victim and bot devices. DDoS attacks with varying packet sizes are simulated using the DDoSim application across IoT and traditional IP-based environments under M-En network criteria. Our approach employs a multi-level framework (MULTI-LF) featuring two machine learning models: a lightweight Model 1 (M1) trained on a selective, critical packet dataset for fast and efficient initial detection, and a more complex, highly accurate Model 2 (M2) trained on extensive data. When M1 exhibits low confidence in its predictions, the decision is escalated to M2 for verification and potential fine-tuning of M1 using insights from M2. If both models demonstrate low confidence, the system flags the incident for human intervention, facilitating model updates with human-verified categories to enhance adaptability to unseen attack patterns. We validate the MULTI-LF through real-world simulations, demonstrating superior classification accuracy of 0.999 and low prediction latency of 0.866 seconds compared to established baselines. Furthermore, we evaluate performance in terms of memory usage (3.632 MB) and CPU utilization (10.05%) in real-time scenarios.

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