ML Defender (aRGus NDR): An Open-Source Embedded ML NIDS for Botnet and Anomalous Traffic Detection in Resource-Constrained Organizations

• URL: http://arxiv.org/abs/2604.04952v1
• Date: Fri, 03 Apr 2026 05:20:13 GMT
• Title: ML Defender (aRGus NDR): An Open-Source Embedded ML NIDS for Botnet and Anomalous Traffic Detection in Resource-Constrained Organizations
• Abstract summary: We present an open-source network intrusion detection system built in C++20, deployable on commodity hardware at 150-200 USD.<n> ML Defender implements a six-component pipeline over eBPF/XDP packet capture, ZeroMQ transport, and Protocol Buffers serialization.<n>The Ransomware Threat Wins policy selects the maximum arithmetic of both scores, using ML inference to suppress false positives.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Ransomware and DDoS attacks disproportionately impact hospitals, schools, and small organizations that cannot afford enterprise security solutions. We present ML Defender (aRGus NDR), an open-source network intrusion detection system built in C++20, deployable on commodity hardware at approximately 150-200 USD. ML Defender implements a six-component pipeline over eBPF/XDP packet capture, ZeroMQ transport, and Protocol Buffers serialization, combining a rule-based Fast Detector with an embedded Random Forest classifier. The Maximum Threat Wins policy selects the arithmetic maximum of both scores, using ML inference to suppress false positives. Evaluated against the CTU-13 Neris botnet dataset: F1=0.9985, Precision=0.9969, Recall=1.0000, FPR=0.0002% (2 FP in 12,075 benign flows). The Fast Detector alone produces 6.61% FPR on benign traffic; the ML layer reduces this to zero -- a ~500-fold reduction. Per-class inference latency: 0.24-1.06 microseconds on commodity hardware. Under progressive load testing, the pipeline sustains ~34-38 Mbps with zero packet drops across 2.37 million packets. RAM stable at ~1.28 GB. The bottleneck is VirtualBox NIC emulation, not pipeline logic. All figures are conservative lower bounds; bare-metal characterization is future work. This work was developed through the Consejo de Sabios, a structured multi-LLM peer review methodology. Test-Driven Hardening (TDH) is proposed as a methodology for security-critical distributed systems. ML Defender is released under the MIT license.

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