NOS-Gate: Queue-Aware Streaming IDS for Consumer Gateways under Timing-Controlled Evasion
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Abstract (arXiv)
Timing and burst patterns can leak through encryption, and an adaptive adversary can exploit them. This undermines metadata-only detection in a stand-alone consumer gateway. Therefore, consumer gateways need streaming intrusion detection on encrypted traffic using metadata only, under tight CPU and latency budgets. We present a streaming IDS for stand-alone gateways that instantiates a lightweight two-state unit derived from Network-Optimised Spiking (NOS) dynamics per flow, named NOS-Gate. NOS-Gate scores fixed-length windows of metadata features and, under a K-of-M persistence rule, triggers a reversible mitigation that temporarily reduces the flow's weight under weighted fair queueing (WFQ). We evaluate NOS-Gate under timing-controlled evasion using an executable worlds benchmark that specifies benign device processes, auditable attacker budgets, contention structure, and packet-level WFQ replay to quantify queue impact. All methods are calibrated label-free via burn-in quantile thresholding. Across multiple reproducible worlds and malicious episodes, at an achieved 0.1% false-positive operating point, NOS-Gate attains 0.952 incident recall versus 0.857 for the best baseline in these runs. Under gating, it reduces p99.9 queueing delay and p99.9 collateral delay with a mean scoring cost of ≈ 2.09,μs per flow-window on CPU.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 9 of its 29 references that are in the map. First posted on arXiv 1 Jan 2026.
Builds on
9 of 29 references.
References · 9
- 2018Deep Learning in Spiking Neural Networkscited by 263
- 2018Kitsune: An Ensemble of Autoencoders for Online Network Intrusion Detectioncited by 121
- 2018D"IoT: A Federated Self-learning Anomaly Detection System for IoTcited by 89
- 2018N-BaIoT: Network-based Detection of IoT Botnet Attacks Using Deep Autoencoderscited by 67
- 2016IoT Sentinel: Automated Device-Type Identification for Security Enforcement in IoTcited by 30
- 2023Low-Quality Training Data Only? A Robust Framework for Detecting Encrypted Malicious Network Trafficcited by 12
- 2023Detecting Unknown Encrypted Malicious Traffic in Real Time via Flow Interaction Graph Analysiscited by 6
- 2025Network-Optimised Spiking Neural Network for Event-Driven Networkingcited by 3
- 2025Network-Optimised Spiking Neural Network (NOS) Scheduling for 6G O-RAN: Spectral Margin and Delay-Tail Controlcited by 2
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Citing papers per year: 2026: 1.
1 paper.