mlsanityData to 14 Oct 2026

NOS-Gate: Queue-Aware Streaming IDS for Consumer Gateways under Timing-Controlled Evasion

arXiv 2601.00389 ↗First posted 1 Jan 2026cs.CR cs.LG cs.NIcited by 1

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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

  1. 2018Deep Learning in Spiking Neural Networkscited by 263
  2. 2018Kitsune: An Ensemble of Autoencoders for Online Network Intrusion Detectioncited by 121
  3. 2018D"IoT: A Federated Self-learning Anomaly Detection System for IoTcited by 89
  4. 2018N-BaIoT: Network-based Detection of IoT Botnet Attacks Using Deep Autoencoderscited by 67
  5. 2016IoT Sentinel: Automated Device-Type Identification for Security Enforcement in IoTcited by 30
  6. 2023Low-Quality Training Data Only? A Robust Framework for Detecting Encrypted Malicious Network Trafficcited by 12
  7. 2023Detecting Unknown Encrypted Malicious Traffic in Real Time via Flow Interaction Graph Analysiscited by 6
  8. 2025Network-Optimised Spiking Neural Network for Event-Driven Networkingcited by 3
  9. 2025Network-Optimised Spiking Neural Network (NOS) Scheduling for 6G O-RAN: Spectral Margin and Delay-Tail Controlcited by 2

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