A Lightweight Hybrid IDS for Fog-Enabled IoT: Integrating Deep Sequence Models with Rule-Based Filtering

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@article{1722544,
      author = {Sandra Okoro, Daniel Kim, Aarav Sethi},
      title = {A Lightweight Hybrid IDS for Fog-Enabled IoT: Integrating Deep Sequence Models with Rule-Based Filtering},
      journal = {Iconic Research And Engineering Journals},
      year = {2024},
      volume = {8},
      number = {5},
      pages = {1620-1625},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1722544.pdf},
      abstract = {This paper describes a lightweight hybrid IDS for fog-enabled IoT that places a fast rule-based pre-filter at the edge and a BiLSTM-CNN sequence model at the fog layer. The pre-filter discards obvious benign and clearly malicious flows so the deeper model only processes uncertain traffic, cutting both latency and energy. On UNSW-NB15, NSL-KDD, and TON-IoT the design achieves up to 98.6% F1 while keeping per-inference energy below 38 mJ.},
      month = {November},
      doi = {https://doi.org/10.64388/IREV8I5-1722544}
  }