Weekly Research Note(2018-11-24~11-30)

2018/11/24 Research
  1. 综合Fog computing + ML + IoT attack/anomaly detection 三者的研究论文方向:
    • system architecture: 提供三者的组成关系
    • ML/DL methods: detection的方法有不同
    • study cases: 针对不同的应用或者网络环境,例如smart city, vehicle; cellular network, sensor network, low power wide area networks
    • fog computing management: how to dynamically orchestrate system components/ manage computing resources/ security problem of edge servers themselves.

Paper: Dynamic management of a deep learning‑based anomaly detection system for 5G networks This paper points out that new technologies should consider new features: efficient, automatic, seamlesss =>

  • resource management according to the number of users and their generated traffic
  • hot upgrade of detection models
  • dynamic deployment of new resources on demand
  • deployment of specific analysis tools to extract detailed information
  1. 除了KDD 99 和 NSL-KDD之外的一些anomaly detection的数据集:
    • ISSNIP dataset (Melbourne IoT data)
    • IBRL dataset
    • s12
    • Banana
  2. 一些有名的安全方面的会议:
    • Oakland,
    • USENIX Security,
    • CCS,
    • NDSS …

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