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 …