Dependable federated learning for IoT intrusion detection against poisoning attacks

This publication has 31 references indexed in Scilit:

• SHARKS: Smart Hacking Approaches for RisK Scanning in Internet-of-Things and Cyber-Physical Systems based on Machine learning
  IEEE Transactions on Emerging Topics in Computing, 2021
• Internet of Things Intrusion Detection: Centralized, On-Device, or Federated Learning?
  IEEE Network, 2020
• DeepFed: Federated Deep Learning for Intrusion Detection in Industrial Cyber–Physical Systems
  IEEE Transactions on Industrial Informatics, 2020
• PoisonGAN: Generative Poisoning Attacks Against Federated Learning in Edge Computing Systems
  IEEE Internet of Things Journal, 2020
• CorrAUC: A Malicious Bot-IoT Traffic Detection Method in IoT Network Using Machine-Learning Techniques
  IEEE Internet of Things Journal, 2020
• Selection of effective machine learning algorithm and Bot-IoT attacks traffic identification for internet of things in smart city
  Future Generation Computer Systems, 2020
• Reliable Federated Learning for Mobile Networks
  IEEE Wireless Communications, 2020
• Intelligent approach to build a Deep Neural Network based IDS for cloud environment using combination of machine learning algorithms
  Computers & Security, 2019
• Demystifying IoT Security: An Exhaustive Survey on IoT Vulnerabilities and a First Empirical Look on Internet-Scale IoT Exploitations
  IEEE Communications Surveys & Tutorials, 2019
• Internet of Things: A survey on machine learning-based intrusion detection approaches
  Computer Networks, 2019