A trusted feature aggregator federated learning for distributed malicious attack detection

摘要

With the rapid development of IoT technology, millions of physical devices embedded with electronics or software are put into regular production. Each IoT device is connected to the user's life and property privacy. Without a credible intrusion detection and defense mechanism installed on the device, it may be attacked by hackers, such as monitoring events of home cameras and control of smart devices. These attack events will have a serious impact on users' production and life. This paper proposes a Blockchained-Federated Learning based cloud intrusion detection scheme. The scheme sends the local training parameters of the IoT intrusion alarm set to the cloud computing center for global prediction, and stores the model training process information and behavior on the blockchain. In order to solve the high probability of false alerts affecting the accuracy of the federated learning model, the scheme proposes an alert filter identification module. At the same time, through the erasure code-based blockchain storage solution, the traditional blockchain storage performance is improved to meet the storage needs of a large number of alert training data in real scenarios.