The Abnormal Detection for Network Traffic of Power IoT Based on Device Portrait

• Main Authors: Jiaxuan Fei, Qigui Yao, Mingliang Chen, Xiangqun Wang, Jie Fan
• Format: Article
• Language: English
• Published: Hindawi Limited 2020-01-01
• Series: Scientific Programming
• Online Access: http://dx.doi.org/10.1155/2020/8872482
• ISSN: 1058-9244; 1875-919X

The construction of power Internet of things is an important development direction for power grid enterprises. Although power Internet of things is a kind of network, it is denser than the ordinary Internet of things points and more complex equipment types, so it has higher requirements for network security protection. At the same time, due to the special information perception and transmission mode in the Internet of things, the information transmitted in the network is easy to be stolen and resold, and traditional security measures can no longer meet the security protection requirements of the new Internet of things devices. To solve the privacy leakage and security attack caused by the illegal intrusion in the network, this paper proposes to construct a device portrait for terminal devices in the power Internet of things and detect abnormal traffic in the network based on device portrait. By collecting traffic data in the network environment, various network traffic characteristics are extracted, and abnormal traffic is analyzed and identified by the machine learning algorithm. By collecting the traffic data in the network environment, the features are extracted from the physical layer, network layer, and application layer of the message, and the device portrait is generated by a machine learning algorithm. According to the established attack mode, the corresponding traffic characteristics are analyzed, and the detection of abnormal traffic is achieved by comparing the attack traffic characteristics with the device portrait. The experimental results show that the accuracy of this method is more than 90%.