Power Intelligent Terminal Intrusion Detection Based on Deep Learning and Cloud Computing

Author:

Li Tong12,Zhao Hai1,Tao Yaodong3ORCID,Huang Donghua3,Yang Chao4,Xu Shuheng3

Affiliation:

1. College of Computer Science and Engineering, Northeastern University, Shenyang 110169, China

2. Liaoning Electric Power Research Institute of State Grid Corporation of China, Liaoning 110055, China

3. Beijing DualPi Intelligent Security Technology Co. Ltd., Beijing 100088, China

4. State Grid Liaoning Electric Power Co., Ltd., Liaoning 110004, China

Abstract

Numerous internal and external intrusion attacks have appeared one after another, which has become a major problem affecting the normal operation of the power system. The power system is the infrastructure of the national economy, ensuring that the information security of its network not only is an aspect of computer information security but also must consider high-standard security requirements. This paper analyzes the intrusion threat brought by the power information network and conducts in-depth research and investigation combined with the intrusion detection technology of the power information network. It analyzes the structure of the power knowledge network and cloud computing through deep learning-based methods and provides a network interference detection model. The model combines the methods of abuse detection and anomaly detection, which solves the problem that the abuse analysis model does not detect new attack variants. At the same time, for big data network data retrieval, it retrieves and analyzes data flow quickly and accurately with the help of deep learning of data components. It uses a fuzzy integral method to optimize the accuracy of power information network intrusion prediction, and the accuracy reaches 98.11%, with an increase of 0.6%.

Funder

State Grid Corporation of China

Publisher

Hindawi Limited

Subject

General Mathematics,General Medicine,General Neuroscience,General Computer Science

Cited by 1 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Enhancing Cloud Security with Deep Learning-Based Intrusion Detection in Cloud Computing Environments;2024 2nd International Conference on Advancement in Computation & Computer Technologies (InCACCT);2024-05-02

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