scholarly journals Intrusion Detection Method of Electric Power Information Network in Cloud Computing Environment

2021 ◽  
Vol 2113 (1) ◽  
pp. 012050
Author(s):  
Jiaqi Zhang ◽  
Guoping Feng ◽  
Dexi Zhou ◽  
Mingjiu Li
Keyword(s):  
Cloud Computing ◽  
Intrusion Detection ◽  
Electric Power ◽  
Detection Method ◽  
Power Grids ◽  
Normal Operation ◽  
Computing Environment ◽  
Bp Network ◽  
Widespread Application ◽  
Cloud Computing Environment

Abstract With the widespread application of power grid systems, the information security problems faced by power grids have become more obvious. Various internal and external intrusion attacks that occur frequently have become an important issue affecting the normal operation of power generation and operations. The purpose of this paper is to study the intrusion detection method of electric power information(PI) network in the cloud computing environment. With the help of the cloud platform’s ability to process big data, and based on the analysis of the PI network structure, a DBN optimized BP network algorithm is proposed, and the optimized BP neural network is used as a runtime classification program. Experimental results show that MR-DBN-BP has a detection rate of 96.7% for intrusion detection of PI networks, which can effectively detect intrusions and effectively protect the power dispatch system network.

scholarly journals End-to-end diagnosis of cloud systems against intermittent faults

2020 ◽  
pp. 40-40
Author(s):  
Chao Wang ◽  
Zhongchuan Fu ◽  
Yanyan Huo
Keyword(s):  
Cloud Computing ◽  
Fault Diagnosis ◽  
Computing Environment ◽  
Bp Network ◽  
Cloud Computing Environment ◽  
Intermittent Faults ◽  
Diagnosis Method ◽  
Diagnosis Accuracy ◽  
End To End ◽  
Detection Mechanisms

The diagnosis of intermittent faults is challenging because of their random manifestation due to intricate mechanisms. Conventional diagnosis methods are no longer effective for these faults, especially for hierachical environment, such as cloud computing. This paper proposes a fault diagnosis method that can effectively identify and locate intermittent faults originating from (but not limited to) processors in the cloud computing environment. The method is end-to-end in that it does not rely on artificial feature extraction for applied scenarios, making it more generalizable than conventional neural network-based methods. It can be implemented with no additional fault detection mechanisms, and is realized by software with almost zero hardware cost. The proposed method shows a higher fault diagnosis accuracy than BP network, reaching 97.98% with low latency.

scholarly journals Study of Immune-Based Intrusion Detection Technology in Virtual Machines for Cloud Computing Environment

2017 ◽  
Vol 2017 ◽  
pp. 1-15 ◽  
Author(s):  
Ruirui Zhang ◽  
Xin Xiao
Keyword(s):  
Cloud Computing ◽  
Intrusion Detection ◽  
Virtual Machine ◽  
Virtual Machines ◽  
The State ◽  
Signal Acquisition ◽  
Computing Environment ◽  
Cloud Computing Environment ◽  
Detection Model ◽  
Detection Technology

Cloud computing platforms are usually based on virtual machines as the underlying architecture; the security of virtual machine systems is the core of cloud computing security. This paper presents an immune-based intrusion detection model in virtual machines of cloud computing environment, denoted as IB-IDS, to ensure the safety of user-level applications in client virtual machines. In the model, system call sequences and their parameters of processes are used, and environment information in the client virtual machines is extracted. Then the model simulates immune responses to ensure the state of user-level programs, which can detect attacks on the dynamic runtime of applications and has high real-time performance. There are five modules in the model: antigen presenting module, signal acquisition module, immune response module, signal measurement module, and information monitoring module, which are distributed into different levels of virtual machine environment. Performance analysis and experimental results show that the model brings a small performance overhead for the virtual machine system and has a good detection performance. It is applicable to judge the state of user-level application in guest virtual machine, and it is feasible to use it to increase the user-level security in software services of cloud computing platform.

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