Research of Power Online Security Auxiliary Decision System Based on Matlab Simulation

Wang, Wenfa; Xu, Chun; Wang, Shiyao

doi:10.2174/1874444301507011834

Cited by 1 publication

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“…Software vulnerabilities, also called vulnerabilities, usually refer to the defects existing in computer system software or problems arising in the process of system use. According to the de nition of CVE, an authoritative vulnerability publishing organization, a vulnerability is a computational logic error found in software and some hardware components (for example, rmware) that, when exploited by an attacker, will adversely a ect one of the three elements of information security (con dentiality, integrity, availability) [2]. By exploiting the vulnerability, an attacker can obtain access to the system or network, thus performing malicious acts [3].…”

Section: Introductionmentioning

confidence: 99%

Internet of Things Information Network Security Situational Awareness Based on Machine Learning Algorithms

Liu

2022

Mobile Information Systems

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In order to accurately predict the security situation of Internet of Things information network, a research method based on machine learning algorithm for security situational awareness of Internet of Things information network is proposed. The perception result is represented by the perception model, the sample data are preprocessed based on the linear discriminant analysis method, the sample data are optimized to obtain the combined features, and then the processed data are used as the training input of the RBF neural network to find out the mapping relationship with the network situation value, so as to quantify the security posture of the system. The results show that automatic discovery and classification of seven violations is achieved. Since the platform was launched in China Mobile, more than 65,000 suspected illegal IoT cards have been discovered, effectively monitoring and controlling the operation and behavior of IoT cards. The processing efficiency of IoT card violations has been increased by more than 20 times. After the system is put into use, the discovery of suspected illegal users and behaviors can be realized through full automation, and the process of analysis, confirmation, and disposal can be shortened to 2 hours, which effectively reduce the false alarm rate and reduce operator costs. In previous monitoring of IoT cards, the false-positive rate of illegal IoT cards was about 83%, while the false-positive rate of existing algorithms dropped to 20%.Monitoring shows that business abuse monitoring detects the highest proportion of illegal IoT cards, 59%; infractions of Internet of Things cards detected through Internet abuse monitoring accounted for 20%; the proportion of Internet of Things card with machine card separation is 8%; in the information security risk monitoring (including spam text messages and harassing phone calls), the number of illegal Internet of Things cards found is small, only 3% and 2%, respectively; other infractions, including unauthorized use in locations and user complaints, accounted for 8%. It can effectively improve the ability to discover illegal IoT cards, greatly improve the accuracy of judgment, and improve the efficiency of disposal. The comparison verifies that the method is reliable and effective in the security situation awareness of the Internet of Things information network. Using the Internet of Things information security management system software based on the machine learning algorithm, the system software suitable for anomaly data detection is trained by adjusting the main parameters of the algorithm, which improves the automation and intelligence degree of the system software.

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Section: Introductionmentioning

confidence: 99%

Internet of Things Information Network Security Situational Awareness Based on Machine Learning Algorithms

Liu

2022

Mobile Information Systems

View full text Add to dashboard Cite

show abstract

Research of Power Online Security Auxiliary Decision System Based on Matlab Simulation

Cited by 1 publication

References 3 publications

Internet of Things Information Network Security Situational Awareness Based on Machine Learning Algorithms

Internet of Things Information Network Security Situational Awareness Based on Machine Learning Algorithms

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