SAGE:
 S
 tealthy
 A
 ttack
 Ge
 neration in cyber-physical systems

Biehler, Michael; Zhang, Zhong; Shi, Jianjun

doi:10.1080/24725854.2023.2184004

Cited by 4 publications

(1 citation statement)

References 40 publications

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“…It involves the use of information and communication technology and other solutions in fault and intrusion detection, and mere monitoring of energy generation, transmission, and distribution. Biehler et al (2024) aim to present a comprehensive review of the next smart grid research trends and technological background and discuss a futuristic next-generation smart grid driven by AI and leveraged by IoT and 5G.…”

Section: Literature Reviewmentioning

confidence: 99%

Network Information Security Monitoring Under Artificial Intelligence Environment

Fu,

Liu,

Zhang

et al. 2024

International Journal of Information Security and Privacy

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At present, network attack means emerge in endlessly. The detection technology of network attack must be constantly updated and developed. Based on this, the two stages of network attack detection (feature selection and traffic classification) are discussed. The improved bat algorithm (O-BA) and the improved random forest algorithm (O-RF) are proposed for optimization. Moreover, the NIS system is designed based on the Agent concept. Finally, the simulation experiment is carried out on the real data platform. The results showed that the detection precision, accuracy, recall, and F1 score of O-BA are significantly higher than those of references [17], [18], [19], and [20], while the false positive rate is the opposite (P < 0.05). The detection precision, accuracy, recall, and F1 score of O-RF algorithm are significantly higher than those of Apriori, ID3, SVM, NSA, and O-RF algorithm, while the false positive rate is significantly lower than that of Apriori, ID3, SVM, NSA, and O-RF algorithm (P < 0.05).

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Section: Literature Reviewmentioning

confidence: 99%