Anomaly Intrusion Detection Systems in IoT Using Deep Learning Techniques: A Survey

Alsoufi, Muaadh A.; Razak, Shukor Abd; Siraj, Maheyzah Md; Ali, Abdulalem; Nasser, Maged; Abdo, Salah

doi:10.1007/978-3-030-70713-2_60

Cited by 11 publications

(1 citation statement)

References 40 publications

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“…In reference [5], from the perspective of the unavailability of some network services under intrusion attacks, impact factors are selected to evaluate the network tolerance to the intrusion of key power systems. Literature [6] used the basic principle of deep learning to model and evaluate the tolerance of network intrusion detection system. In addition, by using KNN, random forest, support vector machine, deep belief network and other classifiers, the evaluation effect of intrusion tolerance can also be improved by identifying intrusion features.…”

Section: Introductionmentioning

confidence: 99%

An intelligent assessment method of power information intrusion tolerance based on machine learning

Huang

Guo²,

Song³

et al. 2022

Third International Conference on Computer Communication and Network Security (CCNS 2022)

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An intelligent evaluation method of power information intrusion tolerance based on machine learning is proposed to solve the problems of selection of evaluation indexes, poor evaluation accuracy and efficiency in the evaluation method of power information intrusion tolerance. An intelligent evaluation system of power information intrusion tolerance is established, and an intelligent evaluation model is established by using random forest algorithm. The random forest algorithm determines the evaluation weight according to the quantitative value of the index, sets the value of the intrusion risk function, and obtains the intelligent evaluation results of intrusion tolerance. The method studied is applied to the comparative evaluation experiment of the survivability situation of the power information physical system. The evaluation mean square deviation of the intelligent evaluation method based on machine learning is less than 0.1, the average time is 140.72ms, and the evaluation efficiency is increased by 42.51%. At 1000min, the reliability value is still 0.46, which has practical significance.

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Section: Introductionmentioning

confidence: 99%