2020
DOI: 10.1007/978-3-030-62223-7_7
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A Two-Phase Cycle Algorithm Based on Multi-objective Genetic Algorithm and Modified BP Neural Network for Effective Cyber Intrusion Detection

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Cited by 2 publications
(2 citation statements)
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“…Yang et al [10] presented an LM-BP neural network model which optimizes the weight threshold of BP neural network. To improve the detection rate of IDS, Gong et al [11] combined the multi-objective genetic algorithm with BP neural network and proposed a novel two-phase cycle training algorithm. Lu et al [12] proposed an intrusion detection model named IPSO-BPNN which combines improved particle swarm optimisation algorithm and BP neural network.…”
Section: Introductionmentioning
confidence: 99%
“…Yang et al [10] presented an LM-BP neural network model which optimizes the weight threshold of BP neural network. To improve the detection rate of IDS, Gong et al [11] combined the multi-objective genetic algorithm with BP neural network and proposed a novel two-phase cycle training algorithm. Lu et al [12] proposed an intrusion detection model named IPSO-BPNN which combines improved particle swarm optimisation algorithm and BP neural network.…”
Section: Introductionmentioning
confidence: 99%
“…The proposed approach can discover a pool of MBPNN-based solutions to detect the intrusions accurately. The approach proposed in this paper has been published by the international conference on ML4CS 2020 [39]. Based on the conference paper, this report is mainly expanded as follows: A genetic algorithm is used to find the optimal combination solution set for prediction, instead of the manual selection method used in the conference paper, and the genetic algorithm manifests better performance.…”
mentioning
confidence: 99%