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首页> 外文期刊>International journal of communication systems >Intrusion detection in cloud environment using hybrid genetic algorithm and back propagation neural network
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Intrusion detection in cloud environment using hybrid genetic algorithm and back propagation neural network

机译：Intrusion detection in cloud environment using hybrid genetic algorithm and back propagation neural network

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摘要

The security for cloud network systems is essential and significant to securethe data source from intruders and attacks. Implementing an intrusion detectionsystem (IDS) for securing from those intruders and attacks is the bestoption. Many IDS models are presently based on different techniques and algorithmslike machine learning and deep learning. In this research, an IDS forthe cloud computing environment is proposed. Here in this model, the geneticalgorithm (GA) and back propagation neural network (BPNN) are used forattack detection and classification. The Canadian Institute for CybersecurityCIC-IDS 2017 dataset is used for the evaluation of performance analysis.Initially, from the dataset, the data are preprocessed, and by using the geneticalgorithm, the attack was detected. The detected attacks are classified usingthe BPNN classifier for identifying the types of attacks. The performanceanalysis was executed, and the results are obtained and compared with theexisting machine learning-based classifiers like FC-ANN, NB-RF, KDBN, andFCM-SVM techniques. The proposed GA-BPNN model performs better inevery performance metric, like accuracy, precision, recall, and detection rate.Overall, from the performance analysis, the best classification accuracy isachieved for Web attack detection with 97.90%, and the best detection rate isachieved for Brute force attack detection with 97.89%.

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