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DDoS Attack Detection in Cloud Computing Based on ensemble feature selection and Deep Learning

Computers, Materials & Continua 2023年第5期75卷 3571-3588页

作者： Yousef Sanjalawe Turke Althobaiti Cybersecurity Department Faculty of Information TechnologyAmerican University of Madaba(AUM)Amman11821Jordan Computer Science Department PY CollageNorthern Border University(NBU)9280 NBUAr’arSaudi Arabia Computer Science Department Faculty of ScienceNorthern Border University(NBU)9280 NBUAr’arSaudi Arabia Remote Sensing Northern Border University(NBU) 9280 NBUAr’arSaudi Arabia

Intrusion Detection System(IDS)in the cloud Computing(CC)environment has received paramount interest over the last few *** the latest approaches,Deep Learning(DL)-based IDS methods allow the discovery of attacks with the highest *** the CC environment,Distributed Denial of Service(DDoS)attacks are *** cloud services will be rendered unavailable to legitimate end-users as a consequence of the overwhelming network traffic,resulting in financial *** various researchers have proposed many detection techniques,there are possible obstacles in terms of detection performance due to the use of insignificant traffic ***,in this paper,a hybrid deep learning mode based on hybridizing Convolutional Neural Network(CNN)with Long-Short-Term Memory(LSTM)is used due to its robustness and efficiency in detecting normal and attack ***,the ensemble feature selection,mutualization aggregation between Particle Swarm Optimizer(PSO),Grey Wolf Optimizer(PSO),Krill Hird(KH),andWhale Optimization Algorithm(WOA),is used to select the most important features that would influence the detection performance in detecting DDoS attack in CC.A benchmark dataset proposed by the Canadian Institute of Cybersecurity(CIC),called CICIDS 2017 is used to evaluate the proposed *** results revealed that the proposed IDS outperforms the state-of-the-art IDSs,as it achieved 97.9%,98.3%,97.9%,98.1%,*** a result,the proposed IDS achieves the requirements of getting high security,automatic,efficient,and self-decision detection of DDoS attacks.

关键词： CIC IDS 2017 cloud computing distributed denial of service ensemble feature selection intrusion detection system

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A new ensemble feature selection and its application to pattern classification

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控制理论与应用（英文版） 2009年第4期7卷 419-426页

作者： Dongbo ZHANG Yaonan WANG Institute of Information Engineering Xiangtan University Xiangtan Hunan 411105 China College of Electrical and Information Engineering Hunan University Changsha Hunan 410082 China

Neural network ensemble based on rough sets reduct is proposed to decrease the computational complexity of conventional ensemble feature selection algorithm. First, a dynamic reduction technology combining genetic algorithm with resampling method is adopted to obtain reducts with good generalization ability. Second, Multiple BP neural networks based on different reducts are built as base classifiers. According to the idea of selective ensemble, the neural network ensemble with best generalization ability can be found by search strategies. Finally, classification based on neural network ensemble is implemented by combining the predictions of component networks with voting. The method has been verified in the experiment of remote sensing image and five UCI datasets classification. Compared with conventional ensemble feature selection algorithms, it costs less time and lower computing complexity, and the classification accuracy is satisfactory.

关键词： Rough sets reduction ensemble feature selection Neural network ensemble Remote sensing image classification

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