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Optimized generative adversarial networks for adversarial Sample Generation

Computers, Materials & Continua 2022年第8期72卷 3877-3897页

作者： Daniyal M.Alghazzawi Syed Hamid Hasan Surbhi Bhatia Faculty of Computing and Information Technology King Abdulaziz UniversityJeddah21589Saudi Arabia Department of Information Systems College of Computer Sciences and Information TechnologyKing Faisal UniversitySaudi Arabia

Detecting the anomalous entity in real-time network traffic is a popular area of research in recent *** few researches have focused on creating malware that fools the intrusion detection system and this paper focuses on this *** are using Deep convolutional generative adversarial networks(DCGAN)to trick the malware classifier to believe it is a normal *** this work,a new dataset is created to fool the Artificial Intelligence(AI)based malware detectors,and it consists of different types of attacks such as Denial of Service(DoS),scan 11,scan 44,botnet,spam,User Datagram Portal(UDP)scan,and ssh *** discriminator used in the DCGAN discriminates two different attack classes(anomaly and synthetic)and one normal *** model collapse,instability,and vanishing gradient issues associated with the DCGAN are overcome using the proposed hybrid Aquila optimizer-based Mine blast harmony search algorithm(AO-MBHS).This algorithm helps the generator to create realistic malware samples to be undetected by the *** performance of the proposed methodology is evaluated using different performance metrics such as training time,detection rate,F-Score,loss function,Accuracy,False alarm rate,*** superiority of the hybrid AO-MBHS based DCGAN model is noticed when the detection rate is changed to 0 after the retraining method to make the defensive technique hard to be noticed by the malware detection *** support vector machines(SVM)is used as the malicious traffic detection application and its True positive rate(TPR)goes from 80%to 0%after retraining the proposed model which shows the efficiency of the proposed model in hiding the samples.

关键词： Aquila optimizer convolutional generative adversarial networks mine blast harmony search algorithm network traffic dataset adversarial artificial intelligence techniques

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