计算机科学
服务拒绝攻击
深度学习
应用层DDoS攻击
计算机安全
块链
计算机网络
人工智能
实时计算
互联网
万维网
作者
Ilyas Benkhaddra,Abhishek Kumar,Mohamed Ali Setitra,ZineEl Abidine Bensalem,Hang Lei
出处
期刊:Transactions on Emerging Telecommunications Technologies
日期:2023-01-25
卷期号:34 (4)
被引量:21
摘要
Abstract The attack named Distributed Denial of Service (DDoS) that takes place in the large blockchain network requires an efficient and robust attack detection and prevention mechanism for authenticated access. Blockchain is a distributed network in which the attacker tries to hack the network by utilizing all the resources with the application of enormous requests. Several methods like Rival Technique, filter modular approach and so on, were developed to detect and prevent the DDoS attack in the blockchain; still, detection accuracy is a challenging task. Hence, this research introduces an efficient technique using optimization‐based deep learning by considering the blockchain network and smart contract for the detection and prevention of DDoS attacks. Based on the user request, the traffic is analyzed, and the verification using the smart contract is made to find the authenticated user. After the verification, the response is provided for the authenticated user, and the suspicious traffic is utilized for the detection of DDoS attacks using the Poaching Raptor Optimization‐based deep neural network (Poaching Raptor‐based DNN), in which the classifier is tuned using the proposed optimization algorithm to reduce the training loss. The proposed algorithm is designed by hybridizing the habitual practice of the raptor by considering the concurring behavior, hunting style along with poaching behavior of the Lobo to enhance the detection accuracy. After the attack detection, the nonattacker is responded, and the attacker is prevented by entering the IP/MAC address in the logfile. The performance of the proposed method is evaluated in terms of recall, precision, FPR, and accuracy and obtained the values of 96.3%, 98.22%, 3.33%, and 95.12%, respectively.
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