Blockchain-Assisted Hybrid Harris Hawks Optimization Based Deep DDoS Attack Detection in the IoT Environment

被引:21
|
作者
Katib, Iyad [1 ]
Ragab, Mahmoud [2 ]
机构
[1] King Abdulaziz Univ, Fac Comp & Informat Technol, Dept Comp Sci, Jeddah 21589, Saudi Arabia
[2] King Abdulaziz Univ, Fac Comp & Informat Technol, Informat Technol Dept, Jeddah 21589, Saudi Arabia
关键词
intrusion detection system; DDoS attacks; internet of things; metaheuristics; blockchain; FRAMEWORK; INTERNET; HEALTH; EDGE;
D O I
10.3390/math11081887
中图分类号
O1 [数学];
学科分类号
0701 ; 070101 ;
摘要
The Internet of Things (IoT) is developing as a novel phenomenon that is applied in the growth of several crucial applications. However, these applications continue to function on a centralized storage structure, which leads to several major problems, such as security, privacy, and a single point of failure. In recent years, blockchain (BC) technology has become a pillar for the progression of IoT-based applications. The BC technique is utilized to resolve the security, privacy, and single point of failure (third-part dependency) issues encountered in IoT applications. Conversely, the distributed denial of service (DDoS) attacks on mining pools revealed the existence of vital fault lines amongst the BC-assisted IoT networks. Therefore, the current study designs a hybrid Harris Hawks with sine cosine and a deep learning-based intrusion detection system (H3SC-DLIDS) for a BC-supported IoT environment. The aim of the presented H3SC-DLIDS approach is to recognize the presence of DDoS attacks in the BC-assisted IoT environment. To enable secure communication in the IoT networks, BC technology is used. The proposed H3SC-DLIDS technique designs a H3SC technique by integrating the concepts of Harris Hawks optimization (HHO) and sine cosine algorithm (SCA) for feature selection. For the intrusion detection process, a long short-term memory auto-encoder (LSTM-AE) model is utilized in this study. Finally, the arithmetic optimization algorithm (AOA) is implemented for hyperparameter tuning of the LSTM-AE technique. The proposed H3SC-DLIDS method was experimentally validated using the BoT-IoT database, and the results indicate the superior performance of the proposed H3SC-DLIDS technique over other existing methods, with a maximum accuracy of 99.05%.
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收藏
页数:16
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