Explainable AI-Based DDoS Attacks Classification Using Deep Transfer Learning

作     者：Ahmad Alzu’bi Amjad Albashayreh Abdelrahman Abuarqoub Mai A.M.Alfawair 

作者机构：Department of Computer ScienceJordan University of Science and TechnologyIrbid22110Jordan Department of Computer ScienceThe University of JordanAmman11942Jordan Cardiff School of TechnologiesCardiff Metropolitan UniversityCardiffCF52YBUK Prince Abdullah bin Ghazi Faculty of Information and Communication TechnologyAl-Balqa Applied UniversitySalt19117Jordan 

出 版 物：《Computers, Materials & Continua》 (计算机、材料和连续体（英文）)

年 卷 期：2024年第80卷第9期

页      面：3785-3802页

核心收录：

学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 081104[工学-模式识别与智能系统] 08[工学] 0835[工学-软件工程] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：The authors would like to thank the anonymous reviewers for their constructive feedback and insightful comments  which helped us in improving the quality of the manuscript. The authors received no specific funding for this study 

主　　题：DDoS attack classification deep learning explainable AI cybersecurity 

摘      要：In the era of the Internet of Things(IoT),the proliferation of connected devices has raised security concerns,increasing the risk of intrusions into diverse *** the convenience and efficiency offered by IoT technology,the growing number of IoT devices escalates the likelihood of attacks,emphasizing the need for robust security tools to automatically detect and explain *** paper introduces a deep learning methodology for detecting and classifying distributed denial of service(DDoS)attacks,addressing a significant security concern within IoT *** effective procedure of deep transfer learning is applied to utilize deep learning backbones,which is then evaluated on two benchmarking datasets of DDoS attacks in terms of accuracy and time *** leveraging several deep architectures,the study conducts thorough binary and multiclass experiments,each varying in the complexity of classifying attack types and demonstrating real-world ***,this study employs an explainable artificial intelligence(XAI)AI technique to elucidate the contribution of extracted features in the process of attack *** experimental results demonstrate the effectiveness of the proposed method,achieving a recall of 99.39%by the XAI bidirectional long short-term memory(XAI-BiLSTM)model.