New Denial of Service Attacks Detection Approach Using Hybridized Deep Neural Networks and Balanced Datasets

作     者：Ouail Mjahed Salah El Hadaj El Mahdi El Guarmah Soukaina Mjahed 

作者机构：L2ISDepartment of Computer SciencesFaculty of Sciences and TechnologyCadi Ayyad UniversityMarrakech40000Morocco 2Mathematics and Informatics DepartmentRoyal Air SchoolMarrakech40000Morocco 

出 版 物：《Computer Systems Science & Engineering》 (计算机系统科学与工程（英文）)

年 卷 期：2023年第47卷第10期

页      面：757-775页

学科分类：08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

主　　题：Classification neural networks metaheuristic algorithm intrusion detection system DoS/DDoS 

摘      要：Denial of Service(DoS/DDoS)intrusions are damaging cyberattacks,and their identification is of great interest to the Intrusion Detection System(IDS).Existing IDS are mainly based on Machine Learning(ML)methods including Deep Neural Networks(DNN),but which are rarely hybridized with other *** intrusion data used are generally imbalanced and contain multiple ***,the proposed approach aims to use a DNN-based method to detect DoS/DDoS attacks using CICIDS2017,CSE-CICIDS2018 and CICDDoS 2019 datasets,according to the following key points.a)Three imbalanced CICIDS2017-2018-2019 datasets,including Benign and DoS/DDoS attack classes,are used.b)A new technique based on K-means is developed to obtain semi-balanced datasets.c)As a feature selectionmethod,LDA(Linear Discriminant Analysis)performance measure is chosen.d)Four metaheuristic algorithms,counting Artificial Immune System(AIS),Firefly Algorithm(FA),Invasive Weeds Optimization(IWO)and Cuckoo Search(CS)are used,for the first time together,to increase the performance of the suggested DNN-based DoS attacks *** experimental results,based on semi-balanced training and test datasets,indicated that AIS,FA,IWO and CS-based DNNs can achieve promising results,even when ***-DNN yields a tested accuracy of 99.97%,99.98%and 99.99%,for the three considered datasets,respectively,outperforming performance established in several related works.