Improved Supervised and Unsupervised Metaheuristic-Based Approaches to Detect Intrusion in Various Datasets  

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

机构地区：[1]Faculty of Sciences and Technology,Department of Computer Sciences,Cadi Ayyad University,Marrakech,40000,Morocco [2]Mathematics and Informatics Department,Royal Air School,Marrakech,40000,Morocco

出　　处：《Computer Modeling in Engineering & Sciences》2023年第10期265-298,共34页工程与科学中的计算机建模（英文）

摘　　要：Due to the increasing number of cyber-attacks,the necessity to develop efficient intrusion detection systems(IDS)is more imperative than ever.In IDS research,the most effectively used methodology is based on supervised Neural Networks(NN)and unsupervised clustering,but there are few works dedicated to their hybridization with metaheuristic algorithms.As intrusion detection data usually contains several features,it is essential to select the best ones appropriately.Linear Discriminant Analysis(LDA)and t-statistic are considered as efficient conventional techniques to select the best features,but they have been little exploited in IDS design.Thus,the research proposed in this paper can be summarized as follows.a)The proposed approach aims to use hybridized unsupervised and hybridized supervised detection processes of all the attack categories in the CICIDS2017 Dataset.Nevertheless,owing to the large size of the CICIDS2017 Dataset,only 25%of the data was used.b)As a feature selection method,the LDAperformancemeasure is chosen and combinedwith the t-statistic.c)For intrusion detection,unsupervised Fuzzy C-means(FCM)clustering and supervised Back-propagation NN are adopted.d)In addition and in order to enhance the suggested classifiers,FCM and NN are hybridized with the seven most known metaheuristic algorithms,including Genetic Algorithm(GA),Particle Swarm Optimization(PSO),Differential Evolution(DE),Cultural Algorithm(CA),Harmony Search(HS),Ant-Lion Optimizer(ALO)and Black Hole(BH)Algorithm.Performance metrics extracted from confusion matrices,such as accuracy,precision,sensitivity and F1-score are exploited.The experimental result for the proposed intrusion detection,based on training and test CICIDS2017 datasets,indicated that PSO,GA and ALO-based NNs can achieve promising results.PSO-NN produces a tested accuracy,global sensitivity and F1-score of 99.97%,99.95%and 99.96%,respectively,outperforming performance concluded in several related works.Furthermore,the best-proposed approaches are valued in the most recent

关 键 词：Classification neural networks Fuzzy C-means metaheuristic algorithm CICIDS2017 intrusion detection system 

分 类 号：TP309[自动化与计算机技术—计算机系统结构]