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

Computer Modeling in Engineering & Sciences 2023年第10期137卷 265-298页

作者： ouail mjahed Salah El Hadaj El Mahdi El Guarmah Soukaina mjahed Faculty of Sciences and Technology Department of Computer SciencesCadi Ayyad UniversityMarrakech40000Morocco Mathematics and Informatics Department Royal Air SchoolMarrakech40000Morocco

Due to the increasing number of cyber-attacks,the necessity to develop efficient intrusion detection systems(IDS)is more imperative than *** 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 *** intrusion detection data usually contains several features,it is essential to select the best ones *** 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 ***,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 ***,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)*** metrics extracted from confusion matrices,such as accuracy,precision,sensitivity and F1-score are *** 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 ***-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 ***,the best-p

关键词： Classification neural networks Fuzzy C-means metaheuristic algorithm CICIDS2017 intrusion detection system

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

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Computer Systems Science & Engineering 2023年第10期47卷 757-775页

作者： ouail mjahed Salah El Hadaj El Mahdi El Guarmah Soukaina mjahed L2IS Department of Computer SciencesFaculty of Sciences and TechnologyCadi Ayyad UniversityMarrakech40000Morocco 2Mathematics and Informatics DepartmentRoyal Air SchoolMarrakech40000Morocco

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.

关键词： Classification neural networks metaheuristic algorithm intrusion detection system DoS/DDoS

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Hybridization of Fuzzy and Hard Semi-Supervised Clustering Algorithms Tuned with Ant Lion Optimizer Applied to Higgs Boson Search

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Computer Modeling in Engineering & Sciences 2020年第11期125卷 459-494页

作者： Soukaina mjahed Khadija Bouzaachane Ahmad Taher Azar Salah El Hadaj Said Raghay Faculty of Sciences and Technology Department of Applied Mathematics and Computer SciencesCadi Ayyad UniversityMarrakech40000Morocco Robotics and Internet-of-Things Lab(RIOTU) Prince Sultan UniversityRiyadh12435Saudi Arabia Faculty of Computers and Artificial Intelligence Benha UniversityBenha13511Egypt

This paper focuses on the unsupervised detection of the Higgs boson particle using the most informative features and variables which characterize the“Higgs machine learning challenge 2014”data *** unsupervised detection goes in this paper analysis through 4 steps:(1)selection of the most informative features from the considered data;(2)definition of the number of clusters based on the elbow *** experimental results showed that the optimal number of clusters that group the considered data in an unsupervised manner corresponds to 2 clusters;(3)proposition of a new approach for hybridization of both hard and fuzzy clustering tuned with Ant Lion Optimization(ALO);(4)comparison with some existing metaheuristic optimizations such as Genetic Algorithm(GA)and Particle Swarm Optimization(PSO).By employing a multi-angle analysis based on the cluster validation indices,the confusion matrix,the efficiencies and purities rates,the average cost variation,the computational time and the Sammon mapping visualization,the results highlight the effectiveness of the improved Gustafson-Kessel algorithm optimized withALO(ALOGK)to validate the proposed *** if the paper gives a complete clustering analysis,its novel contribution concerns only the Steps(1)and(3)considered *** first contribution lies in the method used for Step(1)to select the most informative features and *** used the t-Statistic technique to rank ***,a feature mapping is applied using Self-Organizing Map(SOM)to identify the level of correlation between ***,Particle Swarm Optimization(PSO),a metaheuristic optimization technique,is used to reduce the data set *** second contribution of thiswork concern the third step,where each one of the clustering algorithms as K-means(KM),Global K-means(GlobalKM),Partitioning AroundMedoids(PAM),Fuzzy C-means(FCM),Gustafson-Kessel(GK)and Gath-Geva(GG)is optimized and tuned with ALO.

关键词： Ant lion optimization binary clustering clustering algorithms Higgs boson feature extraction dimensionality reduction elbow criterion genetic algorithm particle swarm optimization

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