Ouail Mjahed^1,*, Salah El Hadaj¹, El Mahdi El Guarmah^1,2, Soukaina Mjahed¹

Computer Systems Science and Engineering, Vol.47, No.1, pp. 757-775, 2023, DOI:10.32604/csse.2023.039111

Abstract Denial of Service (DoS/DDoS) intrusions are damaging cyber-attacks, 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 techniques. The intrusion data used are generally imbalanced and contain multiple features. Thus, 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… More >

Rakhshan Zulfiqar^1,2, Tariq Javed¹, Zain Anwar Ali^2,*, Eman H. Alkhammash³, Myriam Hadjouni⁴

Intelligent Automation & Soft Computing, Vol.37, No.1, pp. 985-1000, 2023, DOI:10.32604/iasc.2023.037248

Abstract The deployment of sensor nodes is an important aspect in mobile wireless sensor networks for increasing network performance. The longevity of the networks is mostly determined by the proportion of energy consumed and the sensor nodes’ access network. The optimal or ideal positioning of sensors improves the portable sensor networks effectiveness. Coverage and energy usage are mostly determined by successful sensor placement strategies. Nature-inspired algorithms are the most effective solution for short sensor lifetime. The primary objective of work is to conduct a comparative analysis of nature-inspired optimization for wireless sensor networks (WSNs’) maximum network coverage. Moreover, it identifies quantity… More >

Abdullah Mujawib Alashjaee^*

Intelligent Automation & Soft Computing, Vol.37, No.1, pp. 235-254, 2023, DOI:10.32604/iasc.2023.036247

Abstract With the recent increase in network attacks by threats, malware, and other sources, machine learning techniques have gained special attention for intrusion detection due to their ability to classify hundreds of features into normal system behavior or an attack attempt. However, feature selection is a vital preprocessing stage in machine learning approaches. This paper presents a novel feature selection-based approach, Remora Optimization Algorithm-Levy Flight (ROA-LF), to improve intrusion detection by boosting the ROA performance with LF. The developed ROA-LF is assessed using several evaluation measures on five publicly available datasets for intrusion detection: Knowledge discovery and data mining tools competition,… More >

Ouail Mjahed^1,*, Salah El Hadaj¹, El Mahdi El Guarmah^1,2, Soukaina Mjahed¹

CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.1, pp. 265-298, 2023, DOI:10.32604/cmes.2023.027581

Abstract 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… More >

Li Yuan¹, Jianping Ji¹, Xuegong Liu¹, Tong Liu², Huiling Chen³, Deng Chen^4,*

CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.1, pp. 415-454, 2023, DOI:10.32604/cmes.2023.026098

Abstract The Swarm intelligence algorithm is a very prevalent field in which some scholars have made outstanding achievements. As a representative, Slime mould algorithm (SMA) is widely used because of its superior initial performance. Therefore, this paper focuses on the improvement of the SMA and the mitigation of its stagnation problems. For this aim, the structure of SMA is adjusted to develop the efficiency of the original method. As a stochastic optimizer, SMA mainly stimulates the behavior of slime mold in nature. For the harmony of the exploration and exploitation of SMA, the paper proposed an enhanced algorithm of SMA called… More > Graphic Abstract

Chandana Gouri Tekkali, Karthika Natarajan^*

CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 3171-3187, 2023, DOI:10.32604/cmc.2023.036865

Abstract Fraud Transactions are haunting the economy of many individuals with several factors across the globe. This research focuses on developing a mechanism by integrating various optimized machine-learning algorithms to ensure the security and integrity of digital transactions. This research proposes a novel methodology through three stages. Firstly, Synthetic Minority Oversampling Technique (SMOTE) is applied to get balanced data. Secondly, SMOTE is fed to the nature-inspired Meta Heuristic (MH) algorithm, namely Binary Harris Hawks Optimization (BinHHO), Binary Aquila Optimization (BAO), and Binary Grey Wolf Optimization (BGWO), for feature selection. BinHHO has performed well when compared with the other two. Thirdly, features… More >

Dongyang Li¹, Shiyu Du^2,*, Yiming Zhang², Meiting Zhao³

CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 2775-2803, 2023, DOI:10.32604/cmc.2023.035911

Abstract Metaheuristic algorithms, as effective methods for solving optimization problems, have recently attracted considerable attention in science and engineering fields. They are popular and have broad applications owing to their high efficiency and low complexity. These algorithms are generally based on the behaviors observed in nature, physical sciences, or humans. This study proposes a novel metaheuristic algorithm called dark forest algorithm (DFA), which can yield improved optimization results for global optimization problems. In DFA, the population is divided into four groups: highest civilization, advanced civilization, normal civilization, and low civilization. Each civilization has a unique way of iteration. To verify DFA’s… More >

Salahahaldeen Duraibi^*

Computer Systems Science and Engineering, Vol.46, No.2, pp. 2509-2525, 2023, DOI:10.32604/csse.2023.036119

Abstract With the growth of the discipline of digital communication, the topic has acquired more attention in the cybersecurity medium. The Intrusion Detection (ID) system monitors network traffic to detect malicious activities. The paper introduces a novel Feature Selection (FS) approach for ID. Reptile Search Algorithm (RSA)—is a new optimization algorithm; in this method, each agent searches a new region according to the position of the host, which makes the algorithm suffers from getting stuck in local optima and a slow convergence rate. To overcome these problems, this study introduces an improved RSA approach by integrating Cauchy Mutation (CM) into the… More >

Pavel Trojovský^1,*, Mohammad Dehghani¹, Eva Trojovská¹, Eva Milkova²

CMES-Computer Modeling in Engineering & Sciences, Vol.136, No.2, pp. 1527-1573, 2023, DOI:10.32604/cmes.2023.025908

Abstract In this paper, based on the concept of the NFL theorem, that there is no unique algorithm that has the best performance for all optimization problems, a new human-based metaheuristic algorithm called Language Education Optimization (LEO) is introduced, which is used to solve optimization problems. LEO is inspired by the foreign language education process in which a language teacher trains the students of language schools in the desired language skills and rules. LEO is mathematically modeled in three phases: (i) students selecting their teacher, (ii) students learning from each other, and (iii) individual practice, considering exploration in local search and… More > Graphic Abstract

Hadi Givi^1,*, Marie Hubálovská²

CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 5283-5300, 2023, DOI:10.32604/cmc.2023.034695

Abstract Metaheuristic algorithms are one of the most widely used stochastic approaches in solving optimization problems. In this paper, a new metaheuristic algorithm entitled Billiards Optimization Algorithm (BOA) is proposed and designed to be used in optimization applications. The fundamental inspiration in BOA design is the behavior of the players and the rules of the billiards game. Various steps of BOA are described and then its mathematical model is thoroughly explained. The efficiency of BOA in dealing with optimization problems is evaluated through optimizing twenty-three standard benchmark functions of different types including unimodal, high-dimensional multimodal, and fixed-dimensional multimodal functions. In order… More >