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A Hybrid Model Using Bio-Inspired Metaheuristic Algorithms for Network Intrusion Detection System

Computers, Materials & Continua 2021年第7期68卷 409-429页

作者： omar almomani Department of Computer Network and Information Systems The World Islamic Sciences and Education UniversityAmman11947Jordan

Network Intrusion Detection System(IDS)aims to maintain computer network security by detecting several forms of attacks and unauthorized uses of applications which often can not be detected by *** features selection approach plays an important role in constructing effective network *** bio-inspired metaheuristic algorithms used to reduce features to classify network traffic as abnormal or normal traffic within a shorter duration and showing more ***,this paper aims to propose a hybrid model for network IDS based on hybridization bio-inspired metaheuristic algorithms to detect the generic *** proposed model has two objectives;The first one is to reduce the number of selected features for Network *** objective was met through the hybridization of bioinspired metaheuristic algorithms with each other in a hybrid *** algorithms used in this paper are particle swarm optimization(PSO),multiverse optimizer(MVO),grey wolf optimizer(GWO),moth-flame optimization(MFO),whale optimization algorithm(WOA),firefly algorithm(FFA),and bat algorithm(BAT).The second objective is to detect the generic attack using machine learning *** objective was met through employing the support vector machine(SVM),C4.5(J48)decision tree,and random forest(RF)***-NB15 dataset used for assessing the effectiveness of the proposed hybrid ***-NB15 dataset has nine attacks *** generic attack is the highest among ***,the proposed model aims to identify generic *** data showed that J48 is the best classifier compared to SVM and RF for the time needed to build the *** terms of features reduction for the classification,my data show that the MFO-WOA and FFA-GWO models reduce the features to 15 features with close accuracy,sensitivity and F-measure of all features,whereas MVO-BAT model reduces features to 24 features with the same accuracy,sensitivity and F-measure of all features for all classifiers.

关键词： IDS metaheuristic algorithms PSO MVO GWO MFO WOA FFA BAT SVM J48 RF UNSW-NB15 dataset

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Bio-inspired Hybrid Feature Selection Model for Intrusion Detection

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Computers, Materials & Continua 2022年第10期73卷 133-150页

作者： Adel Hamdan Mohammad Tariq Alwada’n omar almomani Sami Smadi Nidhal Elomari Computer Science Department The World Islamic Sciences and Education UniversityAmmanJordan Network and Cybersecurity Department Teesside UniversityMiddlesbroughUnited Kingdom Information and Network Security The World Islamic Sciences and Education UniversityAmmanJordan Software Engineering The World Islamic Sciences and Education UniversityAmmanJordan

Intrusion detection is a serious and complex *** due to a large number of attacks around the world,the concept of intrusion detection has become very *** research proposes a multilayer bioinspired feature selection model for intrusion detection using an optimized genetic ***,the proposed multilayer model consists of two layers(layers 1 and 2).At layer 1,three algorithms are used for the feature *** algorithms used are Particle Swarm Optimization(PSO),Grey Wolf Optimization(GWO),and Firefly Optimization Algorithm(FFA).At the end of layer 1,a priority value will be assigned for each feature *** layer 2 of the proposed model,the Optimized Genetic Algorithm(GA)is used to select one feature set based on the priority *** are done on standard GA to perform optimization and to fit the proposed *** Optimized GA is used in the training phase to assign a priority value for each feature ***,the priority values are categorized into three categories:high,medium,and ***,the Optimized GA is used in the testing phase to select a feature set based on its *** feature set with a high priority will be given a high priority to be *** the end of phase 2,an update for feature set priority may occur based on the selected features priority and the calculated *** proposed model can learn and modify feature sets priority,which will be reflected in selecting *** evaluation purposes,two well-known datasets are used in these *** first dataset is UNSW-NB15,the other dataset is the *** evaluation criteria are used,such as precision,recall,and *** experiments in this research suggest that the proposed model has a powerful and promising mechanism for the intrusion detection system.

关键词： Intrusion detection Machine learning Optimized Genetic Algorithm(GA) Particle Swarm Optimization algorithms(PSO) Grey Wolf Optimization algorithms(GWO) FireFly Optimization Algorithms(FFA) Genetic Algorithm(GA)

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Ensemble-Based Approach for Efficient Intrusion Detection in Network Traffic

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Intelligent Automation & Soft Computing 2023年第8期37卷 2499-2517页

作者： Ammar almomani Iman Akour Ahmed M.Manasrah omar almomani Mohammad Alauthman Esra’a Abdullah Amaal Al Shwait Razan Al Sharaa School of Computing Skyline University CollegeUniversity City of SharjahP.O.Box 1797SharjahUnited Arab Emirates IT-Department-Al-Huson University College Al-Balqa Applied UniversityP.O.Box 50IrbidJordan Information Systems Department College of Computing&InformaticsUniversity of SharjahUnited Arab Emirates Comp.Info Sciences(CIS)Division Higher Colleges of TechnologySharjahUnited Arab Emirates Computer Sciences Department Yarmouk UniversityIrbidJordan Computer Network and Information Systems Department The World Islamic Sciences and Education UniversityAmman11947Jordan Department of Information Security Faculty of Information TechnologyUniversity of PetraAmmanJordan

The exponential growth of Internet and network usage has neces-sitated heightened security measures to protect against data and network ***,executed through network packets,pose a significant challenge for firewalls to detect and prevent due to the similarity between legit-imate and intrusion *** vast network traffic volume also complicates most network monitoring systems and *** intrusion detection methods have been proposed,with machine learning techniques regarded as promising for dealing with these *** study presents an Intrusion Detection System Based on Stacking Ensemble Learning base(Random For-est,Decision Tree,and k-Nearest-Neighbors).The proposed system employs pre-processing techniques to enhance classification efficiency and integrates seven machine learning *** stacking ensemble technique increases performance by incorporating three base models(Random Forest,Decision Tree,and k-Nearest-Neighbors)and a meta-model represented by the Logistic Regression *** using the UNSW-NB15 dataset,the pro-posed IDS gained an accuracy of 96.16%in the training phase and 97.95%in the testing phase,with precision of 97.78%,and 98.40%for taring and testing,*** obtained results demonstrate improvements in other measurement criteria.

关键词： Intrusion detection system(IDS) machine learning techniques stacking ensemble random forest decision tree k-nearest-neighbor

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Classification of Cybersecurity Threats, Vulnerabilities and Countermeasures in Database Systems

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Computers, Materials & Continua 2024年第11期81卷 3189-3220页

作者： Mohammed Amin Almaiah Leen Mohammad Saqr Leen Ahmad Al-Rawwash Layan Ahmed Altellawi Romel Al-Ali omar almomani King Abdullah the II IT School University of JordanAmman11942Jordan The National Research Center for Giftedness and Creativity King Faisal UniversityAl-Ahsa31982Saudi Arabia Department of Networks and Cybersecurity Al-Ahliyya Amman UniversityAmman19328Jordan

Database systems have consistently been prime targets for cyber-attacks and threats due to the critical nature of the data they *** the increasing reliance on database management systems,this field continues to face numerous *** management systems serve as the foundation of any information system or *** cyber-attack can result in significant damage to the database system and loss of sensitive ***,cyber risk classifications and assessments play a crucial role in risk management and establish an essential framework for identifying and responding to cyber *** assessment aids in understanding the impact of cyber threats and developing appropriate security controls to mitigate *** primary objective of this study is to conduct a comprehensive analysis of cyber risks in database management systems,including classifying threats,vulnerabilities,impacts,and *** classification helps to identify suitable security controls to mitigate cyber risks for each type of ***,this research aims to explore technical countermeasures to protect database systems from cyber *** study employs the content analysis method to collect,analyze,and classify data in terms of types of threats,vulnerabilities,and *** results indicate that SQL injection attacks and Denial of Service(DoS)attacks were the most prevalent technical threats in database systems,each accounting for 9%of *** audit trails,intrusion attempts,and ransomware attacks were classified as the second level of technical threats in database systems,comprising 7%and 5%of incidents,***,the findings reveal that insider threats were the most common non-technical threats in database systems,accounting for 5%of ***,the results indicate that weak authentication,unpatched databases,weak audit trails,and multiple usage of an account were the most common technical vulner

关键词： Cyber threats database systems cyber risk assessment vulnerabilities countermeasures

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