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Analytical Comparison of Resource Search Algorithms in Non-DHT Mobile Peer-to-Peer Networks

Computers, Materials & Continua 2021年第7期68卷 983-1001页

作者： Ajay Arunachalam Vinayakumar Ravi Moez Krichen Roobaea Alroobaea jehad saad alqurni Centre for Applied Autonomous Sensor Systems(AASS) Örebro UniversityÖrebroSweden Center for Articial Intelligence Prince Mohammad Bin Fahd UniversityKhobarSaudi Arabia Faculty of CSIT Al-Baha UniversitySaudi Arabia ReDCAD LaboratoryUniversity of SfaxTunisia Department of Computer Science College of Computers and Information TechnologyTaif UniversityP.O.Box 11099Taif21944Saudi Arabia Department of Education Technologies College of EducationImam Abdulrahman Bin Faisal UniversitySaudi Arabia

One of the key challenges in ad-hoc networks is the resource discovery *** efciently&quickly the queried resource/object can be resolved in such a highly dynamic self-evolving network is the underlying question?Broadcasting is a basic technique in the Mobile Ad-hoc Networks(MANETs),and it refers to sending a packet from one node to every other node within the transmission *** is a type of broadcast where the received packet is retransmitted once by every *** naive ooding technique oods the network with query messages,while the random walk scheme operates by contacting subsets of each node’s neighbors at every step,thereby restricting the search *** earlier works have mainly focused on the simulation-based analysis of ooding technique,and its variants,in a wired network ***,there have been some empirical studies in peer-to-peer(P2P)networks,the analytical results are still lacking,especially in the context of mobile P2P *** this article,we mathematically model different widely used existing search techniques,and compare with the proposed improved random walk method,a simple lightweight approach suitable for the non-DHT *** provide analytical expressions to measure the performance of the different ooding-based search techniques,and our proposed *** analytically derive 3 relevant key performance measures,i.e.,the *** of steps needed to nd a resource,the probability of locating a resource,and the *** of messages generated during the entire search process.

关键词： Mathematical model MANET P2P networks P2P MANET unstructured search algorithms Peer-to-Peer ad-hoc ooding random walk resource discovery content discovery mobile peer-to-peer broadcast peer

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Privacy Preservation in IoT Devices by Detecting Obfuscated Malware Using Wide Residual Network

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Computers, Materials & Continua 2024年第11期81卷 2395-2436页

作者： Deema Alsekait Mohammed Zakariah Syed Umar Amin Zafar Iqbal Khan jehad saad alqurni Applied College Department of Computer Science and Information TechnologyPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia College of Computer and Information Sciences King Saud UniversityRiyadh11362Saudi Arabia College of Computer and Information Sciences Prince Sultan UniversityRiyadh11586Saudi Arabia Department of Educational Technologies College of EducationImam Abdulrahman Bin Faisal UniversityDammam31441Saudi Arabia

The widespread adoption of Internet of Things(IoT)devices has resulted in notable progress in different fields,improving operational effectiveness while also raising concerns about privacy due to their vulnerability to virus ***,the study suggests using an advanced approach that utilizes machine learning,specifically the Wide Residual Network(WRN),to identify hidden malware in IoT *** research intends to improve privacy protection by accurately identifying malicious software that undermines the security of IoT devices,using the MalMemAnalysis ***,thorough experimentation provides evidence for the effectiveness of the WRN-based strategy,resulting in exceptional performance measures such as accuracy,precision,F1-score,and *** study of the test data demonstrates highly impressive results,with a multiclass accuracy surpassing 99.97%and a binary class accuracy beyond 99.98%.The results emphasize the strength and dependability of using advanced deep learning methods such as WRN for identifying hidden malware risks in IoT ***,a comparison examination with the current body of literature emphasizes the originality and efficacy of the suggested *** research builds upon previous studies that have investigated several machine learning methods for detecting malware on IoT ***,it distinguishes itself by showcasing exceptional performance metrics and validating its findings through thorough experimentation with real-world *** WRN offers benefits in managing the intricacies of malware detection,emphasizing its capacity to enhance the security of IoT *** summarize,this work proposes an effective way to address privacy concerns on IoT devices by utilizing advanced machine learning *** research provides useful insights into the changing landscape of IoT cybersecurity by emphasizing methodological rigor and conducting comparative performance *** research co

关键词： Obfuscated malware detection IoT devices Wide Residual Network(WRN) malware detection machine learning

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Network Security Enhanced with Deep Neural Network-Based Intrusion Detection System

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Computers, Materials & Continua 2024年第7期80卷 1457-1490页

作者： Fatma S.Alrayes Mohammed Zakariah Syed Umar Amin Zafar Iqbal Khan jehad saad alqurni Information Systems Department College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia College of Computer and Information Sciences King Saud UniversityRiyadh11362Saudi Arabia College of Computer and Information Sciences Prince Sultan UniversityRiyadh11586Saudi Arabia Department of Educational Technologies College of EducationImam Abdulrahman Bin Faisal UniversityP.O.Box 1982Dammam31441Saudi Arabia

This study describes improving network security by implementing and assessing an intrusion detection system(IDS)based on deep neural networks(DNNs).The paper investigates contemporary technical ways for enhancing intrusion detection performance,given the vital relevance of safeguarding computer networks against harmful *** DNN-based IDS is trained and validated by the model using the NSL-KDD dataset,a popular benchmark for IDS *** model performs well in both the training and validation stages,with 91.30%training accuracy and 94.38%validation ***,the model shows good learning and generalization capabilities with minor losses of 0.22 in training and 0.1553 in ***,for both macro and micro averages across class 0(normal)and class 1(anomalous)data,the study evaluates the model using a variety of assessment measures,such as accuracy scores,precision,recall,and F1 *** macro-average recall is 0.9422,the macro-average precision is 0.9482,and the accuracy scores are ***,macro-averaged F1 scores of 0.9245 for class 1 and 0.9434 for class 0 demonstrate the model’s ability to precisely identify anomalies *** research also highlights how real-time threat monitoring and enhanced resistance against new online attacks may be achieved byDNN-based intrusion detection systems,which can significantly improve network *** study underscores the critical function ofDNN-based IDS in contemporary cybersecurity procedures by setting the foundation for further developments in this *** research aims to enhance intrusion detection systems by examining cooperative learning techniques and integrating up-to-date threat knowledge.

关键词： Machine-Learning Deep-Learning intrusion detection system security privacy deep neural network NSL-KDD Dataset

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CNN Channel Attention Intrusion Detection SystemUsing NSL-KDD Dataset

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Computers, Materials & Continua 2024年第6期79卷 4319-4347页

Intrusion detection systems(IDS)are essential in the field of cybersecurity because they protect networks from a wide range of online *** goal of this research is to meet the urgent need for small-footprint,highly-adaptable Network Intrusion Detection Systems(NIDS)that can identify *** NSL-KDD dataset is used in the study;it is a sizable collection comprising 43 variables with the label’s“attack”and“level.”It proposes a novel approach to intrusion detection based on the combination of channel attention and convolutional neural networks(CNN).Furthermore,this dataset makes it easier to conduct a thorough assessment of the suggested intrusion detection ***,maintaining operating efficiency while improving detection accuracy is the primary goal of this ***,typical NIDS examines both risky and typical behavior using a variety of *** the NSL-KDD dataset,our CNN-based approach achieves an astounding 99.728%accuracy rate when paired with channel *** to previous approaches such as ensemble learning,CNN,RBM(Boltzmann machine),ANN,hybrid auto-encoders with CNN,MCNN,and ANN,and adaptive algorithms,our solution significantly improves intrusion detection ***,the results highlight the effectiveness of our suggested method in improving intrusion detection precision,signifying a noteworthy advancement in this *** efforts will focus on strengthening and expanding our approach in order to counteract growing cyberthreats and adjust to changing network circumstances.

关键词： Intrusion detection system(IDS) NSL-KDD dataset deep-learning machine-learning CNN channel Attention network security

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Improved Whale Optimization with Local-Search Method for Feature Selection

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Computers, Materials & Continua 2023年第4期75卷 1371-1389页

作者： Malek Alzaqebah Mutasem KAlsmadi Sana Jawarneh jehad saad alqurni Mohammed Tayfour Ibrahim Almarashdeh Rami Mustafa A.Mohammad Fahad A.Alghamdi Nahier Aldhafferi Abdullah Alqahtani Khalid A.Alissa Bashar A.Aldeeb Usama A.Badawi Maram Alwohaibi Hayat Alfagham Department of Mathematics College of ScienceImam Abdulrahman Bin Faisal UniversityP.O.Box 1982Dammam31441Saudi Arabia Basic&Applied Scientific Research Center Imam Abdulrahman Bin Faisal UniversityP.O.Box 1982Dammam31441Saudi Arabia Department of MIS College of Applied Studies and Community ServiceImam Abdulrahman Bin Faisal UniversityP.O.Box 1982Dammam31441Saudi Arabia Department of Computer Science Community CollegeImam Abdulrahman Bin Faisal UniversityP.O.Box 1982Dammam31441Saudi Arabia Department of Educational Technology College of EducationImam Abdulrahman Bin Faisal UniversityP.O.Box 1982DammamSaudi Arabia Department of Computer Information Systems College of Computer Science and Information TechnologyImam Abdulrahman Bin Faisal UniversityP.O.Box 1982Dammam31441Saudi Arabia Department of Computer Science College of Computer Science and Information TechnologyImam Abdulrahman Bin Faisal UniversityP.O.Box 1982Dammam31441Saudi Arabia Deanship of Information and Communication Technology Imam Abdulrahman bin Faisal UniversityP.O.Box 1982Dammam31441Saudi Arabia

Various feature selection algorithms are usually employed to improve classification models’overall *** algorithms typically accompany such algorithms to select the optimal set of *** the most currently attractive trends within optimization algorithms are hybrid *** present paper presents two Stages of Local Search models for feature selection based on WOA(Whale Optimization Algorithm)and Great Deluge(GD).GD Algorithm is integrated with the WOA algorithm to improve exploitation by identifying the most promising regions during the *** version is employed using the best solution found by the WOA algorithm and exploited by the GD *** addition,disruptive selection(DS)is employed to select the solutions from the population for local *** is chosen to maintain the diversity of the population via enhancing low and high-quality ***(15)standard benchmark datasets provided by the University of California Irvine(UCI)repository were used in evaluating the proposed approaches’***,a comparison was made with four population-based algorithms as wrapper feature selection methods from the *** proposed techniques have proved their efficiency in enhancing classification accuracy compared to other wrapper ***,the WOA can search effectively in the feature space and choose the most relevant attributes for classification tasks.

关键词： Optimization whale optimization algorithm great deluge algorithm feature selection and classification

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