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Novel Framework for an Intrusion Detection system Using Multiple Feature selection Methods Based on Deep Learning

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Tsinghua science and Technology 2024年第4期29卷 948-958页

作者： A.E.M.Eljialy mohammed Yousuf Uddin sultan Ahmad Department of Information systems College of Computer Engineering and SciencesPrince Sattam Bin Abdulaziz UniversityAlkharjSaudi Arabia Department of Computer science College of Computer Engineering and SciencesPrince Sattam Bin Abdulaziz UniversityAlkharjSaudi Arabiaand also with University Center for Research and Development(UCRD)Department of Computer Science and EngineeringChandigarh UniversityPunjabIndia

Intrusion detection systems (idss) are deployed to detect anomalies in real time. They classify a network’s incoming traffic as benign or anomalous (attack). An efficient and robust ids in software-defined networks is an inevitable component of network security. The main challenges of such an ids are achieving zero or extremely low false positive rates and high detection rates. Internet of Things (IoT) networks run by using devices with minimal resources. This situation makes deploying traditional idss in IoT networks unfeasible. Machine learning (ML) techniques are extensively applied to build robust idss. Many researchers have utilized different ML methods and techniques to address the above challenges. The development of an efficient ids starts with a good feature selection process to avoid overfitting the ML model. This work proposes a multiple feature selection process followed by classification. In this study, the software-defined networking (sDN) dataset is used to train and test the proposed model. This model applies multiple feature selection techniques to select high-scoring features from a set of features. Highly relevant features for anomaly detection are selected on the basis of their scores to generate the candidate dataset. Multiple classification algorithms are applied to the candidate dataset to build models. The proposed model exhibits considerable improvement in the detection of attacks with high accuracy and low false positive rates, even with a few features selected.

关键词： random forest feature selection decision tree software-defined network logistic regression intrusion detection system XGB classifier AdaBoost

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

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

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

Human dental pulp stem/stromal cells in clinical practice

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World Journal of stem Cells 2024年第2期16卷 54-57页

作者： mohammed E Grawish Department of Oral Biology Faculty of DentistryMansoura UniversityMansoura 35561Egypt Department of Oral Biology Faculty of Oral and Dental MedicineDelta University for Science and TechnologyMansoura 11152Egypt

Dental pulp stem/stromal cells(DPsCs)are fibroblast-like,neural crest-derived,and multipotent cells that can differentiate into several *** are relatively easy to isolate from healthy and inflamed pulps,with little ethical concerns and can be successfully cryopreserved and *** therapeutic effects of DPsCs derived from animal or human sources have been extensively studied through in-vitro and in-vivo animal experiments and the findings indicated that DPsCs are effective not only for dental diseases but also for systemic *** that translational research is a critical step through which the fundamental scientific discoveries could be translated into applicable diagnostics and therapeutics that directly benefit humans,several clinical studies were carried out to generate evidence for the efficacy and safety of autogenous or allogeneic human DPsCs(hDPsCs)as a treatment modality for use in cell-based therapy,regenerative medicine/dentistry and tissue *** clinical medicine,hDPsCs were effective for treating acute ischemic stroke and human exfoliated deciduous teeth-conditioned medium(sHED-CM)repaired vascular damage of the corpus cavernous,which is the main cause of erectile *** in clinical dentistry,autologous sHED was able to rege-nerate necrotic dental pulp after implantation into injured teeth,and micrografts enriched with autologous hDPsCs and collagen sponge were considered a treatment option for human intrabony *** contrast,hDPsCs did not add a significant regenerative effect when they were used for the treatment of post-extraction ***-scale clinical studies across diverse populations are still lacking to provide robust evidence on the safety and efficacy of hDPsCs as a new treatment option for various human diseases including dental-related problems.

关键词： Dental pulp stem/stromal cells Human clinical studies Regenerative medicine Regenerative dentistry Cell-based therapy

Learning Dual-Layer User Representation for Enhanced Item Recommendation

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Computers, Materials & Continua 2024年第7期80卷 949-971页

作者： Fuxi Zhu Jin Xie mohammed alshahrani Applied Research Center of Artificial Intelligence Wuhan CollegeWuhan430212China College of Computer science South-Central MINZU UniversityWuhan430074China Unmanned.Company Riyadh11564Saudi Arabia

User representation learning is crucial for capturing different user preferences,but it is also critical challenging because user intentions are latent and dispersed in complex and different patterns of user-generated data,and thus cannot be measured ***-based data models can learn user representations by mining latent semantics,which is beneficial to enhancing the semantic function of user ***,these technologies only extract common features in historical records and cannot represent changes in user ***,sequential feature can express the user’s interests and intentions that change time by *** the sequential recommendation results based on the user representation of the item lack the interpretability of preference *** address these issues,we propose in this paper a novel model with Dual-Layer User Representation,named DLUR,where the user’s intention is learned based on two different layer ***,the latent semantic layer adds an interactive layer based on Transformer to extract keywords and key sentences in the text and serve as a basis for *** sequence layer uses the Transformer model to encode the user’s preference intention to clarify changes in the user’s ***,this dual-layer user mode is more comprehensive than a single text mode or sequence mode and can effectually improve the performance of *** extensive experiments on five benchmark datasets demonstrate DLUR’s performance over state-of-the-art recommendation *** addition,DLUR’s ability to explain recommendation results is also demonstrated through some specific cases.

关键词： User representation latent semantic sequential feature interpretability

Dynamic thermal performance of multilayer hollow clay walls filled with insulation materials:Toward energy saving in hot climates

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Energy and Built Environment 2024年第1期5卷 70-80页

作者： Yassine Chihab mohammed Garoum Najma Laaroussi mohammed V University in Rabat MaterialsEnergy and Acoustics TEAMRabatMorocco

This study examines the benefits of incorporating passive techniques into multilayer hollow clay brick walls to improve their dynamic thermal *** finite element approach was used to solve the incompressible Navier-stokes and energy equations to analyze the dynamic thermal response of walls exposed to real thermal excitations of the Marrakesh *** results show that increasing the emissivity from 0.1 to 0.9 significantly increases the total heat load over 24 ***,filling 100% of the cavities with insulation materials delayed the temperature peak by about 2.3 h and lowered the decrement factor by roughly 43%,with a value smaller than *** addition,it is demonstrated that the total thermal load is reduced by approximately 28% for improved wall configurations(100% insulation filling cavities)compared to traditional wall configurations(100% air filling cavities),which aids in improving building energy efficiency.

关键词： Time lag Decrement factor Coupled heat transfer Analytical solution Hollow clay brick Insulation filling cavities Energy savings in building

Fluorophosphates and fluorosulfates cathode materials: Progress towards high energy density sodium-ion battery

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Nano Research 2024年第3期17卷 1427-1440页

作者： mohammed Hadouchi Jingrong Hou Toshinari Koketsu Abdelilah Lahmar Jiwei Ma Laboratoire de Chimie Appliquée des Matériaux Centre des Sciences des MatériauxFaculty of ScienceMohammed V University in RabatAvenue Ibn BattoutaBP 1014RabatMorocco shanghai Key Laboratory for R&D and Application of Metallic Functional Materials Institute of New Energy for VehiclesSchool of Materials Science and EngineeringTongji UniversityShanghai 201804China Battery Materials Development section Business Creation Sector R&D CenterMitsui Mining&Smelting Co.Ltd.362-0021 SaitamaJapan Laboratoiry of Condensed Matter Physics University of Picardie Jules Verne33 Rue Saint-Leu 80039AmiensFrance

The rapid diffusion of renewable energy boosts the wide deployment of large-scale energy storage *** the low cost and high crustal abundance,sodium-ion battery(sIB)technology is expected to become a dominant technology in that area in the *** the practical application,novel cathode materials are urged to develop that show high energy density without sacrificing their cost and benignity to the *** the years of many studies,this still remains a huge challenge to battery *** this review,we discuss recent breakthroughs in sIB cathode materials with high energy density,namely fluorphosphates and *** design of materials,the crystal structure,the electrochemical performance,and the underlaying intercalation mechanism are systematically *** strategies and research directions are also provided to advance future high-energy,low-cost,and ecofriendly cathode materials for next generation sIB.

关键词： fluorophosphates fluorosulfates crystal structure intercalation mechanism high energy density sodium-ion battery

Traffic Control Based on Integrated Kalman Filtering and Adaptive Quantized Q-Learning Framework for Internet of Vehicles

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Computer Modeling in Engineering & sciences 2024年第3期138卷 2103-2127页

作者： Othman s.Al-Heety Zahriladha Zakaria Ahmed Abu-Khadrah Mahamod Ismail sarmad Nozad Mahmood mohammed Mudhafar shakir Hussein alsariera Faculty of Electronic and Computer Engineering Universiti Teknikal Malaysia Melaka(UTeM)Durian TunggalMalaysia College of Computing&Informatics Saudi Electronic UniversityRiyadhSaudi Arabia Department of Electrical Electronic and System EngineeringUniversiti Kebangsaan MalaysiaUKM BangiSelangorMalaysia Computer Technology Engineering College of Engineering TechnologyAl-Kitab UniversityKirkukIraq Faculty of Engineering and Electronic Engineering Universiti Tun Hussein Onn Malaysia(UTHM)Batu PahatMalaysia 6Research Center DepartmentThe University of MashreqBaghdadIraq

Intelligent traffic control requires accurate estimation of the road states and incorporation of adaptive or dynamically adjusted intelligent algorithms for making the *** this article,these issues are handled by proposing a novel framework for traffic control using vehicular communications and Internet of Things *** framework integrates Kalman filtering and *** smoothing Kalman filtering,our data fusion Kalman filter incorporates a process-aware model which makes it superior in terms of the prediction *** traditional Q-learning,our Q-learning algorithm enables adaptive state quantization by changing the threshold of separating low traffic from high traffic on the road according to the maximum number of vehicles in the junction *** evaluation,the model has been simulated on a single intersection consisting of four roads:east,west,north,and south.A comparison of the developed adaptive quantized Q-learning(AQQL)framework with state-of-the-art and greedy approaches shows the superiority of AQQL with an improvement percentage in terms of the released number of vehicles of AQQL is 5%over the greedy approach and 340%over the state-of-the-art ***,AQQL provides an effective traffic control that can be applied in today’s intelligent traffic system.

关键词： Q-learning intelligent transportation system(ITs) traffic control vehicular communication kalman filtering smart city Internet of Things

First-Principles Investigation of Charge Transfer Mechanism of B-Doped 3C-siC semiconductor Material

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World Journal of Condensed Matter Physics 2024年第2期14卷 35-44页

作者： Abdullahi Alkali Dauda muhammad Yusuf Onimisi Adeyemi Joshua Owolabi Hameed Adeneyi Lawal Hassan muhammad Gambo Bashir mohammed Aliyu surajo Bala muhammad Lamido Madugu muhammad Abdurrahman Nainna Johnson Akinade Bamikole Physics Department Faculty of Science Air Force Institute of Technology Kaduna Nigeria Physics Department Faculty of Science Nigeria Defence Academy Kaduna Nigeria Physics Department Umaru Musa Yar&#8217adua University Batagarawa Nigeria Physics Department Faculty of Science Gombe State University Gombe Nigeria Physics Department Federal University of Lafia Lafia Nigeria

This study delves into the charge transfer mechanism of boron (B)-doped 3C-siC through first-principles investigations. We explore the effects of B doping on the electronic properties of 3C-siC, focusing on a 12.5% impurity concentration. Our comprehensive analysis encompasses structural properties, electronic band structures, and charge density distributions. The optimized lattice constant and band gap energy of 3C-siC were found to be 4.373 Å and 1.36 eV respectively, which is in agreement with previous research (Bui, 2012;Muchiri et al., 2018). Our results show that B doping narrows the band gap, enhances electrical conductivity, and influences charge transfer interactions. The charge density analysis reveals substantial interactions between B dopants and surrounding carbon atoms. This work not only enhances our understanding of the material’s electronic properties, but also highlights the importance of charge density analysis for characterizing charge transfer mechanisms and their implications in the 3C-siC semiconductors.

关键词： First-Principles Calculations DFT Boron (B)-Doped 3C-siC Charge Transfer

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

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