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A Novel Eccentric Intrusion Detection Model Based on Recurrent Neural Networks with Leveraging LSTM

Computers, Materials & Continua 2024年第3期78卷 3089-3127页

作者： Navaneetha Krishnan Muthunambu Senthil Prabakaran Balasubramanian Prabhu Kavin Kishore Senthil Siruvangur Kavitha Chinnadurai jehad Ali Department of Computer Applications Karpagam College of EngineeringCoimbatore641032India Department of Computer Science and Engineering Karpagam College of EngineeringCoimbatore641032India Department of Data Science and Business Systems College of Engineering and TechnologySRM Institute of Science and TechnologyKattankulathur603203India Department of Computer Science and Engineering Sathyabama Institute of Science and TechnologyJeppiaar NagarRajiv Gandhi SalaiChennai600119India Department of AI Convergence Network Ajou UniversitySuwon16499Korea

The extensive utilization of the Internet in everyday life can be attributed to the substantial accessibility of online services and the growing significance of the data transmitted via the ***,this development has expanded the potential targets that hackers might *** adequate safeguards,data transmitted on the internet is significantly more susceptible to unauthorized access,theft,or *** identification of unauthorised access attempts is a critical component of cybersecurity as it aids in the detection and prevention of malicious *** research paper introduces a novel intrusion detection framework that utilizes Recurrent Neural Networks(RNN)integrated with Long Short-Term Memory(LSTM)*** proposed model can identify various types of cyberattacks,including conventional and distinctive *** networks,a specific kind of feedforward neural networks,possess an intrinsic memory *** Neural Networks(RNNs)incorporating Long Short-Term Memory(LSTM)mechanisms have demonstrated greater capabilities in retaining and utilizing data dependencies over extended *** such as data types,training duration,accuracy,number of false positives,and number of false negatives are among the parameters employed to assess the effectiveness of these models in identifying both common and unusual *** are utilised in conjunction with LSTM to support human analysts in identifying possible intrusion events,hence enhancing their decision-making capabilities.A potential solution to address the limitations of Shallow learning is the introduction of the Eccentric Intrusion Detection *** model utilises Recurrent Neural Networks,specifically exploiting LSTM *** proposed model achieves detection accuracy(99.5%),generalisation(99%),and false-positive rate(0.72%),the parameters findings reveal that it is superior to state-of-the-art techniques.

关键词： Cybersecurity intrusion detection machine learning leveraging long short-term memory(LLSTM) CICIDS2019 dataset innovative cyberattacks

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A Hybrid and Lightweight Device-to-Server Authentication Technique for the Internet of Things

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Computers, Materials & Continua 2024年第3期78卷 3805-3823页

作者： Shaha Al-Otaibi Rahim Khan Hashim Ali Aftab Ahmed Khan Amir Saeed jehad Ali Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Science Abdul Wali Khan University MardanMardan23200Pakistan Department of Computer Science and IT UET PeshawarJalozai Campus PeshawarPeshawar24240Pakistan Department of AI Convergence Network Ajou UniversitySuwon16499South Korea

The Internet of Things(IoT)is a smart networking infrastructure of physical devices,i.e.,things,that are embedded with sensors,actuators,software,and other technologies,to connect and share data with the respective server *** IoTs are cornerstones in different application domains,the device’s authenticity,i.e.,of server(s)and ordinary devices,is the most crucial issue and must be resolved on a priority ***,various field-proven methodologies were presented to streamline the verification process of the communicating devices;however,location-aware authentication has not been reported as per our knowledge,which is a crucial metric,especially in scenarios where devices are *** paper presents a lightweight and location-aware device-to-server authentication technique where the device’s membership with the nearest server is subjected to its location information along with other ***,Media Access Control(MAC)address and Advance Encryption Scheme(AES)along with a secret shared key,i.e.,λ_(i) of 128 bits,have been utilized by Trusted Authority(TA)to generate MaskIDs,which are used instead of the original ID,for every device,i.e.,server and member,and are shared in the offline ***,TA shares a list of authentic devices,i.e.,server S_(j) and members C_(i),with every device in the IoT for the onward verification process,which is required to be executed before the initialization of the actual communication ***,every device should be located such that it lies within the coverage area of a server,and this location information is used in the authentication process.A thorough analytical analysis was carried out to check the susceptibility of the proposed and existing authentication approaches against well-known intruder attacks,i.e.,man-in-the-middle,masquerading,device,and server impersonations,etc.,especially in the IoT ***,proposed authentication and existing state-of-the-art approaches have been s

关键词： Internet of things authenticity security location communication

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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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抑郁症的非药物治疗：系统性综述与证据图

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英国医学杂志中文版 2017年第11期20卷 660-667页

作者： Wigdan H Farah Mouaz Alsawas Maria Mainou Fares Alahdab Magdoleen H Farah Ahmed T Ahmed Essa A Mohamed jehad almasri Michael R Gionfriddo Ana Castaneda-Guarderas Khaled Mohammed Zhen Wang Noor Asi Craig N Sawchuk Mark D Williams Larry J Prokop M Hassan Murad Annie LeBlanc Evidence-Based Przctiee Center Mayo Clinic Robert D. and Patricia E. Kern Center for the Science of Health Care Delivery Mayo Clinic Rochester Minnesota USA Knowledge and Evaluation Research Unit Mayo Clinic Rochester Minnesota USA Division of Preventive. Occupational and Aerospace Medicine Mayo Clinic Rochester Minnesota USA Integrated Behavioral Health Departmenl of Psychiatry and Psychology Mayo Clinic Rochester Minnesota USA Mayo GraduateSchool Mayo ClinicRochesterMinnesota USA Center for PharmacyInnovation and Outcomes Geislnger Health System Forty Fort PennsylvaniaUSA Department of Emergency Medicine Mayo ClinicRochesterMinnesota USA Mayo Clinic

背景抑郁症非药物治疗的疗效比较目前仍然不清楚。方法我们对一些系统性综述作了概述，回顾了随机对照试验（RCT），比较抑郁症非药物治疗的有效性和认知行为治疗（CBT）。对多个电子数据库在2016年2月之前发表的所有文献进行了检索，... 详细信息

背景抑郁症非药物治疗的疗效比较目前仍然不清楚。方法我们对一些系统性综述作了概述，回顾了随机对照试验（RCT），比较抑郁症非药物治疗的有效性和认知行为治疗（CBT）。对多个电子数据库在2016年2月之前发表的所有文献进行了检索，不限制语言。成对的评阅人进行资料纳入、数据提炼并评估偏倚的风险。在适用的时候进行了荟萃分析。结果我们收录了367项RCT，纳入了约2000名患者，进行了11种治疗，导出17项独立的头对头比较。通过标准化的量表评估，发现CBT、自然疗法、行为干预与躯体活动干预可以降低抑郁的严重程度。然而，这些非药物干预的相对疗效仍然缺乏。这些干预对临床缓解和复发的效果不清楚。CBT的发生率比抗抑郁药物更低。局限性由于证据缺乏一致性、证据不清或偏倚风险高，导致证据的质量只是低到中等，限制了我们研究结果的可信度。结论抑郁症的非药物治疗能减少抑郁症状，在轻度到重度抑郁症患者中，应当合并抗抑郁药物治疗，一同进行。对于非药物治疗的选择，应当根据患者的价值观、偏好、临床和社会因素来与患者共同作出决定。

关键词：抑郁症非药物治疗系统性综述证据图

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Greedy particle swarm and biogeography-based optimization algorithm

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International Journal of Intelligent Computing and Cybernetics 2015年第1期8卷 28-49页

作者： jehad Ababneh Department of Electronics and Communications Engineering A’Sharqiyah UniversityIbraOman Jordan University of Science and Technology IrbidJordan

Purpose–The purpose of this paper is to propose an algorithm that combines the particle swarm optimization(PSO)with the biogeography-based optimization(BBO)***/methodology/approach–The BBO and the PSO algorithms are jointly used in to order to combine the advantages of both *** efficiency of the proposed algorithm is tested using some selected standard benchmark *** performance of the proposed algorithm is compared with that of the differential evolutionary(DE),genetic algorithm(GA),PSO,BBO,blended BBO and hybrid BBO-DE ***–Experimental results indicate that the proposed algorithm outperforms the BBO,PSO,DE,GA,and the blended BBO algorithms and has comparable performance to that of the hybrid BBO-DE ***,the proposed algorithm is simpler than the BBO-DE algorithm since the PSO does not have complex operations such as mutation and crossover used in the DE ***/value–The proposed algorithm is a generic algorithm that can be used to efficiently solve optimization problems similar to that solved using other popular evolutionary algorithms but with better performance.

关键词： Optimization Particle swarm optimization Evolutionary algorithm Biogeography-based optimization

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Doubling down with CAR-T cell cancer immunotherapy:a two-step recognition circuit enables discrimination between target antigen high and low cancer cells

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Signal Transduction and Targeted Therapy 2021年第8期6卷 2280-2281页

作者： jehad Charo Bruno Gomes hoffmann-la roche ag pharmaceutical research and early development oncologyroche innovation center zurichzurichswitzerland hoffmann-la roche ag pharmaceutical research and early development oncologyroche innovation center baselbaselswitzerland

In a recent article published in Science by Hernandez-Lopez et *** antigen receptor(CAR)expressing T(CAR-T)cells with a two-step recognition system successfully discriminated between target cells expressing high and l... 详细信息

关键词： CAR cancer

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Quality of Service Improvement with Optimal Software-Defined Networking Controller and Control Plane Clustering

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Computers, Materials & Continua 2021年第4期67卷 849-875页

作者： jehad Ali Byeong-hee Roh Department of Computer Engineering and Department of AI Convergence Network Suwon16499Korea

The controller is indispensable in software-defined networking(SDN).With several features,controllers monitor the network and respond promptly to dynamic *** performance affects the quality-of-service(QoS)in *** controller supports a set of ***,the support of the features may be more prominent in one ***,a single controller leads to performance,single-point-of-failure(SPOF),and scalability *** overcome this,a controller with an optimum feature set must be available for ***,a cluster of optimum feature set controllers will overcome an SPOF and improve the QoS in ***,leveraging an analytical network process(ANP),we rank SDN controllers regarding their supporting features and create a hierarchical control plane based cluster(HCPC)of the highly ranked controller computed using the ANP,evaluating their performance for the OS3E *** results demonstrated in Mininet reveal that a HCPC environment with an optimum controller achieves an improved ***,the experimental results validated in Mininet show that our proposed approach surpasses the existing distributed controller clustering(DCC)schemes in terms of several performance metrics i.e.,delay,jitter,throughput,load balancing,scalability and CPU(central processing unit)utilization.

关键词： Quality-of-service software-defined networking controller hierarchical control plane clustering scalability

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Overview on the endoscopic treatment for obesity:A review

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World Journal of Gastroenterology 2023年第40期29卷 5526-5542页

作者： Maheeba Abdulla Nafeesa Mohammed jehad AlQamish Department of Internal Medicine Ibn Al Nafees HospitalManama 54533Bahrain Department of Intensive Care Unit Salmaniya Medical ComplexManama 5616Bahrain

Obesity rates have increased,and so has the need for more specific *** trend has raised interest in non-surgical weight loss techniques that are novel,safe,and ***,the present review describes the endoscopic bariatric treatment for obesity,its most recent supporting data,the questions it raises,and its future *** endoscopic bariatric therapies for weight reduction,such as intragastric balloons(IGBs),aspiration therapy(AT),small bowel endoscopy,endoscopic sleeve gastroplasty,endoluminal procedures,malabsorption endoscopic procedures,and methods of regulating gastric emptying,were explored through literature sourced from different ***,AT,and small bowel endoscopy have short-term effects with a possibility of weight *** adverse events have occurred;however,all procedures reduce *** and nausea are common side effects,although serious complications have also been observed.

关键词： Overweight Gastric bypass Malabsorption Intragastric balloons

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Effectiveness of an educational program on improving healthcare providers' knowledge of acute stroke: A randomized block design study

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World Journal of Emergency Medicine 2021年第2期12卷 93-98页

作者： jehad A.Rababah Mohammed MAl-Hammouri Esra’a AlNsour Jordan University of Science and Technology Faculty of NursingIrbid 22110Jordan Prince Al Hussein Bin Abdullah II Academy for Civil Protection Amman 11511Jordan

BACKGROUND: Stroke is a time-sensitive neurological disease and a life-threatening medical condition. Providing timely management for stroke patients is a crucial issue in healthcare settings. The primary objective of this study is to evaluate the effectiveness of an evidence-based educational program on healthcare providers'(HCPs) overall knowledge of ***: A randomized block design with post-test only was used. A total of 189 HCPs(physicians, registered nurses, and paramedics) involved with treating stroke patients in the emergency were recruited. Participants were randomly assigned to either the intervention or waiting list control group. A one-session, stroke educational program was offered to the HCPs followed by a post-test designed to assess knowledge about stroke. RESULTS: A significant main effect on the profession type was found, with physicians having higher mean scores of stroke knowledge compared with nurses and paramedics(F [2, 183]=48.55, P<0.001). The implemented educational program had a positive effect on increasing the level of stroke knowledge among HCPs(F [1, 183]=43.31, P<0.001). The utilization of any evidence-based assessment tools for patients with suspected stroke was denied by 36% of the total ***: The implemented intervention can increase HCP's knowledge regarding stroke. Stroke education should be considered as one of the essential requirements for professional development for all HCPs in the emergency.

关键词： Stroke Misdiagnosis Healthcare providers'knowledge Stroke knowledge

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