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International Journal of Interactive Mobile Technologies 2023年第19期17卷 136-146页

作者： Aljammal, Ashraf H. Qawasmeh, Ahmad Mughaid, Ala Taamneh, Salah Wedyan, Fadi I. Obiedat, Mamoon Department of Computer Science and Applications Prince Al Hussein bin Abdullah II Faculty of Information Technology The Hashemite University Zarqa Jordan Department of Information Technology Prince Al Hussein bin Abdullah II Faculty of Information Technology The Hashemite University Zarqa Jordan Department of Engineering Computing and Mathematical Sciences Lewis University RomeovilleIL United States

Botnets are today recognized as one of the most advanced vulnerability threats. Botnets control a huge percentage of network traffic and PCs. They have the ability to remotely control PCs (zombie machines) by their creator (BotMaster) via Command and Control (C&C) framework. They are the keys to a variety of Internet attacks such as spams, DDOS, and spreading malwares. This study proposes a number of machine learning techniques for detecting botnet assaults via IoT networks to help researchers in choosing the suitable ML algorithm for their applications. Using the BoT-IoT dataset, six different machine learning methods were evaluated: REPTree, RandomTree, RandomForest, J48, metaBagging, and Naive Bayes. Several measures, including accuracy, TPR, FPR, and many more, have been used to evaluate the algorithms’ performance. The six algorithms were evaluated using three different testing situations. Scenario-1 tested the algorithms utilizing all of the parameters presented in the BoT-IoT dataset, scenario-2 used the IG feature reduction approach, and scenario-3 used extracted features from the attacker’s received packets. The results revealed that the assessed algorithms performed well in all three cases with slight differences. © 2023 by the authors of this article. Published under CC-BY.

关键词： Internet of things

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Artificial intelligence in physiological characteristics recognition for internet of things authentication

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Digital Communications and Networks 2024年第3期10卷 740-755页

作者： Zhimin Zhang Huansheng Ning Fadi Farha Jianguo Ding Kim-Kwang Raymond Choo School of Computer and Communication Engineering University of Science and Technology BeijingBeijing100083China Beijing Engineering Research Center for Cyberspace Data Analysis and Applications Beijing100083China Faculty of Informatics Engineering Aleppo UniversityAleppoSyria Department of Computer Science Blekinge Institute of Technology37179KarlskronaSweden Department of Information Systems and Cyber Security University of Texas at San AntonioSan AntonioTX78249-0631USA

Effective user authentication is key to ensuring equipment security,data privacy,and personalized services in Internet of Things(IoT)***,conventional mode-based authentication methods(e.g.,passwords and smart cards)may be vulnerable to a broad range of attacks(e.g.,eavesdropping and side-channel attacks).Hence,there have been attempts to design biometric-based authentication solutions,which rely on physiological and behavioral *** characteristics need continuous monitoring and specific environmental settings,which can be challenging to implement in ***,we can also leverage Artificial Intelligence(AI)in the extraction and classification of physiological characteristics from IoT devices processing to facilitate ***,we review the literature on the use of AI in physiological characteristics recognition pub-lished after *** use the three-layer architecture of the IoT(i.e.,sensing layer,feature layer,and algorithm layer)to guide the discussion of existing approaches and their *** also identify a number of future research opportunities,which will hopefully guide the design of next generation solutions.

关键词： Physiological characteristics recognition Artificial intelligence Internet of things Biological-driven authentication

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Multi-agent QoS-aware autonomic resource provisioning framework for elastic BPM in containerized multi-cloud environment

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Journal of Ambient Intelligence and Humanized Computing 2023年第9期14卷 12895-12920页

作者： Saif, Mufeed Ahmed Naji Niranjan, S.K. Murshed, Belal Abdullah Hezam Al-ariki, Hasib Daowd Esmail Abdulwahab, Hudhaifa Mohammed Department of Computer Applications Sri Jayachamarajendra College of Engineering VTU Mysore India Department of Studies in Computer Science University of Mysore Karnataka Mysore India Department of Computer Science College of Eng. &IT University of Amran Amran Yemen Department of Information Technology Faculty of Engineering and Information Technology Al-ata’a University for Science and Technology Taiz Yemen Department of Computer Networks Engineering and Technologies Sana’a Community College Sana’a Yemen Department of Computer Application Ramaiah Institute of Technology VTU Bangalore India

Cloud computing enables businesses to improve their market competitiveness, enabling instant and easy access to a pool of virtualized and distributed resources such as virtual machines (VM) and containers for executing their business operations efficiently. Though the cloud enables the deployment and management of business processes (BPs), it is challenging to deal with the enormous fluctuating resource demands and ensure smooth execution of business operations in containerized multi-cloud. Therefore, there is a need to ensure elastic provisioning of resources to tackle the over and under-provisioning problems and satisfy the objectives of cloud providers and end-users considering the quality of service (QoS) and service level agreement (SLA) constraints. In this article, an efficient multi-agent autonomic resource provisioning framework is proposed to ensure the effective execution of BPs in a containerized multi-cloud environment with guaranteed QoS. To improve the performance and ensure elastic resource provisioning, autonomic computing is utilized to monitor the resource usage and predict the future resource demands, then resources are scaled based on demand. Initially, the required resources for executing the incoming workloads are identified by clustering the workloads into CPU and I/O intensive, and the local agent achieves this with the help of an initialization algorithm and K-means clustering. Then, the analysis phase predicts the workload demand using the proposed enhanced deep stacked auto-encoder (EDSAE), further, the containers are scaled based on the prediction outcomes, finally, the multi-objective termite colony optimization (MOTCO) algorithm is used by the global agent to find suitable containers for executing the clustered workloads. The proposed framework has been implemented in the Container Cloudsim platform and evaluated using the business workload traces. The overall simulation results proved the effectiveness of the proposed approach compare

关键词： Quality of service

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An Improved Multi-Objective Particle Swarm Optimization Routing on MANET

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computer Systems Science & Engineering 2023年第2期44卷 1187-1200页

作者： G.Rajeshkumar M.Vinoth Kumar K.Sailaja Kumar Surbhi Bhatia Arwa Mashat Pankaj Dadheech Department of Computer Science and Engineering Erode Sengunthar Engineering CollegePerunduraiErode638057Tamil NaduIndia Department of Information Science and Engineering Dayananda Sagar Academy of Technology and ManagementBangalore560082India Department of Master of Computer Applications M.S.Ramaiah Institute of TechnologyBangalore560054India Department of Information Systems College of Computer Science and Information TechnologyKing Faisal UniversityRiyadh11533Saudi Arabia Faculty of Computing and Information Technology King Abdulaziz UniversityRabigh21911Saudi Arabia Department of Computer Science and Engineering Swami Keshvanand Institute of TechnologyManagement&GramothanJaipur302017RajasthanIndia

A Mobile Ad hoc Network(MANET)is a group of low-power con-sumption of wireless mobile nodes that conﬁgure a wireless network without the assistance of any existing infrastructure/centralized *** primary aim of MANETs is to extendﬂexibility into the self-directed,mobile,and wireless domain,in which a cluster of autonomous nodes forms a MANET routing *** Intrusion Detection System(IDS)is a tool that examines a network for mal-icious behavior/policy violations.A network monitoring system is often used to report/gather any suspicious attacks/*** IDS is a software program or hardware system that monitors network/security trafﬁc for malicious attacks,sending out alerts whenever it detects malicious *** impact of Dynamic Source Routing(DSR)in MANETs challenging blackhole attack is investigated in this research *** Cluster Trust Adaptive Acknowledgement(CTAA)method is used to identify unauthorised and malfunctioning nodes in a MANET *** system is active and provides successful delivery of a data packet,which implements Kalman Filters(KF)to anticipate node ***,KF is used to eliminate synchronisation errors that arise during the sending and receiving *** order to provide an energy-efﬁcient solution and to minimize network trafﬁc,route optimization in MANET by using Multi-Objective Particle Swarm Optimization(MOPSO)technique to determine the optimal num-ber of clustered MANET along with energy dissipation in *** to the researchﬁndings,the proposed CTAA-MPSO achieves a Packet Delivery Ratio(PDR)of 3.3%.In MANET,the PDR of CTAA-MPSO improves CTAA-PSO by 3.5%at 30%malware.

关键词： MANET intrusion detection system cluster kalmanﬁlter dynamic source routing multi-objective particle swarm optimization packet delivery ratio

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Enhancing Phishing URL Detection: A Comparative Study of Machine Learning Algorithms 23

Enhancing Phishing URL Detection: A Comparative Study of Mac...

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2023 Asia Conference on Artificial Intelligence, Machine Learning and Robotics, AIMLR 2023

作者： Alsarhan, Ayoub Igried, Bashar Bani Saleem, Raad Mohammad Alauthman, Mohammad Aljaidi, Mohammad Department of Information Technology Faculty of Prince Al-Hussein Bin Abdallah II for Information Technology The Hashemite University Zarqa Jordan Department of Computer Science and Applications Faculty of Prince Al-Hussein Bin Abdallah II for Information Technology TheHashemite University Zarqa Jordan Department of Information Security University of Petra Amman Jordan Department of Computer Science Faculty of Information Technology Zarqa University Zarqa Jordan

ISBN: (纸本)9798400708312

Phishing constitutes a significant threat in the digital world, often exploiting human vulnerabilities to illicitly obtain sensitive data such as personal credentials, financial details, and private information. Misusing this information results in substantial financial loss and personal harm to victims. This study introduces an innovative approach to mitigate the risk of phishing attacks by employing machine learning algorithms to detect phishing URLs. The proposed method applies a suite of algorithms, including j48, Naïve Bayes, JRip, and Decision Table, to a robust dataset of 11,430 URLs, each with 87 extracted features. The results reveal the considerable potential of machine learning in identifying phishing threats. Furthermore, the study explores a novel server-side analysis concept where the server scrutinizes links transmitted via emails or social communication platforms such as WhatsApp, Messenger, and Instagram. The application of phishing detection algorithms filters and prevents the delivery of phishing links, thus reducing the potential harm to users. This research is poised to significantly contribute to cybersecurity by enhancing phishing detection mechanisms' accuracy and efficiency. © 2023 ACM.

关键词： Sensitive data

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Integrating Blockchain technology into Healthcare Through an Intelligent Computing Technique

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computers, Materials & Continua 2022年第2期70卷 2835-2860页

作者： Asif Irshad Khan Abdullah Saad Al-Malaise ALGhamdi Fawaz Jaber Alsolami Yoosef B.Abushark Abdulmohsen Almalawi Abdullah Marish Ali Alka Agrawal Rajeev Kumar Raees Ahmad Khan Department of Computer Science Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Information Systems Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Information Technology Babasaheb Bhimrao Ambedkar UniversityLucknow226025Uttar PradeshIndia Department of Computer Applications Shri Ramswaroop Memorial UniversityBarabanki225003Uttar PradeshIndia

The blockchain technology plays a significant role in the present era of information *** the last few years,this technology has been used effectively in several *** has already made significant differences in human life,as well as is intended to have noticeable impact in many other domains in the forthcoming *** rapid growth in blockchain technology has created numerous new possibilities for use,especially for healthcare *** digital healthcare services require highly effective security methodologies that can integrate data security with the availablemanagement *** test and understand this goal of security management in Saudi Arabian perspective,the authors performed a numerical analysis and simulation through a multi criteria decision making approach in this *** authors adopted the fuzzy Analytical Hierarchy Process(AHP)for evaluating the effectiveness and then applied the fuzzy Technique forOrder of Preference by Similarity to Ideal Solution(TOPSIS)technique to simulate the validation of *** eliciting highly corroborative and conclusive results,the study referred to a real time project of diabetes patients’management application of Kingdom of Saudi Arabia(KSA).The results discussed in this paper are scientifically proven and validated through various analysis *** the present study can be a credible basis for other similar endeavours being undertaken in the domain of blockchain research.

关键词： Blockchain technology data management fuzzy logic AHP TOPSIS

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Implementation of Text Pre-Processing in Gujarati Text-to-Speech Conversion

Implementation of Text Pre-Processing in Gujarati Text-to-Sp...

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Mobile Computing and Sustainable Informatics (ICMCSI), International Conference on

作者： Vishal Narvani Harshal Arolkar Faculty of Computer Applications and Information Technology GLS University Ahmedabad India

Text-to-speech (TTS) systems on computers aim to transform written text into a natural-sounding auditory format. They are specifically designed for the Gujarati language, allowing users to input text and receive corresponding spoken output. This technology is particularly promising in enhancing content accessibility for individuals with literacy challenges or visual impairments, aiding effective information perception. Despite advancements, TTS systems face difficulties in generating emotionally expressive speech resembling human communication. Researchers are actively working on incorporating emotions and sensations into TTS systems, underscoring the potential for further research to enhance their effectiveness. The TTS process involves two phases: i) Pre-processing and normalization of Gujarati text, and ii) Converting the processed Gujarati text into synthesized speech using digital signal processing. This study concentrates on the pre-processing and normalization aspects of Gujarati text-to-speech conversion.

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Comparative Analysis of Outcomes of Tesseract OCR for Different Languages

Comparative Analysis of Outcomes of Tesseract OCR for Differ...

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Intelligent Communication Technologies and Virtual Mobile Networks (ICICV), International Conference on

作者： Kartik Joshi Harshal Arolkar Faculty of Computer Applications and Information Technology GLS University Ahmedabad Gujarat

Hindi and Gujarati are Devanagari scripts that have contributed to the culture of India in the form of literature and human interactions. Due to aging, the textually rich literature of these languages may not exist for future generations. To preserve them, the technological solution known as an optical character recognition engine is used. Tesseract OCR engine supports the conversion of Hindi and Gujarati language-based images to equivalent text outputs. Major challenge in generating better quality of text outputs is acquisition of high resolution and noise free images. This study has compared the working of the Tesseract OCR engine for the Gujarati and Hindi languages and determined how effective it is for real-time applications.

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Performance of VM in SDN-Assisted Cloud Data Center with Working Vacations 7

Performance of VM in SDN-Assisted Cloud Data Center with Wor...

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7th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2023

作者： Barik, Lalbihari Dash, Bibhuti Bhusan Ranjan Mishra, Manoj Rout, Suchismita De, Utpal Chandra Shekhar Patra, Sudhansu King Abdul Aziz University Faculty of Computing and Information Technology in Rabigh Department of Information Systems Saudi Arabia KIIT Deemed to Be University School of Computer Applications Bhubaneswar India

ISBN: (纸本)9798350341485

The Internet of Things (IoT)'s exponentially growing network traffic has a significant impact on energy utilization and network performance. A recommended power-saving traffic control approach for cloud data centers uses virtual machines (VM) vacation. The essential idea is that the (VM) in the cloud system may go on vacation through the utilization of software-defined network (SDN) paradigm. The reduction of active VMs while upholding QoS standards is the aim for the QoS optimization. The M/M/1 queue model with working vacations (WV) and customer requests impatience behavior is examined in this research study. This study considers the possibility that the customer's impatience is caused by a WV. The requests to be served at the cloud data center by the VMs are at slow rate than normal during WVs and are prone to get impatient. An "impatience timer"that follows exponential distribution is activated whenever customer's request reached to the cloud system and discovers that the VM is on WV. A customer leaves the queue and does not come back if their service is not finished before their timer runs out. The system's numerous performance parameters such as average system size, the sojourn time, the percentage of customer's request being served, and the abandonment frequency are calculated. Finally, some numerical findings are obtained to show how some specific parameters affect the system performance indicators. © 2023 IEEE.

关键词： Energy utilization

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Optimizing Deep Learning Models through Spatial Attentive Architectural Enhancements and Binarization Techniques 5

Optimizing Deep Learning Models through Spatial Attentive Ar...

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5th International Conference on Emerging Systems and Intelligent Computing, ESIC 2025

作者： Saha, Bishnu Pada Dash, Satya Ranjan Bandyopadhyay, Mainak Barra, Paola Hoang, Vinh Truong Kiit D.U. School of Computer Engineering Bhubaneswar India Kiit D.U. School of Computer Applications Bhubaneswar India University of Naples 'Parthenope' Department of Science and Technology Italy Hcmc Open University Faculty of Information Technology Viet Nam

ISBN: (纸本)9798331522100

Attention mechanisms in vision applications of the computer world dynamically adjust feature weights to amplify the model's capability to perceive information. This adaptive recalibration improves the overall effectiveness of the model. Spatial attention has seen widespread use to boost the efficiency of convolutional neural networks by enhancing their ability to model spatial information. This approach significantly improves CNNs' spatial representation capabilities. Along this line of research, a novel binarized-attention module is drafted by taking spatial attention into consideration. Binary neural networks, which significantly reduce storage and computational requirements, are a promising approach for deploying deep models on resource-constrained devices. This technique offers efficiency in scenarios with limited resources. In this paper, we hereby implemented our algorithm of binarizing the spatial attention module and obtained diverse findings, demonstrating significant improvements in accuracies with fewer classes and degradation of accuracies with higher classes. Various experimental findings reveal that our technique delivers better accuracy with dataset having 10 classes but low accuracy with the one having 100 classes. Additionally, architectural optimizations, including the adoption of deeper networks like ResNet50, contribute to better handling of complex data patterns. While binarization offers computational efficiency, it often results in a trade-off with accuracy. However, selective binarization of specific network layers can mitigate this issue, maintaining a balance between performance and resource utilization. This study provides insights into the synergistic effects of these techniques, offering a pathway for developing efficient and accurate deep learning models suitable for deployment in resource-constrained environments. © 2025 IEEE.

关键词： Convolutional neural networks

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