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Wireless Network Security Using Load Balanced Mobile Sink Technique

Intelligent Automation & Soft Computing 2023年第2期35卷 2135-2149页

作者： Reem Alkanhel Mohamed Abouhawwash S.N.Sangeethaa K.Venkatachalam Doaa Sami Khafaga Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh 11671Saudi Arabia Department of Mathematics Faculty of ScienceMansoura UniversityMansoura35516Egypt Department of Computational Mathematics Scienceand Engineering(CMSE)Michigan State UniversityEast Lansing48824MIUSA Department of Computer Science and Engineering Bannari Amman Institute of TechnologySathyamangalam638401India Department of Computer Science and Engineering CHRIST(Deemed to be University)Bengaluru560074India Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh 11671Saudi Arabia

Real-time applications based on Wireless Sensor Network(WSN)tech-nologies are quickly increasing due to intelligent *** the most signiﬁcant resources in the WSN are battery power and *** stra-tegies improve the power factor and secure the WSN *** takes more electricity to forward data in a *** numerous clustering methods have been developed to provide energy consumption,there is indeed a risk of unequal load balancing,resulting in a decrease in the network’s lifetime due to network inequalities and less *** possibilities arise due to the cluster head’s limited life *** cluster heads(CH)are in charge of all activities and con-trol intra-cluster and inter-cluster *** proposed method uses Lifetime centric load balancing mechanisms(LCLBM)and Cluster-based energy optimiza-tion using a mobile sink algorithm(CEOMS).LCLBM emphasizes the selection of CH,system architectures,and optimal distribution of *** addition,the LCLBM was added with an assistant cluster head(ACH)for load *** consumption,communications latency,the frequency of failing nodes,high security,and one-way delay are essential variables to consider while evaluating *** will choose a cluster leader based on the inﬂuence of the fol-lowing parameters on the energy balance of *** to simulatedﬁnd-ings,the suggested LCLBM-CEOMS method increases cluster head selection self-adaptability,improves the network’s lifetime,decreases data latency,and bal-ances network capacity.

关键词： Wireless sensor network load balancing mechanism optimization power consumption network’s lifetime

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Efficient and Secure Multiparty Querying over Federated Graph Databases 13

Efficient and Secure Multiparty Querying over Federated Grap...

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13th International Conference on Data science, technology and Applications, DATA 2024

作者： Aljuaid, Nouf Lisitsa, Alexei Schewe, Sven Department of Information Technology Taif University Saudi Arabia Department of Computer Science University of Liverpool Liverpool United Kingdom

ISBN: (纸本)9789897587078

We present a system for efficient privacy-preserving multi-party querying (PPMQ) over federated graph databases. This framework offers a customisable and adaptable approach to privacy preservation using two different security protocols. The first protocol utilises standard secure multiparty computation (SMPC) protocols on the client side, enabling computations to be conducted on data without exposing the data itself. The second protocol is implemented on the server side using a combination of an SMPC protocol to prevent exposing the data to the clients and the use of encrypted hashing to prevent exposing the data to the server. We have conducted experiments to compare the efficiency of our PPMQ system with Neo4j Fabric, the off-the-shelf solution for querying federated graph databases, and with two previous systems, SMPQ and Conclave for secure multiparty querying. The results demonstrated that the execution times and overheads of PPMQ are comparable to those using Neo4j Fabric. Notably, our results reveal that the execution times and overheads of PPMQ outperform both SMPQ and Conclave, showcasing the better efficiency of our approach in preserving privacy within federated graph databases. © 2024 by SCITEPRESS – science and technology Publications, Lda.

关键词： Structured Query Language

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Utilizing Machine Learning Algorithms for Effectively Detection IoT DDoS Attacks 1st

Utilizing Machine Learning Algorithms for Effectively Detect...

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1st International Conference on Advances in Computing Research, ACR’23

作者： Mughaid, Ala Alqahtani, Ali AlZu’bi, Shadi Obaidat, Ibrahim Alqura’n, Rabee AlJamal, Mahmoud AL-Marayah, Raid Department of Information Technology Faculty of Prince Al-Hussien Bin Abdullah for IT The Hashemite University Zarqa Jordan Department of Networks and Communication Engineering College of Computer Science and Information Systems Najran University Najran Saudi Arabia Computer Science Department Faculty of Science and Information Technology Al Zaytoonah University of Jordan Amman Jordan

ISBN: (纸本)9783031337420

One of the most critical features of the Internet of Things (IoT) is that it allows a massive number of devices to communicate with each other at the same time, for example (cars, smoke alarms, watches, glasses, web cameras, etc.). Given that they can connect to the Internet, the huge demand to convert systems into smart systems that can be controlled remotely increases the chances of cyber-attacks that threaten security and availability becoming more frequent, severe, and in a different form. Distributed Denial of Service (DDoS) attacks have become the most common threats in IoT. These attacks make IoT systems unreliable because complex DDoS attacks cannot be detected by traditional security countermeasures such as firewalls and intrusion detection systems (IDS). In this research, we proposed a model dedicated to work on prediction and detection of DDoS attacks on networks IoT, where we run this model on a NF-BoT-IoT-v2 dataset, which contains 37,763,497 records divided into five categories (Benign, Reconnaissance, DDoS, DoS, and Theft), and then we process our dataset to be more business friendly so it only has two categories (100,000 records broken down into 23,906 benign attacks and 76,094 DDoS attacks). This study used WEKA and MATLAB tools to perform the classification efficiently and find the most important features through feature selection. We used a decision tree (J48) machine learning classifier, a Naive Bayes (NB) classifier, and a random forest (RF) classifier. However, the results were as expected and improved accuracy and efficiency. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Denial-of-service attack

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Urban Data Management using Cloud Computing and IoT

Urban Data Management using Cloud Computing and IoT

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2023 computer Applications and Technological Solutions, CATS 2023

作者： Hasan, Muhammad Zulkifl Hussain, Muhammad Zunnurain Mughees, Ahmed Javaid, Muhammad Ahsan Noor, Muzayan Nosheen, Summaira Mubarak, Zaima Chuhan, Saad Hussain Mustafa, Muzzamil University of Central Punjab Faculty of Information Technology Department of Computer Science Lahore Pakistan Bahria University Lahore Campus Department of Computer Science Lahore Pakistan University of Punjab Department of Economics Information Technology Lahore Pakistan National College of Business Administration & Economics Department of Computer Science DHA Campus Lahore Pakistan

ISBN: (纸本)9798350383881

Considering quick growth of technical knowledge, it is pertinent for the metropolitan stake-holders to improve the urban living standards by implementing effective policies assistance from data analysis using the Internet of Things (IoT). The IoT involves usage of various sensors for recording data that corresponds to different aspects of urban life, such as health, transport and governance. The interconnection of these sensors enables data analysts and governing bodies to decipher patterns in data, and in turn better understand the urban environments for efficient city planning, eventually creating smart cities. The design and implementation of smart cities is a hot topic nowadays in the field of urban development. This paper reviews the modern technologies that are being used to implement the smart city concept, in turn making it sustainable and more environment-friendly. The necessity of having a variety of sensing platforms for sustainable urban development is discussed. There is a discussion on different data collection and processing frameworks being deployed in smart cities, highlighting the key benefits of utilizing modern technologies like big data, cloud computing, machine learning (ML) and artificial intelligence (AI) with the IoT. The challenges encountered during these processes are also examined, which create new research opportunities. This paper will serve as a guideline for researchers working in the field of sustainable urban development using IoT along with the other modern technologies. © 2023 IEEE.

关键词： Internet of things

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DeepCNN:Spectro-temporal feature representation for speech emotion recognition

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CAAI Transactions on Intelligence technology 2023年第2期8卷 401-417页

作者： Nasir Saleem Jiechao Gao Rizwana Irfan Ahmad Almadhor Hafiz Tayyab Rauf Yudong Zhang Seifedine Kadry Department of Electrical Engineering Faculty of Engineering and TechnologyGomal UniversityD.I.KhanPakistan Department of Computer Science University of VirginiaCharlottesvilleVirginiaUSA Department of Information Technology College of Computing and Information Technology at KhulaisUniversity of JeddahJeddahSaudi Arabia Department of Computer Engineering and Networks College of Computer and Information SciencesJouf UniversitySkaka AljoufSaudi Arabia Centre for Smart Systems AI and CybersecurityStaffordshire UniversityStoke‐on‐TrentUK School of Computing and Mathematical Sciences University of LeicesterLeicesterUK Department of Applied Data Science Noroff University CollegeKristiansandNorway Artificial Intelligence Research Center(AIRC) Ajman UniversityAjmanUnited Arab Emirates Department of Electrical and Computer Engineering Lebanese American UniversityByblosLebanon

Speech emotion recognition(SER)is an important research problem in human-computer interaction *** representation and extraction of features are significant challenges in SER *** the promising results of recent studies,they generally do not leverage progressive fusion techniques for effective feature representation and increasing receptive *** mitigate this problem,this article proposes DeepCNN,which is a fusion of spectral and temporal features of emotional speech by parallelising convolutional neural networks(CNNs)and a convolution layer-based *** parallel CNNs are applied to extract the spectral features(2D-CNN)and temporal features(1D-CNN)representations.A 2D-convolution layer-based transformer module extracts spectro-temporal features and concatenates them with features from parallel *** learnt low-level concatenated features are then applied to a deep framework of convolutional blocks,which retrieves high-level feature representation and subsequently categorises the emotional states using an attention gated recurrent unit and classification *** fusion technique results in a deeper hierarchical feature representation at a lower computational cost while simultaneously expanding the filter depth and reducing the feature *** Berlin Database of Emotional Speech(EMO-BD)and Interactive Emotional Dyadic Motion Capture(IEMOCAP)datasets are used in experiments to recognise distinct speech *** efficient spectral and temporal feature representation,the proposed SER model achieves 94.2%accuracy for different emotions on the EMO-BD and 81.1%accuracy on the IEMOCAP dataset *** proposed SER system,DeepCNN,outperforms the baseline SER systems in terms of emotion recognition accuracy on the EMO-BD and IEMOCAP datasets.

关键词： decision making deep learning

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Predicting GameFi's Daily Market Direction Using Support Vector Machine 20

Predicting GameFi's Daily Market Direction Using Support Vec...

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20th International Joint Conference on computer science and Software Engineering, JCSSE 2023

作者： Phumphuang, Prathan Jareevongpiboon, Wirat Thammasat University Faculty of Science and Technology Department of Computer Science Pathum Thani Thailand

ISBN: (纸本)9798350300505

This research uses support vector machines to forecast the direction of the GameFi market on a daily basis. GameFi is a blockchain-based product that combines gaming and trading. The customer can profit from any in-game activity. Machine learning is a computational method that is able to forecast the future using historical data or attribute information such as open, high, low, close, volume, and market capitalization. Like many researchers who are working to develop accurate machine learning models that can forecast the market value of stocks, commodities like gold and gas, and cryptocurrencies, the main goal of this study is to use SVM to predict the daily direction of GameFi's price. The experimental result shows that SVM's prediction performance is best at 57.6%. © 2023 IEEE.

关键词： Forecasting

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An anonymous and fair auction system based on blockchain

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Journal of Supercomputing 2023年第13期79卷 13909-13951页

作者： Ye, Zongli Chen, Chin-Ling Weng, Wei Sun, Hongyu Tsaur, Woei-Jiunn Deng, Yong-Yuan School of Computer and Information Engineering Xiamen University of Technology Xiamen361024 China School of Information Engineering Changchun Sci-Tech University Changchun130600 China Department of Computer Science and Information Engineering Chaoyang University of Technology Taichung City413310 Taiwan Department of Computer Science Jilin Normal University Siping136000 China State Key Laboratory of Numerical Simulation Siping136000 China Computer Center National Taipei University New Taipei City237303 Taiwan Department of Computer Science and Information Engineering National Taipei University New Taipei City237303 Taiwan

Consumer approval of the online auction paradigm is demonstrated by rapidly expanding online auction transaction volumes. Bidders can access a wider variety of products, and sellers can reach a larger audience through online auction platforms. However, privacy concerns have reduced users’ trust in auction platforms, and complicated disputes have led consumers to lose faith in online auctions, negatively impacting market growth. The emergence of blockchain technology has brought a new approach to solving these problems. Blockchain is a new generation of information technology that is decentralized, transparent, traceable, anti-tampering, and unforgeable, following big data, cloud computing, and artificial intelligence. In this paper, we present a blockchain-based online auction solution as well as an auction protocol that uses ring signatures to guarantee anonymity. Three features summarize this essay: (1) Using blockchain technology to ensure that the auction transaction process is transparent, traceable, tamper-proof, and unforgeable. (2) The ring signature-based anonymous auction protocol allows for concealing bidders’ public keys in a group of public keys. Because malicious individuals are prevented from evaluating data by tracking numerous transactions of a single public key, this significantly protects bidders’ privacy. (3) The proposed scheme effectively resolves disputes that may arise during the auction. © 2023, The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature.

关键词： Blockchain

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Increasing Crop Quality and Yield with a Machine Learning-Based Crop Monitoring system

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computers, Materials & Continua 2023年第8期76卷 2401-2426页

作者： Anas Bilal Xiaowen Liu Haixia Long Muhammad Shafiq Muhammad Waqar College of Information Science Technology Hainan Normal UniversityHaikou571158China School of Information Engineering Qujing Normal UniversityQujing655011China Department of Computer Science COMSATS UniversityIslamabad45550Pakistan

Farming is cultivating the soil,producing crops,and keeping *** agricultural sector plays a crucial role in a country’s economic *** research proposes a two-stage machine learning framework for agriculture to improve efficiency and increase crop *** the first stage,machine learning algorithms generate data for extensive and far-flung agricultural areas and forecast *** recommended crops are based on various factors such as weather conditions,soil analysis,and the amount of fertilizers and pesticides *** the second stage,a transfer learningbased model for plant seedlings,pests,and plant leaf disease datasets is used to detect weeds,pesticides,and diseases in the *** proposed model achieved an average accuracy of 95%,97%,and 98% in plant seedlings,pests,and plant leaf disease detection,*** system can help farmers pinpoint the precise measures required at the right time to increase yields.

关键词： Machine learning computer vision trends in smart farming precision agriculture Agriculture 4.0

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On Big Data Forensic and Forensic Cloud Environment

On Big Data Forensic and Forensic Cloud Environment

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2022 International Conference on Smart Applications, Communications and Networking, SmartNets 2022

作者： Tabona, Oteng Ramokapane, Kopo M. Maupong, Thabiso M. Higher Colleges of Technology Faculty of Computer and Information Systems Abu Dhabi United Arab Emirates University of Bristol Department of Computer Science Bristol United Kingdom Botswana International University of Science and Technology Department of Computer Science and Information System Palapye Botswana

ISBN: (纸本)9781665487580

The number of electronic devices involved in cyber related crimes has increased significantly in the last few years. This is primarily due to advancements in technology and increased accessibility to devices. This poses significant challenges with respect to forensic investigations as investigators are faced with processing huge volumes of data from various sources within a limited time. Due to the urgency, frequency, and magnitude of some cases, investigators may miss deadlines, and crucial leads. In this paper, we consider these above stated challenges and propose a new definition for big data forensics, which recognises the time aspect and variety of sources when carrying our digital forensics. This definition will help digital forensic tool designers consider these critical aspects and design comprehensive tools to combat the limitations of the current state-of-the-art. We review an existing tool, the Forensic Cloud Environment (FCE), against the proposed definition. We show how its design leverages the power of Hadoop Distributed File system (HDFS), HBase, and MapReduce to provide a comprehensive solution for big data forensics. In conclusion, we provide an argument for FCE as a solution for resolving big data forensic challenges based on general Scientific Working Group on Digital Evidence (SWGDE) forensic principles. © 2022 IEEE.

关键词： Big data

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A Machine Learning based Intelligent Inventory system for Construction Industry

A Machine Learning based Intelligent Inventory System for ...

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International Conference on Interdisciplinary Approaches in Civil Engineering for Sustainable Development, IACESD 2023

作者： Kumar, M. Manoj Rohith, D. Kumar, T. G. Mohan Rahul, R. Koustub, B. S. Krishna Department of Computer Science and Engg Jyothy Institute of Technology Karnataka Bengaluru India Department of Information Science and Engg Nitte Meenakshi Institute of Technology Karnataka Bengaluru India

ISBN: (纸本)9789819700714

Inventory management in the construction industry is a complex and time-consuming process that requires careful attention to detail to ensure materials and supplies are properly managed within budget and on schedule. Effective inventory management involves tracking, organizing, and optimizing construction equipment and supplies. Machine learning algorithms can play a significant role in enhancing inventory management in the construction industry. These algorithms can be used for demand forecasting, optimizing inventory levels, predicting maintenance needs, selecting suppliers, and generating reports. By analyzing historical data on material usage and equipment expenses, machine learning algorithms can predict future demand, optimize inventory levels in real-time, anticipate equipment maintenance requirements, proactively schedule maintenance, identify reliable and cost-effective suppliers, and generate reports for inventory tracking. Implementing machine learning in inventory management can help construction companies reduce costs, improve operational efficiency, enhance cash flow, and streamline procurement processes. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Construction industry

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