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International Conference on Informatics and Computing (ICIC)

作者： Irma Yunita Nasution Muhammad Zarlis Maya Silvi Lydia Faculty of Computer Science and Information Technology Universitas Sumatera Utara Medan Indonesia Departement of Information System Management BINUS Postgraduate Universitas Bina Nusantara Jakarta Indonesia

ISBN: (数字)9798331517601

ISBN: (纸本)9798331517618

This study focuses on enhancing signature authenticity recognition by leveraging Convolutional Neural Networks (CNN) combined with Principal Component Analysis (PCA) and Directed Acyclic Graphs (DAG). While CNNs have demonstrated promising results, discrepancies between training and test accuracies remain a challenge. By integrating PCA for dimensionality reduction and DAG for optimizing training, this research aims to improve efficiency and accuracy. Despite achieving $\mathbf{1 0 0 \%}$ training accuracy, the test accuracy reached only $\mathbf{9 7 \%}$, indicating the necessity for refining parameters further. This study emphasizes the value of systematic methodologies and comprehensive parameter exploration to achieve substantial advancements in signature recognition accuracy.

关键词： Training Dimensionality reduction Directed acyclic graph Accuracy systematics Fluctuations Refining Convolutional neural networks Informatics Principal component analysis

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Optimal Bottleneck-Driven Deep Belief Network Enabled Malware Classification on IoT-Cloud Environment

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computers, Materials & Continua 2023年第2期74卷 3101-3115页

作者： Mohammed Maray Hamed Alqahtani Saud S.Alotaibi Fatma S.Alrayes Nuha Alshuqayran Mrim M.Alnfiai Amal S.Mehanna Mesfer Al Duhayyim Department of Information Systems College of Computer ScienceKing Khalid UniversityAbhaSaudi Arabia Department of Information Systems College of Computer ScienceCenter of Artificial IntelligenceUnit of CybersecurityKing Khalid UniversityAbhaSaudi Arabia Department of Information Systems College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of information systems College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Information Systems College of Computer and Information SciencesImam Mohammad Ibn Saud Islamic UniversitySaudi Arabia Department of Information Technology College of Computers and Information TechnologyTaif UniversityP.O.Box 11099Taif21944Saudi Arabia Department of Digital Media Faculty of Computers and Information TechnologyFuture University in EgyptNew Cairo11845Egypt Department of Computer Science College of Sciences and Humanities-AflajPrince Sattam Bin Abdulaziz UniversitySaudi Arabia

Cloud Computing(CC)is the most promising and advanced technology to store data and offer online services in an effective *** such fast evolving technologies are used in the protection of computerbased systems from cyberattacks,it brings several advantages compared to conventional data protection *** of the computer-based systems that effectively protect the data include Cyber-Physical systems(CPS),Internet of Things(IoT),mobile devices,desktop and laptop computer,and critical *** software(malware)is nothing but a type of software that targets the computer-based systems so as to launch cyberattacks and threaten the integrity,secrecy,and accessibility of the *** current study focuses on design of Optimal Bottleneck driven Deep Belief Network-enabled Cybersecurity Malware Classification(OBDDBNCMC)*** presentedOBDDBN-CMCmodel intends to recognize and classify the malware that exists in IoT-based cloud *** attain this,Zscore data normalization is utilized to scale the data into a uniform *** addition,BDDBN model is also exploited for recognition and categorization of *** effectually fine-tune the hyperparameters related to BDDBN model,GrasshopperOptimizationAlgorithm(GOA)is *** scenario enhances the classification results and also shows the novelty of current *** experimental analysis was conducted upon OBDDBN-CMC model for validation and the results confirmed the enhanced performance ofOBDDBNCMC model over recent approaches.

关键词： Malware detection security Internet of Things cloud computing machine learning parameter adjustment

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Multi-Agent Based Stochastic Dynamical Model to Measure Community Resilience

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Journal of Social Computing 2022年第3期3卷 262-286页

作者： Jaber Valinejad Lamine Mili C.Natalie Van Der Wal Data and System Science Lab Harvard Medical SchoolHarvard UniversityCambridgeMA 02138USA Bradley Department of Electrical and Computer Engineering Virginia TechNorthern Virginia CenterFalls ChurchVA 22043USA Department of Multi-Actor Systems Faculty of TechnologyPolicy and ManagementDelft University of TechnologyDelft2600 AAthe Netherlands.

Emergency services and utilities need appropriate planning tools to analyze and improve infrastructure and community resilience to *** as a key metric of community resilience is the social well-being of a community during a disaster,which is made up of mental and physical social *** factors influencing community resilience directly or indirectly are emotional health,emergency services,and the availability of critical infrastructures services,such as food,agriculture,water,transportation,electric power,and communications *** turns out that in computational social science literature dealing with community resilience,the role of these critical infrastructures along with some important social characteristics is not *** address these weaknesses,we develop a new multi-agent based stochastic dynamical model,standardized by overview,design concepts,details,and decision(ODD+D)protocol and derived from neuro-science,psychological and social sciences,to measure community resilience in terms of mental and physical *** this model,we analyze the micro-macro level dependence between the emergency services and power systems and social characteristics such as fear,risk perception,informationseeking behaviour,cooperation,flexibility,empathy,and experience,in an artificial ***,we simulate this model in two case studies and show that a high level of flexibility,experience,and cooperation enhances community *** for both theory and practice are discussed.

关键词： community resilience collective behavior emergency services power systems critical infrastructures artificial society overview,design concepts,details,and decision(ODD+D) cyber-physical-social system

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An Enhanced Method for Detecting Attack in Collaborative Recommender system 1

An Enhanced Method for Detecting Attack in Collaborative Rec...

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1st International Conference in Advanced Innovation on Smart City, ICAISC 2023

作者： Zayed, Reda A. Hefny, Hesham A. Ibrahim, Lamiaa F. Salman, Hesham A. Cairo University Faculty of Graduate Studies for Statistical Research Computer Scinces Dep. Cairo Egypt Everyone's Smart University College of Informatics United Arab Emirates Higher Institute for Computers & Information Technology ElShorouk Management Information System Dep. Cairo Egypt

ISBN: (纸本)9781665472753

In recent decades, the advent of digital information services by YouTube, Amazon, Netflix, and many other web services of this kind have made recommendation systems more and more ubiquitous in our lives. rice field. The system suggests and recommends articles to the user that may interest the user in online advertising (recommending and suggesting appropriate content to the user that matches the user's tastes and activities). Recommendation systems have become an integral part of our daily online journeys. The quality of predictions is degraded by the attackers by injection of fake profiles. therefore, the shilling attacks detection are necessary. thus, various shilling attacks detection techniques proposed. In this paper, we introduce an enhanced technique for detecting shilling attacks in collaborative recommender system using supervised learning techniques. The proposed method results show that getting better accuracy when we employee ensemble learning algorithm. © 2023 IEEE.

关键词： Recommender systems

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Chiang Rai Phra That Nine Choms Attraction Promoting Mobile Application for Cultural Tourism

Chiang Rai Phra That Nine Choms Attraction Promoting Mobile ...

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International Conference on Electrical Engineering/Electronics, computer, Telecommunications and Information technology, ECTI-CON

作者： Kamonlak Chaidee Kanchana Boontasri Ekkachai Chaidee Wiwat Wongchai Suphakorn Phamon Suruswadee Preedee Department of Computer Information System Faculty of Business Administration And Liberal Arts Rajamangala University of Technology Lanna Chiangrai Chiang Rai Thailand

This study aimed to create a mobile application to promote and guide the cultural tourism attraction Phra That Nine Choms in Chiang Rai Province, Thailand. The critical information about Phra That Nine Choms is gathered and analyzed to identify the problem and user requirements. The operation of the mobile application is designed using a use case diagram, class diagram, and sequence diagram. The Java programming language creates the mobile application through the Eclipse program, Android SDK, and JAVA JDK development tools. 95 Thai and foreign tourists tested the resulting application. Their satisfaction was evaluated through a questionnaire and analyzed. The interpreting results showed that the mobile application is suitable for providing travel recommendations and can serve as a guide for future development to support tourism activities, particularly for cultural and local attractions.

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Transient Session Key Derivation Protocol for Key Escrow Prevention in Public Key Infrastructure 1st

Transient Session Key Derivation Protocol for Key Escrow Pre...

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1st EAI International Conference on Internet of Everything, IoECon 2022

作者： Nyangaresi, Vincent Omollo Abduljabbar, Zaid Ameen Maolood, Ismail Yaqub Sibahee, Mustafa A. Al Ma, Junchao Aldarwish, Abdulla J. Y. Faculty of Biological and Physical Sciences Tom Mboya University Homabay40300 Kenya Department of Computer Science College of Education for Pure Sciences University of Basrah Basrah61004 Iraq Shenzhen Institute Huazhong University of Science and Technology Shenzhen518118 China – System Information Ministry of Higher Education and Scientific Research Erbil Iraq College of Big Data and Internet Shenzhen Technology University Shenzhen518118 China Computer Technology Engineering Department Iraq University College Basrah Iraq

ISBN: (纸本)9783031252211

The Internet of Things (IoT) devices have been deployed to realize smart environments such as smart cities, smart homes, smart health and smart grids. In these domains, the IoT devices collect and forward high volumes of sensitive and private data. It is therefore important that security schemes be developed to protect the exchanged data. In this regard, a myriad of authentication protocols have been developed over the recent past. However, these schemes deploy cryptographic primitives that result in extremely high communication, storage and computation complexities. In addition, some of these protocols still have numerous security and privacy issues that render them unsuitable for deployment in an IoT environment. For instance, untraceability, anonymity, key escrow problems and attack vectors such as de-synchronization and forgery attacks are frequently ignored. In this paper, a transient session key derivation protocol is developed to address some of these security and efficiency challenges. The security analysis executed shows that this protocol offers untraceability, device anonymity and perfect forward key secrecy. In addition, it is robust against de-synchronization, known secret key leakage, eavesdropping and forgery attacks. In terms of operational efficiency, this protocol incurs the lowest computation and communication complexities. © 2023, ICST Institute for computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： Authentication

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IoT Network Forensics based on Transport Layer

IoT Network Forensics based on Transport Layer

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computer Applications (ICCA), IEEE Conference on

作者： Soe Kalayar Naing Su Thet Oo Hnin Wit Yi Oo Phyo Yatanar Lin Khin Mya Nwe Mie Mie Su Thwin Faculty of Computer Science University of Computer Studies Yangon Yangon Myanmar University of Computer Studies Yangon Yangon Myanmar Faculty of Computer System and Technology University of Computer Studies Yangon Yangon Myanmar Information Technology Support and Maintenance University of Computer Studies Yangon Yangon Myanmar Cyber Security Lab University of Computer Studies Yangon Yangon Myanmar

The IoT is concerned in every part of the human beings such as medical care, education, smart cities, home automation, and many business fields. While the IoT gives a myriad of benefits, security remains the biggest issues for using of many IoT applications in various environments. The emergence of new technologies in various fields also pose challenges in the field of forensic researches. It is said that Device Level forensics, Network Level forensics, and Cloud Level forensics are the three types of IoT forensics levels. Among them, Network Level forensics shows all kinds of communication networks that connected to IoT devices over the Internet. In this study, detecting and analyzing TCP-SYN flooding attacks and UDP flooding attacks based on Transport Layer (one type of DoS attacks), network forensics investigation over the traffics are presented. Capturing TCP-SYN flooding attacks and UDP flooding attacks and analyzing them with IO graphs by using Wireshark Network Analysis Tool is also described in this paper.

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Decision Support system in Determining the Achievement of Widyaiswara Value Using WASPAS and TOPSIS Algorithm 1

Decision Support System in Determining the Achievement of Wi...

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1st International Conference on technology Innovation and Its Applications, ICTIIA 2022

作者： Pratama, Yudhistira Adhitya Iswara, Bagus Ajie Nababan, Adli Abdillah Information System Studies Program Pelita Harapan University Medan Campus Faculty Of Computer Science Medan Indonesia Master's Study Program Informatics Engineering Universitas Sumatera Utara Faculty Of Computer Science And Information Technology Medan Indonesia Digital Business Study Program Stmik Pelita Nusantara Medan Indonesia

ISBN: (数字)9781665488266

ISBN: (纸本)9781665488266

Widyaiswara is required to show the best performance to fulfill his duties and obligations. Therefore, it is very important to measure the performance of the Widyaiswara, so that it can be used as evaluation material by the BPSDM PROVSU. However, the assessment process that occurs is still not precise and time-consuming. Therefore, to minimize this, a decision support system is needed that can minimize time errors and data processing in decision-making so that the assessment runs well and accurately. This system was built by applying the WASPAS method and the TOPSIS method. Based on the results of tests carried out five times, the results of the WASPAS method processing time resulted in an average processing time of 0.00029791 ms and an average memory usage value of 2.836.44 kb, while the results of the TOPSIS method processing time test resulted in an average processing time - an average of 0.000296428 ms and an average memory usage value of 2.862.34 kb. © 2022 IEEE.

关键词： Decision support systems

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Design and Implementation of an EMG-based system for Monitoring and Help Request

Design and Implementation of an EMG-based System for Monitor...

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2022 International Conference on Smart systems and Power Management, IC2SPM 2022

作者： Zeine, Carla Daou, Roy Abi Zeid Telawi, Samer Boercsoek, Josef Hayek, Ali Haidar, Gaby Abou University of Kassel Faculty of Electrical Engineering /Computer Science Department of Computer Architecture and System Programming Kassel Germany Lebanese German University Faculty of Public Health Jounieh Department of Computer Architecture and System Programming Biomedical Technologies Department Jounieh Lebanon Université la Sagesse Faculty of Engineering Furn El Chebbak Mechatronics Department Beirut Lebanon University of Applied Science Hamm-Lippstadt Department Lippstadt 2 Hardware Engineering Marker Allee 76-78 Hamm59063 Germany American University of Science and Technology Faculty of Engineering Zahle Lebanon

ISBN: (数字)9781665452915

ISBN: (纸本)9781665452915

Integrating electrophysiological signals in larger systems is getting wider due to the fact that such modalities facilitate the daily tasks of users. However, the high cost of monitoring systems (e.g., oscilloscopes) limits the testing and analysis of the acquired signal from such noisy systems (i. e., electrophysiological devices). The proposed system is a part of a larger proj ect that deals with the identification of sign language movements through the use of Muscle-computer Interfaces. On one hand, the user is able to display the signal acquired by the electrodes on an LCD screen. On the other hand, the system helps a mute patient to issue a special message to his surrounding environment that does not know the sign language to ask for help. The second feature is not unique, as it can be replaced by any other application based on the user's needs. The system was tested on several persons, and the results are promising. © 2022 IEEE.

关键词： Liquid crystal displays

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Driver Behavior Assessment with Different ML Models Using EEG and Physiological Data - A Comparative Study

Driver Behavior Assessment with Different ML Models Using EE...

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International Conference on Electronics, computers and Artificial Intelligence (ECAI)

作者： Sama Hussein Al-Gburi Kanar Alaa Al-Sammak Khattab M. Ali Alheeti George Suciu Abedulqader Gailan Abdulqader Faculty of Electronics Telecommunications and Information Technology National University of Science and Technology Politehnica Bucharest (UNSTPB) Bucharest Romania Faculty of Department of Computer Networking System College of Computer Sciences and Information Technology University of Anbar Ramadi Iraq Faculty of Electronics Telecommunications and Information Technology National University of Science and Technology Politehnica Bucharest (UNSTPB) and BEIA Consult International Bucharest Romania Karkh Second Directorate of Education Ministry of Education Baghdad Iraq

ISBN: (数字)9798350371154

ISBN: (纸本)9798350371161

Ensuring road safety is becoming more and more significant for researchers and for all the society. One of the main issues in this area considers driver drowsiness. Therefore, the focus of this paper is the comprehensive analysis and comparison of four distinct machine learning models: Logistic Regression, Random Forest, Support Vector Machine (SVM), and Gradient Boosting. The ultimate goal is to gain insights into the intricate factors that shape driver behavior. When assessing the effectiveness of each model, four key metrics are taken into account: precision, recall, F1 score, and accuracy. In this study, we used a “TRYOUT” dataset. The study yielded several significant findings regarding the performance of the machine learning models employed. Remarkably, the accuracy scores for all models were notably high, falling within a range of 0.8651 to 0.9999. Among all the models, the Random Forest model stood out as the most effective, displaying an impressive accuracy score of 0.9999 on the merged data. The SVM model, while not as proficient as the Random Forest, still managed a respectable performance, particularly on the MPM39 dataset where it achieved an accuracy of $\mathbf{7 5. 0 6 \%}$. The Logistic Regression model demonstrated its strength on the CVD08 dataset, although its performance was comparatively weaker on the other datasets. Lastly, the Gradient Boosting model demonstrated consistent competency across all datasets, underscoring its reliability in a range of circumstances.

关键词： Support vector machines Logistic regression Accuracy Shape Computational modeling Brain modeling Boosting

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