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Search and Rescue Optimization with Machine Learning Enabled Cybersecurity Model

computer systems Science & engineering 2023年第5期45卷 1393-1407页

作者： Hanan Abdullah Mengash Jaber S.Alzahrani Majdy M.Eltahir Fahd N.Al-Wesabi Abdullah Mohamed Manar Ahmed Hamza Radwa Marzouk Department of Information Systems College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Industrial Engineering College of Engineering at AlqunfudahUmm Al-Qura UniversitySaudi Arabia Department of Information Systems College of Science&Art at MahayilKing Khalid UniversitySaudi Arabia Department of Computer Science College of Science&Art at MahayilKing Khalid UniversitySaudi Arabia Research Centre Future University in EgyptNew Cairo11745Egypt Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia Department of Mathematics Faculty of ScienceCairo UniversityGiza12613Egypt

Presently,smart cities play a vital role to enhance the quality of living among human beings in several ways such as online shopping,e-learning,ehealthcare,*** the benefits of advanced technologies,issues are also existed from the transformation of the physical word into digital word,particularly in online social networks(OSN).Cyberbullying(CB)is a major problem in OSN which needs to be addressed by the use of automated natural language processing(NLP)and machine learning(ML)*** article devises a novel search and rescue optimization with machine learning enabled cybersecurity model for online social networks,named SRO-MLCOSN *** presented SRO-MLCOSN model focuses on the identification of CB that occurred in social networking *** SRO-MLCOSN model initially employs Glove technique for word embedding ***,a multiclass-weighted kernel extreme learning machine(M-WKELM)model is utilized for effectual identification and categorization of ***,Search and Rescue Optimization(SRO)algorithm is exploited to fine tune the parameters involved in the M-WKELM *** experimental validation of the SRO-MLCOSN model on the benchmark dataset reported significant outcomes over the other approaches with precision,recall,and F1-score of 96.24%,98.71%,and 97.46%respectively.

关键词： Cybersecurity cyberbullying social networking machine learning search and rescue optimization

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Comparative Evaluation of Machine Learning-Based Controller Area Network Intrusion Detection systems 14

Comparative Evaluation of Machine Learning-Based Controller ...

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14th International Conference on information and Communication systems, ICICS 2023

作者： Al-Jarrah, Omar Y. Ramadan, Omar A. Alsaify, Baha Alkhushayni, Suboh Jordan University of Science and Technology Faculty of Computer and Information Technology Dept. Network Engineering and Security P.O. Box 3030 Irbid22110 Jordan Jordan University of Science and Technology Faculty of Engineering Dept. Mechanical Engineering P.O. Box 3030 Irbid22110 Jordan Yarmouk University Dept. Information Systems Irbid Jordan

ISBN: (纸本)9798350307870

The controller area network (CAN) protocol is widely used in vehicle networks. However, it lacks essential security features like confidentiality and authentication. To enhance vehicle security, researchers have proposed and developed various methods, including intrusion detection systems (IDSs). These IDSs play a key role in early detection of potential cyberattacks, safeguarding vehicles, passengers, and road users. Machine learning (ML) algorithms are increasingly employed in CAN IDSs but they face challenges due to diverse experimental settings, datasets, and metrics used for evaluation, impeding progress and comparison of different approaches in this field. This study aims to benchmark well-recognized ML algorithms, including gaussianNB, k-nearest neighbors (KNN), decision trees (DT), random forest (RF), long short-term memory (LSTM), and convolutional neural networks (CNN), for intrusion detection in CAN. We evaluate the performance of these algorithms using seven evaluation metrics on the real ORNL automotive dynamometer (ROAD) dataset, a state-of-the-art benchmark in this field. Experimental results demonstrate that KNN, DT, RF, LSTM, and CNN can detect various types of attacks (e.g., Fuzzing, targeted ID, and masquerade attacks) with high accuracy. However, the computational and time efficiency of tree-based classifiers, namely DT and RF, makes them a potentially appealing choice for real-time intrusion detection tasks in CAN. © 2023 IEEE.

关键词： Intrusion detection

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OPTIMAL SCHEDULE OF A SPORT SHOOTING COMPETITION 17

OPTIMAL SCHEDULE OF A SPORT SHOOTING COMPETITION

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17th International Symposium on Operational Research in Slovenia, SOR 2023

作者： Ercsey, Zsolt Kovács, Zoltán Storcz, Tamás Department of Systems and Software Technologies Faculty of Engineering and Information Technology University of Pécs Boszorkány Street 2 H-7624 Hungary Optin Ltd. Oroszlán u. 4 SzegedH-6720 Hungary

ISBN: (纸本)9789616165617

The paper explains the problem of amateur sport shooting competition scheduling with a manual and an automated solution. The automated solution provides an optimal schedule to minimize the infrastructure and human resource occupation. The proposed programming model also minimizes the attendance of participating sport clubs, because considers that the amateur competitors want to spend minimum time necessary (with required overhead) for the competition and the team of a sport club usually travel together. © Proceedings of the 17th International Symposium on Operational Research in Slovenia, SOR 2023. All rights reserved.

关键词： Sports

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Deep Reinforcement Learning for Addressing Disruptions in Traffic Light Control

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computers, Materials & Continua 2022年第5期71卷 2225-2247页

作者： Faizan Rasheed Kok-Lim Alvin Yau Rafidah Md Noor Yung-Wey Chong School of Engineering and Computer Science University of HertfordshireHatfieldAL109ABUK Department of Computing and Information Systems Sunway UniversitySubang Jaya47500Malaysia Faculty of Computer Science and Information Technology University of MalayaKuala Lumpur50603Malaysia National Advanced IPv6 Centre Universiti Sains MalaysiaUSMPenang11800Malaysia

This paper investigates the use of multi-agent deep Q-network(MADQN)to address the curse of dimensionality issue occurred in the traditional multi-agent reinforcement learning(MARL)*** proposed MADQN is applied to traffic light controllers at multiple intersections with busy traffic and traffic disruptions,particularly *** is based on deep Q-network(DQN),which is an integration of the traditional reinforcement learning(RL)and the newly emerging deep learning(DL)*** enables traffic light controllers to learn,exchange knowledge with neighboring agents,and select optimal joint actions in a collaborative manner.A case study based on a real traffic network is conducted as part of a sustainable urban city project in the Sunway City of Kuala Lumpur in *** is also performed using a grid traffic network(GTN)to understand that the proposed scheme is effective in a traditional traffic *** proposed scheme is evaluated using two simulation tools,namely Matlab and Simulation of Urban Mobility(SUMO).Our proposed scheme has shown that the cumulative delay of vehicles can be reduced by up to 30%in the simulations.

关键词： Artificial intelligence traffic light control traffic disruptions multi-agent deep Q-network deep reinforcement learning

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Patterns of Remote Work Environments 27

Patterns of Remote Work Environments

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2022 27th European Conference on Pattern Languages of Programs, EuroPLoP 2022

作者： Toldy, Kristian Sulaiman Khail, Waheedullah Vranic, Valentino Institute of Informatics Faculty of Informatics and Information Technologies Information Systems and Software Engineering Slovak University of Technology in Bratislava Bratislava Slovakia Helmholtz-Zentrum Berlin fur Materialien und Energie Gmbh Berlin Germany

ISBN: (纸本)9781450395946

People are more and more in a position to work remotely. They face the same problems of establishing and running remote work environments over and over again. In order to help them a bit, six organizational patterns of remote work environments are presented in this paper: Unofficial Meetings, Remote Mentor, Unified Communication Channels, Standard Operating Procedures, information Hub, and Cut Meetings. The patterns come from a firsthand remote work experience obtained in a company focusing on quality assurance through a crowd testing methodology where software companies get real users to test their newest products. © 2022 ACM.

关键词： Quality assurance

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Gymnasium Library Interface for Multi Access Edge Computing

Gymnasium Library Interface for Multi Access Edge Computing

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International Conference on Control systems, Mathematical Modelling, Automation and Energy Efficiency (SUMMA)

作者： Ivan Filianin Alexander Kapitonov Artem Timoshchuk-Bondar Faculty of Software Engineering and Computer Systems ITMO University St. Petersburg Russia Faculty of Infocommunication Technologies ITMO University St. Petersburg Russia

ISBN: (数字)9798331532178

ISBN: (纸本)9798331532185

This paper addresses the problem of developing reinforcement learning algorithms for capsule placement decision making tasks in a geo-distributed Multi Access Edge Computing environment. A Python library called gym-Iwmecps has been developed to speed up and simplify the process of developing machine learning algorithms with reinforcement learning for decision-making system tasks in MEC networks. At the same time, the Gymnasium Capability API has not been used previously in the tasks of organizing and testing MEC platforms. We also tested basic algorithms Q-network and DQN in conjunction with gym-lwmecps and proved the performance of the developed Python library.

关键词： Training Machine learning algorithms Multi-access edge computing Computational modeling Decision making Process control Reinforcement learning Libraries Mathematical models Testing

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Noisy ECG Signal Data Transformation to Augment Classification Accuracy

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computers, Materials & Continua 2022年第5期71卷 2191-2207页

作者： Iqra Afzal Fiaz Majeed Muhammad Usman Ali Shahzada Khurram Akber Abid Gardezi Shafiq Ahmad Saad Aladyan Almetwally M.Mostafa Muhammad Shafiq Department of Information Technology University of GujratGujrat50700Pakistan Department of Computer Science University of GujratGujrat50700Pakistan Faculty of Computing The Islamia University of BahawalpurBahawalpur63100Pakistan Department of Computer Science COMSATS University IslamabadIslamabadPakistan Industrial Engineering Department College of EngineeringKing Saud UniversityP.O.Box 800Riyadh 11421Saudi Arabia Department of Information Systems College of Computer and Information SciencesKing Saud UniversityP.O.Box 800Riyadh11421Saudi Arabia Department of Information and Communication Engineering Yeungnam UniversityGyeongsan38541Korea

In this era of electronic health,healthcare data is very important because it contains information about human *** addition,the Internet of Things(IoT)revolution has redefined modern healthcare systems and management by providing continuous *** this case,the data related to the heart is more important and requires proper *** the analysis of heart data,Electrocardiogram(ECG)is *** this work,machine learning techniques,such as adaptive boosting(AdaBoost)is used for detecting normal sinus rhythm,atrial fibrillation(AF),and noise in ECG signals to improve the classification *** proposed model uses ECG signals as input and provides results in the form of the presence or absence of disease AF,and classifies other signals as normal,other,or *** article derives different features from the signal using Maximal information Coefficient(MIC)and minimum Redundancy Maximum Relevance(mRMR)technique,and then classifies them based on their *** the ECG contains some kind of noise and irregular data streams so the purpose of this study is to remove artifacts from the ECG signal by deploying the method of Second-Order-Section(SOS)(filter)and correctly classify *** features were extracted to improve the detection of ECG *** with existing methods,this work gives promising results and can help improve the classification accuracy of the ECG signals.

关键词： ECG atrial fibrillation adaptive boosting heart rate variability

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Integrative analyses in omics data: Machine learning perspective 68

Integrative analyses in omics data: Machine learning perspec...

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68. Jahrestagung der Deutsche Gesellschaft fur Medizinische Informatik, Biometrie und Epidemiologie e.V., GMDS 2023 - 68th Annual Conference of the German Association for Medical Informatics, Biometry and Epidemiology, GMDS 2023

作者： Yazici, Miray Unlu Bakir-Gungor, Burcu Yousef, Malik Department of Bioengineering Faculty of Engineering Abdullah Gül University Kayseri Turkey Department of Computer Engineering Faculty of Engineering Abdullah Gül University Kayseri Turkey Department of Information Systems Zefat Academic College Zefat Israel Galilee Digital Health Research Center Zefat Academic College Zefat Israel

Developments in the high throughput technologies have enabled the production of an immense amount of knowledge at the multi-omics level. Considering complex diseases which are affected by multi-factors, single omics datasets might not be sufficient to unveil the molecular mechanisms of heterogeneous diseases. Providing a comprehensive and systematic overview to explain disease hallmarks in significant depth is critical. Utilizing multi-omics datasets has led to the development of a variety of tools and platforms. Machine learning models are utilized in a wide variety of tools to tackle the complexity of disorders and to identify new biomolecular signatures and potential markers. Underlying aspects of these approaches are based on training the models for making predictions and classification of the given data. In this review, we describe current machine learning-based approaches and available implementations. Challenges in the enlightenment of disease mechanisms of onset and progression and future development of the field of medicine will be discussed. The prominence of biological interpretation of model output with corresponding biological knowledge will be also covered in this review. © 2023 Unlu Yazici et al.

关键词： Machine learning

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Bayesian Network Structure Discovery Using Antlion Optimization Algorithm.

International Journal of Systematic Innovation

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International Journal of Systematic Innovation 2022年第1期7卷 46-65页

作者： Awla, Hoshang Qasim Kareem, Shahab Wahhab Mohammed, Amin Salih Computer science department Faculty of Science Soran University Erbil Iraq Dep. of Technical Information Systems Engineering Erbil Technical Engineering College Erbil Poly-technic University Erbil Iraq Department of Information Technology College of Engineering and Computer Science Lebanese French University Kurdistan Region Erbil Iraq

Bayesian networks have recently been used for discovering an optimal learning structure in machine learning. Bayes networks can describe possible dependencies of explanatory variables. As a novel approach to studying the structure of a Bayesian network, the authors present the Antlion Optimization Algorithm (ALO). In the algorithm;deletion, rewind, insertion, and change are utilized to produce ALO to reach the best hull solution. Essentially, the technique used in the ALO algorithm imitates the antlions’ behaviors while hunting. The suggested approach is contrasted with simulated Annealing, Simulated Annealing Hybrid Bee, Greedy Search Hybrid Bee, optimization inspired by Pigeon, and greedy search using the BDe Score function. The researchers also studied the representation of the confusion matrix of these techniques using different reference data sets. The findings of the assessments reveal that the proposed algorithm works better than the other algorithms and has better consistency and score values. As shown by the experimental evaluations, the proposed method has a more reliable performance than other algorithms (including the production of excellent scores and accuracy values). © 2022. All Rights Reserved.

关键词： Antlion Optimization Bayesian Network Hunting Search Local & Global Search Metaheuristics Search and Score Structure Learning

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GAN-based Symmetric Embedding Costs Adjustment for Enhancing Image Steganographic Security 24

GAN-based Symmetric Embedding Costs Adjustment for Enhancing...

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32nd ACM International Conference on Multimedia, MM 2024

作者： Ye, Miaoxin Zhou, Saixing Luo, Weiqi Tan, Shunquan Huang, Jiwu Guangdong Provincial Key Laboratory of Information Security Technology Sun Yat-Sen University Guangzhou China Shenzhen Key Laboratory of Media Security College of Computer Science and Software Engineering Shenzhen University Shenzhen China Guangdong Laboratory of Machine Perception and Intelligent Computing Faculty of Engineering Shenzhen MSU-BIT University Shenzhen China

ISBN: (纸本)9798400706868

Designing embedding costs is pivotal in modern image steganography. Many studies have shown adjusting symmetric embedding costs to asymmetric ones can enhance steganographic security. However, most existing methods heavily depend on manually defined parameters or rules, limiting security performance improvements. To overcome this limitation, we introduce an advanced GAN-based framework that transitions symmetric costs to asymmetric ones without the need for the manual intervention seen in existing approaches, such as the detailed specification of cost modulation directions and magnitudes. In our framework, we firstly achieve symmetric costs for a cover image, which is randomly split into two sub-images, with part of the secret information embedded into one. Subsequently, we design a GAN model to adjust the embedding costs of the second sub-image to asymmetric, facilitating the secure embedding of the remaining secret information. To support our phased embedding approach, our GAN's discriminator incorporates two steganalyers with different tasks: distinguishing the generator's final output, i.e., the stego image, from both the input cover image and the partially embedded stego image, providing diverse guidance to the generator. In addition, we introduce a simple yet effective update strategy to ensure a stable training process. Comprehensive experiments demonstrate that our method significantly enhances security over existing symmetric steganography techniques, achieving state-of-the-art levels compared to other methods focused on embedding costs adjustments. Additionally, detailed ablation studies validate our approach's effectiveness. © 2024 ACM.

关键词： Steganography

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