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2024 International Conference on Decision Aid Sciences and Applications, DASA 2024

作者： Ahmed, Amjed Abbas Ali Alheeti, Khattab M. Najim, Ali Hamza Satar, Nurhizam Safie Mohd Aliesawi, Salah A. Hashim, Aisha Hassan Abdallah Universiti Kebangsaan Malaysia Center for Cyber Security Faculty of Information Science and Technology Bangi Malaysia Department of Computer Techniques Engineering Baghdad Iraq College of Computer and Information Technology Computer Networking Systems Department University of Anbar Anbar Iraq Department of Computer Technical Engineering Al-Diwaniyah Iraq Center for Cyber Security Faculty of Information Science and Technology Bangi43600 Malaysia College of Computer and Information Technology Department of Computer Networking Systems University of Anbar Anbar Iraq Faculty of Engineering Department of Electrical and Computer Engineering International Islamic University Malaysia Selangor Malaysia

ISBN: (纸本)9798350369106

This study addresses the critical aspect of data collection within Wireless Sensor Networks (WSNs), which consist of autonomous, compact sensor devices deployed to monitor environmental conditions. These networks have limited processing capacity, storage, short-range communication capabilities, and constrained battery power. A primary challenge in WSN s revolves around data gathering, particularly when employing static sinks, leading to premature energy depletion in nodes near these sinks. Integrating mobile collectors introduces the potential for energy conservation by shortening data routes. This article introduces three distinct algorithms for optimizing data collection in WSN s. Our approach involves an in-depth analysis of diverse strategies to mitigate packet collisions and facilitate the retransmission of lost packets. Our findings underscore the considerable impact that packet collisions can exert on the overall network performance during data gathering within wireless sensor networks. Through empirical exploration and analysis, we highlight the significance of these algorithms in enhancing the efficiency, energy conservation, and reliability of data collection processes within WSNs. This research sheds light on these algorithms' vital role in addressing the pressing challenges of data gathering and network performance optimization in wireless sensor networks. © 2024 IEEE.

关键词： Aircraft accidents

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On-Device AI security Solutions: A Study on Lightweight Ensemble Learning Technique 17

On-Device AI Security Solutions: A Study on Lightweight Ense...

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17th International Conference on COMmunication Systems and NETworkS, COMSNETS 2025

作者： Dong, Huiyao Kotenko, Igor ITMO University Faculty of Information Technology and Security Saint Petersburg Russia St. Petersburg Federal Research Center of the RAS Laboratory of Computer Security Problems Saint Petersburg Russia

ISBN: (纸本)9798331531195

IoT security poses a significant challenge due to the presence of extensive confidential data and vulnerable devices. Artificial intelligence (AI) is increasingly viewed as pivotal in cybersecurity, enhancing the efficacy of security measures including login mechanisms with fingerprinting, real-time phishing detection, and multi-stage attack identification. However, centralized AI models face issues such as high variance and over-fitting, as well as resource inefficiencies. This study explores the deployment of on-device AI security models in a distributed manner with lightweight yet effective ensemble deep learning techniques. The proposed methods involve lightweight ensemble techniques such as learnable weighted average voting (WAVGVoting) and concatenation-based stacking, integrated with three base learners with distinct structures. The experiments on Edge-IIoT, utilizing 5-fold cross-validation, show that our approaches are effective even in dynamic data and diverse settings. Specifically, the stacking model achieved an average accuracy of 99.8441% with a recall of 99.7762%, while WAVGVoting showed an average precision of 99.9116% and a false alarm rate of 0.0883%, certainly indicating its applicability. © 2025 IEEE.

关键词： deep learning ensemble learning Internet of Things (IoT) intrusion detection

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A Study on Classification of Mobile Application Network Traffic on MIRAGE-COVID-CCMA-2022 Dataset via Machine/Deep Learning and Feature Selection Methods 17

A Study on Classification of Mobile Application Network Traf...

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17th International Conference on information security and Cryptology, ISCTurkiye 2024

作者： Yasar, Imren Dasdemir Coskun, Aysun Department of Information Security Engineering Gazi University Ankara Turkey Faculty of Technology Department of Computer Engineering Gazi University Ankara Turkey

ISBN: (纸本)9798331533649

In recent years, the use of mobile internet has become widespread rapidly with the introduction of smartphones. The increasing weight of mobile network traffic in the overall network traffic has made mobile network traffic classification a new and important topic of study. The study utilizes MIRAGE-COVID-CCMA-2022 dataset for the classification of mobile application network traffic. The MIRAGE-COVID-CCMA-2022 dataset contains the network traffic of nine communication and collaboration mobile applications including Discord, GotoMeeting, Meet, Messenger, Skype, Slack, Teams, Webex, and Zoom. Experiments were conducted using four machine learning classifiers (Support Vector Machine, k-Nearest Neighbor, Adaptive Boosting, and Random Forest), four deep learning classifiers (Convolutional Neural Networks, Long Short-Term Memory, Bidirectional Long Short-Term Memory, and Gated Recurrent Units), and three feature selection methods (Minimum Redundancy Maximum Relevance, k-Nearest Neighbor, and Random Forest). The results of the study show that an overall accuracy of over 95% for the MIRAGE-COVID-CCMA-2022 dataset with a nine-class classification. The study concludes that the most appropriate classification structure is to select the features using the Minimum Redundancy Maximum Relevance method and classify the features with the Random Forest classifier. Also, this classification structure stands out as one of the classification structures with the fastest running time. © 2024 IEEE.

关键词： deep learning feature extraction machine learning mobile computer networks

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Extensive Study of Cloud Computing Technologies, Threats and Solutions Prospective

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computer Systems Science & Engineering 2022年第4期41卷 225-240页

作者： Mwaffaq Abu-Alhaija Nidal M.Turab AbdelRahman Hamza Department of Networks and Information Security Faculty of Information TechnologyAl-Ahliyya Amman UniversityAmman19328Jordan Department of Computer Science Information TechnologyAl-Ahliyya Amman UniversityAmman19328Jordan

Infrastructure as a Service(IaaS)provides logical separation between data,network,applications and machines from the physical constrains of real *** is one of the basis of cloud ***,security issues are also gradually emerging with virtualization of cloud *** security aspects of cloud virtualization will be explored in this research paper,security recognizing potential threats or attacks that exploit these vulnerabilities,and what security measures are used to alleviate such *** addition,a dis-cussion of general security requirements and the existing security schemes is also *** shown in this paper,different components of virtualization environ-ment are targets to various attacks that in turn leads to security issues compromis-ing the whole cloud *** this paper an overview of various cloud security aspects is also *** attack scenarios of virtualization envir-onments and security solutions to cater these attacks have been discussed in the *** then proceed to discuss API security concerns,data security,hijacking of user account and other security *** aforementioned discussions can be used in the future to propose assessment criteria,which could be useful in ana-lyzing the efﬁciency of security solutions of virtualization environment in the face of various virtual environment attacks.

关键词： Cloud computing environment paravirtualization full virtualization cloud virtualization security hypervisor virtual machines

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Forecasting and managing urban futures: machine learning models and optimization of urban expansion

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Asian Journal of Civil Engineering 2024年第6期25卷 4673-4682页

作者： Abid, Mohammed Talib Aljarrah, Njood Shraa, Tamer Alghananim, Haneen Marouf Department of Architecture College of Engineering Al-Nahrain University Baghdah Iraq Computer Sciences Faculty of Information Technology and Computer Sciences Ajloun National University Ajloun Jordan Faculty of Science and Information Technology Cyber Security Irbid National University Irbid Jordan Faculty of Engineering Architecture Department Ain Shams University Cairo Egypt

This paper presents a novel method for predicting and controlling urban growth by combining convolutional neural networks (CNN) with Spider Monkey Optimization (SMO) to efficiently use their combined capabilities. This strategy utilizes sophisticated machine learning algorithms to forecast urban development patterns, while optimization technologies enhance these forecasts by including sustainable urban design ideas. Integration plays a crucial role in tackling the difficulties presented by fast urbanization, including issues related to land use, resource administration, and environmental sustainability in the fields of civil and architectural engineering. The study employs large datasets to assess the efficacy of CNN models in detecting urban expansion patterns and enhances these models with optimization scenarios encompassing land use, traffic flow, electricity efficiency, water resources, and waste management, all with the goal of enhancing urban sustainability. The findings demonstrate a significant improvement in the precision of predictions and the ability to maintain positive results, highlighting the crucial role of integrating machine learning and optimization in urban planning methodologies. This method not only provides urban planners and politicians with strong and effective tools for making decisions but also creates opportunities for implementing creative and sustainable solutions for urban growth. The results provide a substantial contribution to the existing urban planning literature by demonstrating the actual use of these technologies in regulating urban expansion. This will stimulate more study in this rapidly evolving subject. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Convolutional neural networks Machine learning Optimization techniques Spider monkey optimization Sustainable urban planning Urban expansion

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Performance Analysis of Energy and Secure Efficient Routing for Wireless Sensor Networks

Performance Analysis of Energy and Secure Efficient Routing ...

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2024 International Conference on Decision Aid Sciences and Applications, DASA 2024

作者： Ahmed, Amjed Abbas Hussein, Zainab Mohammed Najim, Ali Hamza Ali Alheeti, Khattab M. Mohd Satar, Nurhizam Safie Abdallah Hashim, Aisha Hassan Universiti Kebangsaan Malaysia Center for Cyber Security Faculty of Information Science and Technology Bangi Malaysia Department of Computer Techniques Engineering Baghdad Iraq Baghdad Iraq Department of Computer Technical Engineering Al-Diwaniyah Iraq College of Computer and Information Technology Computer Networking Systems Department University of Anbar Anbar Iraq Center for Cyber Security Faculty of Information Science and Technology Bangi43600 Malaysia International Islamic University Malaysia Faculty of Engineering Department of Electrical and Computer Engineering Selangor Malaysia

ISBN: (纸本)9798350369106

Wireless Sensor Networks (WSNs) are essential for various applications, but their architecture makes them vulnerable to attacks. While traditional security methods like authentication and encryption offer some protection, they often fall short against specific node attacks. This paper introduces the Secure Energy Aware Routing (SEAR) strategy, which uses trust-based assessments to exclude malicious nodes while optimizing energy consumption. SEAR factors in node trust, hop time, and residual energy to ensure reliable data transmission through trustworthy nodes. MATLAB simulations on networks of varying sizes (100-500) compare SEAR with existing methods (EAD and EASER), demonstrating SEAR's superior performance. Results show significant improvements in packet delivery ratio (13-16%), network throughput (19-24 %), energy consumption (32-36%), packet loss (16-190/0), and end-to-end latency (21-26%), proving SEAR's effectiveness in enhancing WSN security, energy efficiency, and performance. © 2024 IEEE.

关键词： MATLAB

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Multi Label Classification Algorithm Based on Conditional Local Correlations among Labels and Ensemble Learning 25

Multi Label Classification Algorithm Based on Conditional Lo...

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25th International Arab Conference on information technology, ACIT 2024

作者： Alazaidah, Raed Nasir, Ola Hadi, Wael Samara, Ghassan Al Daoud, Essam Aljaidi, Mohammad Khouj, Mohammed Talat Elsoud, Esraa Abu Alzoubi, Haneen Zarqa University Faculty of Information Technology Department of Data Science and Ai Zarqa Jordan Zarqa University Faculty of Information Technology Department of Computer Science Zarqa Jordan Petra University Faculty of Information Technology Department of Cyber Security Amman Jordan University of Business and Technology Department of Electrical Engineering Jeddah21432 Saudi Arabia Applied Science Private University Faculty of Information Technology Department of Computer Science Amman Jordan

ISBN: (纸本)9798331540012

Multi-Label Classification (MLC) is an advanced form of traditional classification, where each data instance is simultaneously associated with multiple labels. The importance of MLC has increased significantly in recent years, due to its wide range of application areas. Modern algorithms, when faced with the challenge of multiple labels, continue to show suboptimal performance, conversely, they show significantly superior performance when processing single-label datasets. This paper proposes a new algorithm to deal with the problem of transforming datasets from multiple labels to single labels by exploiting conditional and local correlations between labels after applying the Apriori predictive algorithm to extract relationships. Six ensemble learning models: Voting Hard, Voting Soft, Voting Weighted, Stacking LR, Stacking RF, and Stacking SVM, along with four classification models: SMO, DecisionStump, Random Forest, and Random Committee, were applied to three datasets. The algorithm showed effective and good performance by comparing the results of the proposed Multi Label Classification algorithm based on Conditional Local Correlations among Labels (MLC-CLCL) with several popular algorithms for dealing with multiple labels. © 2024 IEEE.

关键词： Contrastive Learning

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An Analysis of Image Splicing Detection using Convolutional Neural Network (CNN) 1

An Analysis of Image Splicing Detection using Convolutional ...

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1st International Conference on Cyber security and Computing, CyberComp 2024

作者： Abd Warif, Nor Bakiah Hamdan, Muhammad Faiz Ab. Razak, Mohd. Faizal Mohamad, Salwana Firdaus, Ahmad Universiti Tun Hussein Onn Malaysia Centre for Information Security Research Faculty of Computer Science and Information Technology Johor Malaysia Universiti Tun Hussein Onn Malaysia Faculty of Computer Science and Information Technology Johor Malaysia Universiti Malaysia Pahang Al-Sultan Abdullah Faculty of Computing Pahang Pekan Malaysia

ISBN: (纸本)9798350387728

Image splicing is the act of manipulating digital images by copying and pasting pixels from one image onto another, and it can be difficult to detect due to the existence of many editing software. Recently, deep learning approaches have been used to effectively detect image splicing forgery because they learned high-level features from images that are not visible to naked eye. This research aims to evaluate the use of Convolutional Neural Network (CNN) as an image splicing detection method against CASIA V1, Columbia Gray and Columbia Color datasets. The analysis shows that CNN is able to detect 83% accuracy in Columbia Gray, 75% accuracy in CASIA V1, and 69% accuracy in Columbia Color. These results have led to the highest performance, specifically for fake images, which are 96%, 85% and 88%, for each dataset, respectively. This means that the CNN able to detect image splicing correctly, while having difficulties identifying an authentic or real image. © 2024 IEEE.

关键词： Convolutional neural networks

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Smart Water-Powered Renewable Energy System with IoT Integration

Smart Water-Powered Renewable Energy System with IoT Integra...

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2024 International Conference on Decision Aid Sciences and Applications, DASA 2024

作者： Ahmed, Amjed Abbas Najim, Ali Hamza Ibraheem, Ahmed Salman Ali Alheeti, Khattab M. Mohd Satar, Nurhizam Safie Abdallah Hashim, Aisha Hassan Universiti Kebangsaan Malaysia Center for Cyber Security Faculty of Information Science and Technology Bangi Malaysia Department of Computer Techniques Engineering Baghdad Iraq Department of Computer Technical Engineering Al-Diwaniyah Iraq Department of Computer Technical Engineering Baghdad Iraq College of Computer and Information Technology Computer Networking Systems Department University of Anbar Anbar Iraq Center for Cyber Security Faculty of Information Science and Technology Bangi43600 Malaysia International Islamic University Malaysia Faculty of Engineering Department of Electrical and Computer Engineering Selangor Malaysia

ISBN: (纸本)9798350369106

An advanced hybrid renewable energy collecting and storage system prototype that utilizes water flow as its primary power source. Additionally, the system incorporates an energy monitoring system based on the Internet of Things (IoT) technology. The device effectively harnesses energy from the movement of water, storing it for future use in low-voltage applications. It demonstrates a maximum daily energy capture capacity of about 100Wh. The energy monitoring system, based on the Internet of Things (IoT), enables the real-time and remote monitoring of energy harvesting and usage. We thoroughly assessed the energy harvesting component across different situations to identify the crucial parameters that impact energy output. The results of our study demonstrate that several elements, including the spatial separation 'between the energy harvester and the water source, Water flow' speed, sensor orientation, and sensor density, all play significant roles in determining the quantity of energy collected. This research shows that it is possible to integrate IoT technology with hydropower renewable energy systems. It provides significant perspectives on enhancing energy extraction efficiency in practical scenarios. © 2024 IEEE.

关键词： harvesting renewable energy IoT storage energy water flow energy

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Improving VANET Localization Performance with Data Verification: Challenges and Solutions

Improving VANET Localization Performance with Data Verificat...

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2024 International Conference on Decision Aid Sciences and Applications, DASA 2024

作者： Ahmed, Amjed Abbas Kadhim, Aqeel Kamil Najim, Ali Hamza Ali Alheeti, Khattab M. Mohd Satar, Nurhizam Safie Abdallah Hashim, Aisa Hassan Universiti Kebangsaan Malaysia Center for Cyber Security Faculty of Information Science and Technology Bangi Malaysia Department of Computer Techniques Engineering Baghdad Iraq Department of Computer Technical Engineering Baghdad Iraq Department of Computer Technical Engineering Al-Diwaniyah Iraq College of Computer and Information Technology Computer Networking Systems Department University of Anbar Anbar Iraq Center for Cyber Security Faculty of Information Science and Technology Bangi43600 Malaysia International Islamic University Malaysia Faculty of Engineering Department of Electrical and Computer Engineering Selangor Malaysia

ISBN: (纸本)9798350369106

In VANETs, the important and effective applications of vehicle localization include safety and communication applications. Thus, it is difficult to get very accurate localization in the dynamic and often very fluctuating environment of VANET. This research responds to this challenge with a keen thrust on improving vehicle localization using the Mobility Data Verification process. The primary goal of this work is to achieve high accuracy of vehicle positioning within VANET by checking the coherence of mobility data of moving vehicles. Hence, we present a concept that builds upon Mobility Data Verification to provide real-time location details from vehicles. Both GPS and onboard sensors feed this method, which uses a multi-level verification to flag out or eliminate the errors of deliberately spoofed data points. We use simulations to evaluate and validate the effectiveness of the proposed verification approach. As the proposed system, our previously developed and fine-tuned weighted localization (OWL) strategy does so. The performance evaluation results suggest that our proposed solution is feasible to distinguish automobiles that are not trustworthy and improve the performance of the localization algorithm. © 2024 IEEE.

关键词： Vane pumps

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