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arXiv 2024年

作者： Umar, Shahla U. Rashid, Tarik A. Ahmed, Aram M. Hassan, Bryar A. Baker, Mohammed Rashad Network Department College of Computer Science and Information Technology Kirkuk University Kirkuk Iraq Computer Science and Engineering Department School of Science and Engineering University of Kurdistan Hewler KRI Iraq Department of Computer Science College of Science Charmo University Sulaimani Chamchamal46023 Iraq

Bat Algorithm (BA) is a nature-inspired metaheuristic search algorithm designed to efficiently explore complex problem spaces and find near-optimal solutions. The algorithm is inspired by the echolocation behavior of bats, which acts as a signal system to estimate the distance and hunt prey. Although the BA has proven effective for various optimization problems, it exhibits limited exploration ability and susceptibility to local optima. The algorithm updates velocities and positions based on the current global best solution, causing all agents to converge towards a specific location, potentially leading to local optima issues in optimization problems. On this premise, this paper proposes the Modified Bat Algorithm (MBA) as an enhancement to address the local optima limitation observed in the original BA. MBA incorporates the frequency and velocity of the current best solution, enhancing convergence speed to the optimal solution and preventing local optima entrapment. While the original BA faces diversity issues, both the original BA and MBA are introduced. To assess MBA's performance, three sets of test functions (classical benchmark functions, CEC2005, and CEC2019) are employed, with results compared to those of the original BA, Particle Swarm Optimization (PSO), Genetic Algorithm (GA), and Dragonfly Algorithm (DA). The outcomes demonstrate the MBA's significant superiority over other algorithms. Additionally, MBA successfully addresses a real-world assignment problem (call center problem), traditionally solved using linear programming methods, with satisfactory results. © 2024, CC BY.

关键词： Linear programming

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Numerical analysis of unsteady free convection under the combined influence of inclined magnetohydrodynamic and exothermic chemical reaction in an enclosure filled with nanofluid

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International Journal of Thermofluids 2024年 23卷

作者： Alaydamee, Hussein H. Alomari, Mohammed Azeez Al-Salami, Qusay H. Alyousuf, Farah Q.A. Alqurashi, Faris Flayyih, Mujtaba A. Department of Chemical Engineering University of Al-Qadisiyah College of Engineering Al-Qadisiyah58002 Iraq Department of Mechanical Engineering University of Al-Qadisiyah Ad-Diwaniyah58001 Iraq College of Engineering University of Warith Al-Anbiyaa Karbala Iraq Department of Business Administration College of Administrative and Financial Sciences Cihan University-Erbil Iraq Department of Computer Network College of Engineering and Computer Science Lebanese French University Erbil Kurdistan Region Iraq Mechanical Engineering Dept. College of Engineering University of Bisha Bisha Saudi Arabia Biomedical Engineering Department College of Engineering and Technologies Al-Mustaqbal University Hillah Iraq

Unsteady study of the natural convection of aluminum oxide-water nanofluid within a trapezoidal geometry containing a circular cylinder located at its center. Finite Element method has been considered for the numerical analysis. The proposed investigation handled the impact of Rayleigh number (103–105), chemical reaction parameter (0–4), aluminum oxide nanoparticles volume fraction (0–0.06), magnetic field (0–63) and its inclination angle (0°–90°), and circular obstacle diameter (0.3–0.7) effects on time-dependent natural convection of Al2O3–H2O nanofluid. On the other hand, the value of Prandtl number has kept constant at (Pr = 6.2). Since the nanofluid mobility at φ = 0.02, Ha = 3, and Fk = 1 significantly improved, the heat transfer rate achieved its maximum intensity at Ra = 105. Research also reveals a little effect on heat transfer by increasing the fraction of nanoparticles. Additionally, as Ha intensifies from 0 to 63, a final change in the mean Nusselt number of 28.65 % is displayed. Finally, as the magnetic field angle of rotation is diminished, more enhancement in heat transmission is achieved. This research provides insights into the intricate relationship between natural convection and exothermic reaction under the influences of various conditions. This can illustrate the flow and thermal behaviors of nanofluid in such non-uniform shapes in many engineering applications. © 2024

关键词： Natural convection

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Secrecy energy efficiency maximization in D2D-enabled MEC networks: a GNN-based approach 3

Secrecy energy efficiency maximization in D2D-enabled MEC ne...

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3rd International Conference on Electronic Information engineering, Big Data, and computer Technology, EIBDCT 2024

作者： Hu, Jingying Zhang, Zhifei Ge, Yiyang Mao, Jin Chu, Zhipeng Xiong, Ke Fan, Pingyi Eng. Res. Center of Network Management Technology for High Speed Railway of Ministry of Education School of Computer and Information Technology Beijing Jiaotong University Beijing100044 China Collaborative Innovation Center of Railway Traffic Safety Beijing Jiaotong University Beijing100044 China National Engineering Research Center of Advanced Network Technologies Beijing Jiaotong University Beijing100044 China Department of Electronic Engineering Tsinghua University Beijing100084 China

ISBN: (纸本)9781510680449

This paper investigates the device-to-device (D2D)-enabled mobile edge computing (MEC) network, where multiple users offload their tasks to an MEC server or their paired devices for computing. In such a network, secrecy and green communication requirements are crucial due to the presence of potential eavesdroppers and the energy demands of global carbon emissions. To this end, a secrecy energy efficiency (SEE) maximization problem is formulated by jointly optimizing the computation frequency at users, the transmit power at users and the subchannel assignments, subject to the quality of service (QoS) and the available energy constraints. To solve the formulated non-convex mixed-integer nonlinear programming problem, a graph neural networks (GNNs) and genetics based resource allocation (GGRA) method is presented, where the communication system is modelled as a directed graph with relationships among users as edges. In order to enhance the ability to process users' own features and handle deep data patterns, self-loops and the ResNet blocks in convolutions and the genetics based optimization are employed. Simulations show that the proposed GGRA improves SEE performance by about 40% compared with the traditional GNN-based method with the same energy budget. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Energy efficiency

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Anomaly Detection in Quasi-Periodic Time Series based on Automatic Data Segmentation and Attentional LSTM-CNN (Extended Abstract) 39

Anomaly Detection in Quasi-Periodic Time Series based on Aut...

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39th IEEE International Conference on Data engineering, ICDE 2023

作者： Liu, Fan Zhou, Xingshe Cao, Jinli Wang, Zhu Wang, Tianben Wang, Hua Zhang, Yanchun Nanjing Research Institute of Electronics Technology Nanjing China School of Computer Science Northwestern Polytechnical University Xi'an China Department of Computer Science and Computer Engineering La Trobe University Melbourne Australia Northwest A&f University Yangling China College of Engineering and Science Victoria University Melbourne Australia Guangzhou University Guangzhou China New Network Dept. Pengcheng Lab Shenzhen China

ISBN: (纸本)9798350322279

Quasi-periodic time series (QTS) exists widely in the real world, and it is important to detect the anomalies of QTS. In this paper, we propose an automatic QTS anomaly detection framework (AQADF) consisting of a two-level clustering-based QTS segmentation algorithm (TCQSA) and a hybrid attentional LSTM-CNN model (HALCM). TCQSA first automatically splits the QTS into quasi-periods which are then classified by HALCM into normal periods or anomalies. Notably, TCQSA integrates a hierarchical clustering and the k-means technique, making itself highly universal and noise-resistant. HALCM hybridizes LSTM and CNN to simultaneously extract the overall variation trends and local features of QTS for modeling its fluctuation pattern. Furthermore, we embed a trend attention gate (TAG) into the LSTM, a feature attention mechanism (FAM) and a location attention mechanism (LAM) into the CNN to finely tune the extracted variation trends and local features according to their true importance to yield a better representation of the fluctuation pattern of the QTS. On four public datasets, HALCM exceeds four state-of-the-art baselines and obtains at least 97.3% accuracy, TCQSA exceeds two cutting-edge QTS segmentation algorithms and can be applied to different types of QTSs. © 2023 IEEE.

关键词： Anomaly detection

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Numerical analysis of free convection under the influence of radiation and inclined MHD in a triangular cavity filled with hybrid nanofluid and a porous fin

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International Journal of Thermofluids 2024年 24卷

作者： Hassan, Ahmed M. Alomari, Mohammed Azeez Al-Salami, Qusay H. Alyousuf, Farah Q.A. Alqurashi, Faris Flayyih, Mujtaba A. Department of Mechanical Engineering University of Al-Qadisiyah Al-Qadisiyah58001 Iraq Department of Mechanical Engineering University of Al-Qadisiyah Ad-Diwaniyah58001 Iraq College of Engineering University of Warith Al-Anbiyaa Karbala Iraq Department of Business Administration College of Administrative and Financial Sciences Cihan University Erbil Iraq Department of Computer Network College of Engineering and Computer Science Lebanese French University Kurdistan Region Erbil Iraq Mechanical Engineering Dept. College of Engineering University of Bisha Bisha Saudi Arabia Biomedical Engineering Department College of Engineering and Technologies Al-Mustaqbal University Hillah Iraq

This study presents a novel investigation into the thermal behavior of a hybrid nanofluid (MgO − Ag − H2O) in natural convection around a porous fin in a triangular enclosure under the influence of radiation and magnetohydrodynamic effects. The research uniquely combines these complex phenomena, addressing a significant gap in the literature. This configuration has potential applications in advanced solar thermal collectors, electronic cooling systems for high-power devices, and compact heat exchangers in various industries. The main objectives are to understand how various parameters influence heat transfer and fluid flow behavior and to optimize the design for enhanced thermal performance. The study considers a range of variables including Rayleigh number (103 − − 106), Hartmann number (0 – 50), Aspect ratio (0.3 – 0.6), radiation parameters (Rd = 1 − − 5, λ = 1 − 5), and volume concentration (0- 0.05), which have been numerically analyzed using the finite element method (FEM). The findings reveal that increasing the Darcy number (Da) enhances heat transfer at low Rayleigh numbers (Ra = 103,104). However, at higher Ra (Ra = 106), the impact of Da becomes more complex, with a critical Da beyond which heat transfer efficiency decreases due to an increase in flow resistance. The nanoparticle volume concentration plays a vital role, as higher concentrations lead to improved heat transfer efficiency, especially at higher Ra, through enhanced thermal conductivity and thermal dispersion. The length of the porous fin greatly impacts fluid flow patterns and heat transfer rates, with longer fins creating more complex flow patterns, promoting enhanced heat transfer and stronger thermal plumes. Thermal radiation, represented by the radiation parameters (Rd and λ), significantly influences both the heat transfer rate and the convective flow patterns within the enclosure. This study also incorporates a comprehensive entropy generation analysis, providing novel insights into sys

关键词： Rayleigh number

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Maximizing Efficiency: Relocation and Deduplication for Result Caching in Distributed and Collaborative Edge Computing networks

Maximizing Efficiency: Relocation and Deduplication for Resu...

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IEEE Conference on Vehicular Technology (VTC)

作者： Abbas Yekanlou Jun Cai Samuel D. Okegbile Department of Electrical and Computer Engineering Network Intelligence and Innovation Lab (NI2L) Concordia University Montreal Canada

ISBN: (数字)9798331517786

ISBN: (纸本)9798331517793

With the rapid growth of internet of things (IoT) devices, edge computing has emerged as a crucial technology for delivering low-latency and resource-efficient services. However, the surge in edge computing capabilities poses challenges in efficiently managing result caching and deduplication to effectively utilize storage and processing resources. This study introduces a novel approach that harnesses an adaptive enhanced initiation genetic algorithm (AEIGA) for result deduplication/relocation in collaborative and distributed edge computing networks, with the goal of enhancing task offloading and result delivery. Our work proposes an optimization framework that integrates deduplication/relocation strategies to minimize redundancy, improve latency, and optimize storage across edge servers. The proposed AEIGA addresses the NP-hard nature of the formulated optimization problem by efficiently exploring the vast solution space to find optimal or near-optimal solutions. Simulation results demonstrate significant improvements of the proposed AEIGA in various network performance metrics. Our findings highlight the effectiveness of employing AEIGA for result deduplication/relocation in edge computing, offering a scalable solution to meet the escalating demands of IoT networks.

关键词： Vehicular and wireless technologies Simulation Collaboration Termination of employment Internet of Things Servers Surges Optimization Edge computing Genetic algorithms

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Propagating Threat Scores with a TLS Ecosystem Graph Model Derived by Active Measurements

Propagating Threat Scores with a TLS Ecosystem Graph Model D...

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network Traffic Measurement and Analysis Conference (TMA)

作者： Markus Sosnowski Patrick Sattler Johannes Zirngibl Tim Betzer Georg Carle Technical University of Munich Germany Department of Computer Engineering Chair of Network Architectures and Services TUM School of Computation Information and Technology

ISBN: (数字)9783903176645

ISBN: (纸本)9798350378887

The Internet is shaped by independent actors and heterogeneous deployments. With the wide adoption of Transport Layer Security (TLS), a whole ecosystem of intertwined entities emerged. Acquiring a comprehensive view allows searching for previously unknown malicious entities and providing valuable cyber-threat intelligence. Actively collected Internet-wide Domain Name System (DNS) and TLS meta-data can provide the basis for such large-scale analyses. However, in order to efficiently navigate the vast volumes of data, an effective methodology is required. This work proposes a graph model of the TLS ecosystem that utilizes the relationships between servers, domains, and certificates. A Probabilistic Threat Propagation (PTP) algorithm is then used to propagate a threat score from existing blocklists to related nodes. We conducted a one-year-long measurement study of 13 monthly active Internet-wide DNS and TLS measurements to evaluate the methodology. The latest measurement found four highly suspicious clusters among the nodes with high threat scores. External threat intelligence services were used to confirm a high rate of maliciousness in the rest of the newly found servers. With the help of optimized thresholds, we identified 557 domains and 11 IP addresses throughout the last year before they were known to be malicious. Up to 40% of the identified nodes appeared on average three months later on the input blocklist. This work proposes a versatile graph model to analyze the TLS ecosystem and a PTP analysis to help security researchers focus on suspicious subsets of the Internet when searching for unknown threats.

关键词： Analytical models Ecosystems Clustering algorithms Telecommunication traffic Data models Blocklists Internet

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A Novelty Decision-Making Based on Hybrid Indexing, Clustering, and Classification Methodologies: An Application to Map the Relevant Experts Against the Rural Problem

Decision Making: Applications in Management and Engineering

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Decision Making: Applications in Management and engineering 2024年第2期7卷 132-171页

作者： Faisal, Muhammad Rahman, Titik Khawa Abd Mulyadi, Ida Aryasa, Komang Irmawati Thamrin, Musdalifa Department of Computer Science STMIK Profesional Makassar Indonesia Department of Ph.D ICT AeU University Kuala Lumpur Malaysia Department of Informatics Engineering DIPA University Makassar Indonesia Department of Computer and Network Engineering Politeknik Negeri Ujung Pandang Indonesia Department of Informatics Management STMIK Profesional Makassar Indonesia

Exploring the potential of technology in addressing Sustainable Development Goals (SDGs) within intricate rural contexts holds paramount significance. Sustainable rural development holds profound significance for both developed and developing nations. This study was conducted to develop a methodological framework for placing experts with strategically relevant competencies to meet the specific needs of villages, thus enabling the practical application of their expertise in generating innovative solutions to problems in a village. This study entails several pivotal phases. Firstly, it constructs a Community Standard of Living Index (CSLI) using Delphi and Rank Reciprocal (RR). Secondly, it establishes village clustering through a hybrid Fuzzy C-Means (FCM), Self-Organizing Map (SOM), and Xie-Beni (XB). Thirdly, a classification of village development levels is created using Tsukamoto and Smallest of Maximum (SM). Finally, recommendations for placing experts in villages, aligning their skills with identified needs using the Cosine Similarity (CS). The results obtained are compared with factual data of each village to obtain relevant conclusions, where an accuracy value of 0.95 indicates a high success rate in the test results of the proposed technique. This study has the potential to significantly enhance decision-making by introducing opportunities for the development of hybrid methodologies in expert mapping for rural issues. © 2024 Regional Association for Security and crisis management. All rights reserved.

关键词： Rural areas

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Secured Cluster-Based Electricity Theft Detectors Against Blackbox Evasion Attacks

Secured Cluster-Based Electricity Theft Detectors Against Bl...

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Consumer Communications and networking Conference, CCNC IEEE

作者： Islam Elgarhy Ahmed T. El-Toukhy Mahmoud M. Badr Mohamed Mahmoud Mostafa M. Fouda Maazen Alsabaan Hisham A. Kholidy Department of Electrical and Computer Engineering Tennessee Tech. University Cookeville TN USA Department of Computer Systems Faculty of Computer and Information Sciences Ain Shams University Cairo Egypt Department of Electrical Engineering College of Engineering Al-Azhar University Cairo Egypt Department of Network and Computer Security College of Engineering SUNY Polytechnic Institute Utica NY USA Department of Electrical and Computer Engineering Idaho State University Pocatello ID USA Center for Advanced Energy Studies (CAES) Idaho Falls ID USA Department of Computer Engineering College of Computer and Information Sciences King Saud University Riyadh Saudi Arabia

In smart power grids, electricity theft causes huge economic losses to electrical utility companies. Machine learning (ML), especially deep neural network (DNN) models hold state-of-the-art performance in detecting electricity theft cyberattacks. However, DNN models are vulnerable to adversarial attacks, i.e., evasion attacks. In this work, we study the vulnerability of the DNN-based electricity theft detectors against evasion attacks and the influence of the model's regularization (generalization) on robustness. We cluster the power consumers and train a detector for each cluster, and compare the performance and robustness of this detector to a global detector that is trained on all the consumers, data. The results indicate that the cluster-based detector is not only more robust against evasion attacks but also enhances normal classification accuracy because its training data has more consumption pattern similarity compared to the training data of the global detector which requires higher level of regularization. Moreover, unlike the existing solutions that sacrifice the normal accuracy of the model to improve the robustness against evasion attacks, the proposed cluster-based detector holds state-of-the-art performance in both robustness and accuracy.

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A Cloud Computing User Experience Focused Load Balancing Method Based on Modified CMA-ES Algorithm 18th

A Cloud Computing User Experience Focused Load Balancing Met...

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18th International Conference on Green, Pervasive, and Cloud Computing, GPC 2023

作者： Luo, Jihai Dong, Chen Chen, Zhenyi Xu, Li Chen, Tianci College of Computer and Data Science - Fuzhou University Xueyuan Road Fujian Fuzhou360108 China Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou China Department of Computer Science and Engineering University of South Florida TampaFL33620 United States College of Computer and Cyber Security Fujian Normal University Fuzhou350007 China

ISBN: (纸本)9789819998951

The development of the software and hardware has brought about the abundance and overflow of computing resources. Many Internet companies can lease idle computing resources based on the peak and valley cycles of usage load to provide IaaS service. After the surging development, more and more companies are gradually paying attention to the specific user experience in cloud computing. But there are few works about that aspect in load balance problem. Therefore, we consider the load balancing resource allocation problem, from the perspective of user experience, mainly based on geographical distance and regional Equilibrium, combined with user usage habits, in multiple service periods. A detailed mathematical definition has been established for this problem. This article proposes a mathematical model for the problem, along with an modified CMA-ES (Covariance Matrix Adaptation Evolution Strategy) algorithm, called WS-ESS-CMA-ES algorithm, to allocate computing resources and solve the above problem. The process of using the proposed algorithm to solve the problem of cloud computing resource allocation in real scenarios is simulated, and compared with some other algorithms. The experiment results show that our algorithm performs well. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2024.

关键词： Cloud computing

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