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25th International Arab Conference on Information Technology, ACIT 2024

作者： Badarneh, Hazem Jihad Attiany, Loai Ahmad Asassfeh, Mahmoud Al-Shaikh, Ala'A Afaneh, Suha Abu Shquier, Mohammed M. Al-Milli, Nabeel Elrashidi, Ali Samara, Ghassan Zarqa University Department of Cybersecurity Zarqa Jordan Jerash University Department of Computer Science Jerash Jordan Zarqa University Department of Computer Science Zarqa Jordan University of Business and Technology Department of Electrical Engineering Jeddah21432 Saudi Arabia

ISBN: (纸本)9798331540012

In this paper, a review of penetration tests and their power is conducted to give a holistic understanding of the secure network. Furthermore, this paper introduces a full knowledge of the tester methods and the performance and the point of strengths, the weaknesses, and how we understand the process and proceed for testing or securing your network. © 2024 IEEE.

关键词： Network intrusion

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Positioning Control for Voice Coil Motor Using Unbalanced Multilevel Inverter Topology

Positioning Control for Voice Coil Motor Using Unbalanced Mu...

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2025 IEEE International Conference on Mechatronics, ICM 2025

作者： Takei, Gaku Obara, Hidemine Haneda, Ryo Nozaki, Takahiro Graduate School of Science and Technology Keio University Yokohama Japan Yokohama National University Department of Physics Electrical and Computer Engineering Yokohama Japan Keio University Department of System Design Engineering Yokohama Japan

ISBN: (纸本)9798331533892

This article proposes a new multi-level inverter (MLI) based on flying capacitor inverter (FCI), with focused on high-precision positioning control for a voice coil motor (VCM). Multi-level inverters (MLIs) have the characteristic that increasing the number of output voltage levels reduces the output voltage error. However, as the number of levels increases, the number of circuit elements increases, and the circuit becomes more complex. In addition, the output voltage amplitude for each level is constant in conventional inverters. As a result, such as high-precision positioning control of VCM, designing an appropriate inverter for a reference wave that contains a large amount of low-voltage components increases the number of circuit elements. Therefore, this paper proposes a MLI with two unbalanced dc-input voltages, referred to as unbalanced MLIs (UMLIs) hereinafter. This topology uses two different dc-input voltages, and it is possible to reduce the output voltage level in extremely low-voltage range compared to other voltage levels. Simulation results when the high-precision positioning control of the VCM confirm that the proposed UMLIs improves the control performance compared to the conventional FCIs with equal number of output voltage levels and uniform level widths. © 2025 IEEE.

关键词： motion control Multi-level inverter voice coil motor

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Cloud Security: Analyzing Threats and Challenges 25

Cloud Security: Analyzing Threats and Challenges

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25th International Arab Conference on Information Technology, ACIT 2024

作者： Asassfeh, Mahmoud Rajallah Abu Elsoud, Esraa Albilasi, Sultan Mohammed Alqammaz, Abdullah Al-Shaikh, Ala'a Afaneh, Suha Doumi, Ahmad Bany Elrashidi, Ali Samara, Ghassan Zarqa University Department of Cybersecurity Zarqa Jordan Zarqa University Department of Computer Science Zarqa Jordan Jerash University Department of Computer Science Jerash Jordan University of Business and Technology Department of Electrical Engineering Jeddah21432 Saudi Arabia

ISBN: (纸本)9798331540012

The rapid embrace of cloud computing has changed the IT environment, providing unmatched scalability, adaptability, and cost-effectiveness, since cloud services brought in around 631.84 billion dollars globally in 2023, and they will expand at a rapid rate of 18.49% through 2029. With 45.58% of global revenues. Nevertheless, as companies move their essential operations and sensitive information to the cloud more and more, the shared nature of cloud environments creates important security challenges. This paper presents an extensive examination of the security vulnerabilities, challenges, strategies, and countermeasures pertinent to cloud computing since cloud computing includes several types of administration that are made available to service users online, such as programming, gathering, and coordinating equipment *** is necessary to safeguard this data from threats like denial of service attacks and unauthorized access. © 2024 IEEE.

关键词： Cloud security

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A Novel Hybrid Architecture for Superior IoT Threat Detection through Real IoT Environments

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computers, Materials & Continua 2024年第11期81卷 2299-2316页

作者： Bassam Mohammad Elzaghmouri Yosef Hasan Fayez Jbara Said Elaiwat Nisreen Innab Ahmed Abdelgader Fadol Osman Mohammed Awad Mohammed Ataelfadiel Farah H.Zawaideh Mouiad Fadeil Alawneh Asef Al-Khateeb Marwan Abu-Zanona Department of Computer Science Faculty of Computer Science and Information TechnologyJerash UniversityJerash26150Jordan Computer Engineering Department College of Engineering&Information Technology Buraydah CollegesBuraydah51418Saudi Arabia Faculty of Architecture and Design Al-Zaytoonah University of JordanAmman11733Jordan Department of Computer Science and Information Systems College of Applied SciencesAlMaarefa UniversityDiriyahRiyadh13713Saudi Arabia Applied College King Faisal UniversityAl-Ahsa31982Saudi Arabia Department of Business Intelligence and Data Analysis Faculty of Financial Sciences and BusinessIrbid National UniversityIrbid21110Jordan Faculty of Information Technology Ajloun National UniversityAjlun26810Jordan Department of Management Information Systems College of Business AdministrationKing Faisal UniversityAl-Ahsa31982Saudi Arabia

As the Internet of Things(IoT)continues to expand,incorporating a vast array of devices into a digital ecosystem also increases the risk of cyber threats,necessitating robust defense *** paper presents an innovative hybrid deep learning architecture that excels at detecting IoT threats in real-world *** proposed model combines Convolutional Neural Networks(CNN),Bidirectional Long Short-Term Memory(BLSTM),Gated Recurrent Units(GRU),and Attention mechanisms into a cohesive *** integrated structure aims to enhance the detection and classification of complex cyber threats while accommodating the operational constraints of diverse IoT *** evaluated our model using the RT-IoT2022 dataset,which includes various devices,standard operations,and simulated *** research’s significance lies in the comprehensive evaluation metrics,including Cohen Kappa and Matthews Correlation Coefficient(MCC),which underscore the model’s reliability and predictive *** model surpassed traditional machine learning algorithms and the state-of-the-art,achieving over 99.6%precision,recall,F1-score,False Positive Rate(FPR),Detection Time,and accuracy,effectively identifying specific threats such as Message Queuing Telemetry Transport(MQTT)Publish,Denial of Service Synchronize network packet crafting tool(DOS SYN Hping),and Network Mapper Operating System Detection(NMAP OS DETECTION).The experimental analysis reveals a significant improvement over existing detection systems,significantly enhancing IoT security *** our experimental analysis,we have demonstrated a remarkable enhancement in comparison to existing detection systems,which significantly strength-ens the security standards of *** model effectively addresses the need for advanced,dependable,and adaptable security solutions,serving as a symbol of the power of deep learning in strengthening IoT ecosystems amidst the constantly evolving cyber threat *** achievemen

关键词： A hybrid deep learning model IoT threat detection real IoT environments cybersecurity attention mechanism

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Energy-Efficient Parallel Approach for Cloud Service Broker Scheduling 25

Energy-Efficient Parallel Approach for Cloud Service Broker ...

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25th International Arab Conference on Information Technology, ACIT 2024

作者： Khattab, Hebatullah Al-Shaikh, Ala'a Al-Sherideh, Ala'a Saeb Elrashidi, Ali Zarqa University Department of Computer Science Zarqa Jordan Zarqa University Department of Cybersecurity Zarqa Jordan University of Business and Technology Department of Electrical Engineering Jeddah Saudi Arabia

ISBN: (纸本)9798331540012

Cloud Services Brokers (CSBs) manage and organize access to the resources offered by Cloud Service Providers (CSP). As the number of cloud services and users is growing dramatically, operation costs increase proportionally, and that might incur a degradation in the Quality of Service (QoS), which leads to Service Level Agreement (SLA) violations. In this paper, a parallel hybrid metaheuristic approach based on Hill Climbing (HC) and the Greedy Approach is proposed. The approach aims to create energy-efficient schedules that allow CSBs to manage access to cloud resources. The approach was evaluated in terms of parallel runtime, speedup, and energy efficiency. The approach achieved 26x speedup using the same 256 KB dataset on 256 processors. It was also proved that the proposed algorithm is energy efficient. © 2024 IEEE.

关键词： Cloud platforms

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SyREC: A Symbolic-Regression-Based Ensemble Combiner 36

SyREC: A Symbolic-Regression-Based Ensemble Combiner

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36th IEEE International Conference on Tools with Artificial Intelligence, ICTAI 2024

作者： Fong, Kei Sen Motani, Mehul Department of Electrical and Computer Engineering Singapore College of Design and Engineering Singapore Institute of Data Science Singapore N.1 Institute for Health Singapore National University of Singapore Singapore

ISBN: (纸本)9798331527235

Symbolic Regression (SR) is the task of finding a concise white-box mathematical expression that fulfills a given machine learning (ML) objective. In this work, we introduce the first-of-its-kind SR-based ensemble combiner (SyREC) for ML which utilizes the quasi-arithmetic mean (parameterized by a function f) as an ensemble combiner and discovers f via SR. Our Sy REC demonstrates advantages over existing ensemble combiners, i.e., averaging methods and meta-learners. Compared to averaging methods, Sy REC allows for increased combiner complexity via exploring a large function class and provides a learning process to tune the ensemble combiner to the specific dataset. Compared to meta-learners, SyREC preserves important ensemble properties, like idempotency, monotonicity and boundedness, which produce predictions more consistent with the base models. We note SyREC produces a white-box ensemble combiner that lies in the intersection of both categories of existing combiners, addressing the weaknesses while capturing the strengths of each category. To evaluate Sy REC, we present experiments on 16 open-source PMLB datasets and commonly used base models, including a large variety of experiments and analyses across different settings: (i) regression and classification tasks, (ii) equally weighted combiners and custom-weighted combiners, (iii) homogeneous and heterogeneous ensembles, (iv) sequential and parallel ensembles. We find that in all these settings, SyREC shows consistent improvement over existing ensembling approaches. This indicates that SyREC is a robust ensemble combiner, which produces explainable and consistent ensemble predictions. © 2024 IEEE.

关键词： Contrastive Learning

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A Novel Cellular Automaton Model for Optimized Multi-Exit Crowd Evacuation in High-Density Environments 25

A Novel Cellular Automaton Model for Optimized Multi-Exit Cr...

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25th International Arab Conference on Information Technology, ACIT 2024

作者： Alidmat, Omar Khair Alla Abuowaida, Suhaila Alhenawi, Esraa Awwad, Hebatullah Khattab Al-Momani, Adai Yusof, Umi Kalsom Elrashidi, Ali Zarqa University Computer Science Department Zarqa Jordan Al-Albayt University Computer Science Department Mafraq Jordan Universiti Sains Malaysia School of Computer Sciences Pulau Pinang Malaysia University of Business and Technology Department of Electrical Engineering Jeddah Saudi Arabia

ISBN: (纸本)9798331540012

Evacuation systems play a critical role in mitigating casualties and property damage during emergency situations. Comprehending evacuee behavior in overcrowded scenarios is vital for developing effective evacuation strategies. Nonetheless, evacuating large crowds from buildings with multiple exits presents a significant challenge, particularly when exits are asymmetrical and crowds are dense. This paper proposes an innovative two-dimensional cellular automaton model for multi-exit evacuation, designed to streamline evacuee decision-making in an asymmetrical exit layout within dense crowds. The proposed model addresses limitations in traditional evacuation models by incorporating dynamic counting areas and enhanced parameters, including congestion, distance, and empty parameters. These improvements enable the model to more accurately represent evacuee behavior during emergency situations, particularly in environments with multiple exits. The model effectively captures complex crowd dynamics, such as arching and shockwaves, and adjusts exit selection based on real-time congestion levels. Simulations demonstrate that the proposed model significantly reduces evacuation time, unit evacuation time, and travel distance compared to existing models. Key findings highlight the model's ability to mitigate bottlenecks at congested exits, optimize exit utilization, and enhance overall evacuation efficiency. Future research aims to refine the model with real-world data for broader applications in diverse settings like stadiums and airports, thereby improving safety protocols during emergencies. © 2024 IEEE.

关键词： Decision making

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Hardness and Approximation for the Star β -Hub Routing Cost Problem in Δβ -Metric Graphs 29th

Hardness and Approximation for the Star β -Hub Routing C...

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29th International Computing and Combinatorics Conference, COCOON 2023

作者： Tsai, Meng-Shiou Hsieh, Sun-Yuan Hung, Ling-Ju Department of Computer Science and Information Engineering National Cheng Kung University Tainan Taiwan Department of Creative Technologies and Product Design National Taipei University of Business Taoyuan City Taiwan

ISBN: (纸本)9783031491894

Minimizing transportation costs through the design of a hub-and-spoke network is a crucial concern in hub location problems (HLP). Within the realm of HLP, the Δβ -Star p -Hub Routing Cost Problem (Δβ -SpHRP) represents an open problem stemming from the Star p -Hub Routing Cost Problem (SpHRP) discussed in a publication by [Yeh et al., Theoretical computer science, 2022]. The Δβ -SpHRP deals with a specific vertex c, a positive integer p, and a Δβ -metric graph denoted as G, which is an undirected, complete, and weighted graph adhering to the β -triangle inequality. The objective is to identify a spanning tree T that satisfies the following conditions: it is rooted at c, contains exactly p hubs adjacent to c, and assigns each remaining vertex to a hub while minimizing the routing cost of T. This paper expands the input instances from metric graphs to Δβ -metric graphs. Our research demonstrates that SpHRP is NP-hard for any β>12, indicating that SpHRP remains NP-hard for various subclasses of metric graphs. For approximation algorithms, we introduce two approaches that improve upon previous results, particularly when β is close to 12. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Stars

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Cognitive Congestion Alleviation Framework in IoT-Enabled WSN for Next-Gen Intelligent Transport Systems via Optimized Capsule Attention Network 3

Cognitive Congestion Alleviation Framework in IoT-Enabled WS...

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3rd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Mohandas, R. Sudha, Y. Narmatha, P. Venkata Prasad, P. Manikandan, N. Department of Electronics & Communication Engineering Chennai Institute of Technology Tamil Nadu Chennai India Department of Computer Science and Engineering Presidency University Karnataka Bangalore India Department of Computer Science and Engineering Sri Sairam College of Engineering Karnataka Bangalore India Department of Electrical and Electronics Engineering Chaitanya Bharathi Institute of Technology Telangana Hyderabad India Department of Business Administration Kalasalingam Business School Kalasalingam Academy of Research and Education Viruthunagar Krishnankoil India

ISBN: (纸本)9798331527549

The rapid advancement of Intelligent Transportation Systems (ITS) has heightened the importance of reliable, real-time data transmission in Wireless Sensor Networks (WSNs). However, congestion in data-heavy Internet of Things (IoT)-enabled networks remains a critical challenge, impacting communication efficiency and decision-making in ITS. In high-density IoT-ITS applications, traditional congestion control methods are insufficient to address the complexity and dynamics of data flows. Existing solutions often fail to adapt in real time to varying traffic loads, leading to delays and data loss. There is a strong need for a congestion alleviation framework capable of intelligently prioritizing data while dynamically managing network resources to ensure seamless information exchange in large-scale deployments. This study presents a Cognitive Congestion Alleviation Framework in IoT-Enabled WSN for Next-Gen Intelligent Transport Systems via Optimized Capsule Attention Network (V-CapMiAN-Parr), which combines the Vectorized Adaptive Capsule Neural Network (V-AdCapNet) and Multi-instance Attention Network (MAN), fine-tuned by the Parrot Optimizer (ParrOpt). This integration aims to effectively detect, mitigate, and prevent congestion through an advanced capsule-based attention mechanism with adaptive optimization. The suggested V-CapMiAN-Parr framework demonstrated significant improvements in congestion control, achieving data throughput efficiencies above 99.7%, packet delivery rates above 99.5%, and network reliability reaching 99.2% under high traffic loads. The model’s adaptive weighting mechanism ensures real-time responsiveness and reliability, crucial for next-gen ITS applications. The V-CapMiAN-Parr framework effectively addresses congestion issues in IoT-enabled WSNs, providing a robust, scalable solution for ITS applications. This cognitive framework’s advanced data prioritization and adaptive optimization capabilities are well-suited for enhancing the performanc

关键词： Traffic congestion

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IoT Security Based On Lightewight Cryptographic (LWC) Algorithms: A survey 25

IoT Security Based On Lightewight Cryptographic (LWC) Algori...

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25th International Arab Conference on Information Technology, ACIT 2024

作者： Al-Mousa, Mohammad Rasmi Suleiman Al-Zou'bi, Samah Askar, Sami Alqawasmi, Khaled Al-Sherideh, Ala'a Saeb Samara, Ghassan Almatarneh, Rami Khouj, Mohammed Odeh, Mahmoud Zarqa University Department of Cybersecurity Zarqa Jordan Zarqa University Department of Computer Science Zarqa Jordan University of Business and Technology Department of Electrical Engineering Jeddah21432 Saudi Arabia

ISBN: (纸本)9798331540012

The Internet of Things is millions of devices that communicate with each other and transmit sensitive information between them. The information is collected through the sensors on these devices, and is transmitted between the devices via the Internet. However, ensuring security in terms of authenticity, confidentiality, integrity, authentication, and data integrity is considered one of the challenges facing the Internet of Things, because ensuring these matters is through encryption, and traditional encryption algorithms are not suitable and cannot be applied in the Internet of Things environment, which is characterized by limited resources, so it was necessary From searching for algorithms suitable for the limited Internet of Things environment. In this paper, a comparison will be made between the literature studies conducted in this field in terms of challenges and possible solutions, in an attempt to find the gaps in them. © 2024 IEEE.

关键词： Encryption algorithms

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