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2nd International Conference on Cyber Resilience, ICCR 2024

作者： Alauthman, Mohammad Al-Qerem, Ahmad Alkasassbeh, Mouhammd Aslam, Nauman Aldweesh, Amjad University Of Petra Department Of Information Security Amman Jordan Zarqa University Faculty Of Information Technology Computer Science Department Zarqa Jordan Princess Summaya University For Technology Computer Science Department Amman Jordan Northumbria University Department Of Computer And Information Sciences Newcastle upon Tyne United Kingdom College Of Computing And It Shaqra University Shaqra Saudi Arabia

ISBN: (纸本)9798350394962

As cryptocurrencies have grown in prevalence and value, associated malware threats have rapidly emerged, exploiting vulnerabilities in wallets, markets, and decentralization mechanisms. This paper provides a comparative review of dominant cryptocurrency malware categories, including ransomware, crypto-jacking, supply chain hijacking, malicious browser extensions, phishing kits, and miner botnets. Analysis of behaviors, targets, propagation tactics, motivations, and detection challenges reveal commonalities and differences across threat types. Examination of recent literature enables comprehensive characterization of the cryptocurrency malware ecosystem to derive insights into evolution trajectories and systemic issues. Ransomware, crypto-jacking, and coordinated botnets represent prevalent threats highlighted, with malware innovation incentivized by pseudonymous payments, irreversible transactions, and criminal usage of mineable privacy-focused coins. Software supply chain attacks and phishing kits showcase propagation risks. Comparative assessment enables identifying research priorities like transaction tracing, behavioral monitoring, and predictive indicators to strengthen adaptive defenses against cryptocurrency-targeting threats. As cryptocurrency permeates society, proactive ecosystem perspectives will grow crucial in responding to malware creativity. © 2024 IEEE.

关键词： Ecosystems

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Collecting and analyzing key-value data under shuffled differential privacy

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Frontiers of computer Science 2023年第2期17卷 241-243页

作者： Ning WANG Wei ZHENG Zhigang WANG Zhiqiang WEI Yu GU Peng TANG Ge YU Faculty of Information Science and Engineering Ocean University of ChinaQingdao 266100China School of Computer Science and Engineering Northeastern UniversityShenyang 110819China Key Lab of Cryptologic Technology and Information Security Ministry of EducationShandong UniversityQingdao 266200China School of Cyber Science and Technology Shandong UniversityQingdao 266200China

1 Introduction Key-value data,as NoSQL data widely used in recent years,has been frequently collected and used for analysis in major websites and mobile *** frequency distribution estimation on the key domain,as well as the mean estimation of values associated with the same key,are crucial and worth billion dollars in the ***,in real applications,the key-value records are submitted by terminal devices of different *** collecting the information from users may pose serious risks on personal *** the other hand,the emerging shuffled differential privacy(SDP)model[1]is proposed to collect sensitive data from users.

关键词： NoSQL app value

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Secure and fast mobility handover scheme for IoMT in 5 G and future 6 G networks

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Alexandria Engineering Journal 2025年 128卷 127-143页

作者： Islam, Shayla Topazal, S.M. Hasan, Mohammad Kamrul Kolandaisamy, Raenu AL Abbas, Huda Saleh Vo, Nguyen Thakkar, Rahul Khan, Muhammad Attique Gupta, Deepak AlHammadi, Dina Abdulaziz Institute of Computer Science and Digital Innovation UCSI University Kuala Lumpur56000 Malaysia Center for Cyber Security Faculty of Information Science and Technology the National University of Malaysia Selangor Bangi43600 Malaysia Department of computer science College of Computer Science and Engineering Taibah University Madina Saudi Arabia Victorian Institute of Technology Melbourne Australia Center of AI College of Computer Engineering and Science Prince Mohammad Bin Fahd University Al-Khobar Saudi Arabia Maharaja Agrasen Institute of Technology Delhi India Department of Information Systems College of Computer and Information Sciences Princess Nourah bint Abdulrahman University P.O.Box 84428 Riyadh11671 Saudi Arabia

The emergence of 5 G and 6 G networks promises significant advancements in the Internet of Medical Things (IoMT) by enabling real-time medical data transmission and continuous connectivity for mobile healthcare devices. However, traditional Handover (HO) mechanisms face latency, security, energy consumption, and scalability challenges when supporting fast-moving and dense IoMT environments. This paper presents a secure and fast mobility handover scheme for IoMT in 5 G and future 6 G networks, incorporating a predictive algorithm, Blockchain-Based (BC-B) decentralized authentication mechanism, and AI-driven anomaly detection. The predictive algorithm anticipates device movement and pre-allocates resources at the target Base Station (BS), reducing HO latency by 20–30 % in 5 G and up to 40 % in 6 G. Meanwhile, blockchain-based authentication ensures tamper-proof and decentralized security, reducing vulnerability to cyberattacks by 90 % compared to traditional methods. Additionally, the energy consumption during HO events is reduced by 20–35 %, dropping from 15 mJ in traditional schemes to 12 mJ in 5 G and further to 10 mJ in 6 G. The scheme also incorporates AI-driven anomaly detection, achieving an accuracy of 96.7 %, ensuring proactive identification and mitigation of potential security threats. The analyzed scheme maintains consistent throughput with less than 5 % reduction and scales effectively for up to 300 devices per cell. The simulation results demonstrate significant improvements across key performance metrics, including latency, security, energy efficiency, and scalability, making this approach highly suitable for critical IoMT applications such as telesurgery, remote patient monitoring, and innovative healthcare environments. These advancements position the analyzed framework as a cornerstone for next-generation (NG) healthcare networks. © 2025 The Authors

关键词： Remote patient monitoring

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Traffic Congestion Prediction Using Machine Learning: Amman City Case Study

Traffic Congestion Prediction Using Machine Learning: Amman ...

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2024 International Conference on Medical Imaging, Electronic Imaging, information Technologies, and Sensors, MIEITS 2024

作者： Arabiat, Areen Hassan, Mohammad Al Momani, Omar Department of Communication and Computer Engineering Faculty of Engineering Al Ahliyya Amman University Amman19111 Jordan Department of Networks security Faculty of Information Technology The World Islamic Sciences and Educations University WISE Amman Jordan

ISBN: (纸本)9781510680814

Traffic congestion leads to wasted time, pollution, and increased fuel usage. Traffic congestion prediction has become a developing research topic in recent years, particularly in the field of machine learning (ML). The evaluation of various traffic parameters is used to predict traffic congestion by relying on historical data. In this study, we will predict traffic congestion in Amman city specifically at Northbound Street at 8th Circle using different ML classifiers. The 8th Circle links four main streets: Westbound, Northbound, Eastbound, and Southbound. Because of the heavy traffic on Northbound, it was chosen to predict traffic congestion. Datasets were collected from the greater Amman municipality hourly based. Datasets were divided and per-process to be understandable. The Logistic Regression, Decision Tree (DT), Random Forest, and multilayer perceptron (MLP) classifiers have been chosen to predict traffic congestion at Northbound Street linked with the 8th Circle. Four experiments were run under The Waikato Environment for Knowledge Analysis (WEKA) tools to find the best classifiers to predict traffic congestion at Northbound. The chosen classifiers have been evaluated using F-Measure, sensitivity, precision, and accuracy evaluation metrics. The obtained results from all experiments have demonstrated the Logistic Regression is the best classifier to predict traffic congestion. The accuracy of Logistic Regression to predict traffic congestion at Northbound Street was97.6%. © 2024 SPIE.

关键词： Traffic congestion

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Enhancing Convergence Efficiency in Federated Learning Models 25

Enhancing Convergence Efficiency in Federated Learning Model...

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25th International Arab Conference on information technology, ACIT 2024

作者： Samara, Ghassan Elhilo, Ala'Mazen Aljaidi, Mohammad Alamleh, Amneh Al-Mousa, Mohammad Rasmi Batyha, Radwan Alazaidah, Raed Bani-Bakr, Alaa Elrashidi, Ali Zarqa University Faculty of It Department of Computer Science Zarqa Jordan Zarqa University Faculty of Information Technology Zarqa Jordan Zarqa University Faculty of Information Technology Department of Data Science and Ai Zarqa Jordan Zarqa University Department of Cybersecurity Zarqa Jordan Applied Science Private University Faculty of Information Technology Amman Jordan Zarqa University Faculty of It Department of Cyber Security Zarqa Jordan University of Business and Technology Department of Electrical Engineering Jeddah21432 Saudi Arabia

ISBN: (纸本)9798331540012

This study evaluates federated learning models' performance compared to centralized models using the MNIST and CIFAR-10 datasets. Four models were tested: two Federated Learning models and two centralized models. The evaluation focused on accuracy, training rounds, and CO2 emissions, to optimize convergence and reducing energy consumption while maintaining strong model performance. The results showed that Federated learning models outperformed centralized models, with the Federated Learning MNIST model achieving 98.79% accuracy compared to 87.93% for the centralized model. Also, the accuracy of the Federated Learning CIFAR-10 model achieved 91.20% accuracy, exceeding the centralized model's 90.14%. The most accuracy improvements happened in the early training rounds. An adjusted hill-climbing technique was used to the centralized models, decreasing the number of iterations and CO2 emissions without significantly reducing accuracy. © 2024 IEEE.

关键词： Adversarial machine learning

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Personalized Federated Learning for Heterogeneous Residential Load Forecasting

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Big Data Mining and Analytics 2023年第4期6卷 421-432页

作者： Xiaodong Qu Chengcheng Guan Gang Xie Zhiyi Tian Keshav Sood Chaoli Sun Lei Cui the Shanxi Key Laboratory of Advanced Control and Equipment Intelligence Taiyuan University of Science and TechnologyTaiyuan 030024China the Faculty of Engineering and Information Technology University of Technology SydneyUltimo 2007Australia the Centre for Cyber Security Research and Innovation Deakin UniversityMelbourne 3125Australia the School of Computer Science and Technology Taiyuan University of Science and TechnologyTaiyuan 030024China

Accurate load forecasting is critical for electricity production,transmission,and *** learning(DL)model has replaced other classical models as the most popular prediction ***,the deep prediction model requires users to provide a large amount of private electricity consumption data,which has potential privacy *** nodes can federally train a global model through aggregation using federated learning(FL).As a novel distributed machine learning(ML)technique,it only exchanges model parameters without sharing raw ***,existing forecasting methods based on FL still face challenges from data heterogeneity and privacy ***,we propose a user-level load forecasting system based on personalized federated learning(PFL)to address these *** obtained personalized model outperforms the global model on local ***,we introduce a novel differential privacy(DP)algorithm in the proposed system to provide an additional privacy *** on the principle of generative adversarial network(GAN),the algorithm achieves the balance between privacy and prediction accuracy throughout the *** perform simulation experiments on the real-world dataset and the experimental results show that the proposed system can comply with the requirement for accuracy and privacy in real load forecasting scenarios.

关键词： load forecasting personalized federated learning differential privacy

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Assessing the Effects of Blackhole Attacks on MANET Reliability and security

Assessing the Effects of Blackhole Attacks on MANET Reliabil...

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

作者： Ahmed, Amjed Abbas Fadhil, Saif Aamer Najim, Ali Hamza Ali Alheeti, Khattab M. Mohd Satar, Nurhizam Safie Abdallah Hashim, Aisha Hassan Center for Cyber Security Faculty of Information Science and Technology Universiti Kebangsaan Malaysia Bangi Malaysia Department of Computer Techniques Engineering Baghdad Iraq Department of Computer Technical Engineering Baghdad10011 Iraq College of Computer and Information Technology Computer Networking Systems Department University of Anbar Anbar Iraq Bangi43600 Malaysia International Islamic University Malaysia Faculty of Engineering Department of Electrical and Computer Engineering Selangor Malaysia

ISBN: (纸本)9798350369106

Mobile Ad Hoc Networks (MANETs) are characterized by some important attributes, including infrastructure, mobile, and dynamic nature, which makes them have vast applications in different areas of computer networks. The black hole attack falls among the strongly criticized security threats within the framework of the MANETs. This deceptive behavior captures data packets from a source destined for the malicious node and then declines them when they are received. This paper comprehensively reviews prior studies done to analyze and mitigate black hole attacks in MANETs. Besides this, we will conduct a comparative analysis of the research above projects. The present paper extends prior work by actually performing the black hole assault simulations in a MANETs environment. This is done with the aid of the NS2 simulator and the Ad Hoc On-Demands Distance Vector (AODV) routing protocol. In this study, the black hole attack is analyzed and the consequences of the attack are assessed in terms of the system performance and security. We analyze crucial parameters like end-to-end latency, PDR, packet drop, and throughput to evaluate the consequences of this assault. From our results derived from the analysis of our simulations, we aim to present substantial points to do with the impacts of blackhole attacks on MANETs. In this way, our goal is to participate in the perpetually evolving efforts aimed at enhancing the security and reliability of such dynamic networks. © 2024 IEEE.

关键词： Mobile ad hoc networks

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Linear Random Early Detection for Congestion Control at the Router Buffer

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Informatica (Slovenia) 2022年第5期46卷 105-114页

作者： Abu-Shareha, Ahmad Adel Al-Kasasbeh, Basil Shambour, Qusai Y. Abualhaj, Mosleh M. Alsharaiah, Mohmmad Abdalla Al-Khatib, Sumaya N. Department of Computer Science Faculty of Information Technology Al-Ahliyya Amman University Amman Jordan Riyadh Saudi Arabia Department of Software Engineering Faculty of Information Technology Al-Ahliyya Amman University Amman Jordan Department of Networks and Information Security Faculty of Information Technology Al-Ahliyya Amman University Amman Jordan Department of Data Science and Artificial Intelligence Faculty of Information Technology Al-Ahliyya Amman University Amman Jordan

Active Queue Management (AQM) methods control the router's buffer to maintain high network performance and control congestion at the router buffer. Random Early Detection (RED) method is the most well-known and the most utilized AQM. RED suffers from a high dropping rate, which motivates the later AQM methods to use more complex processes, which reach the limits of using fuzzy systems as a processing technique. Yet, high computational cost affects the router's performance specifically and the network as a whole, with so-called processing delay. In this paper, a linear version of RED (LRED) is presented to reduce the computational cost of the original RED and maintain the network performance in terms of throughput, delay, dropping, and loss. LRED is built based on two distinctive features, simplifying the congestion indicator calculation and reducing the operations in calculating the dropping probability. The experimental results showed that the proposed method reduces the delay and the processing time while maintaining the throughput and loss of the RED method. © 2022 Slovene Society Informatika. All rights reserved.

关键词： Network performance

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Post-Quantum Group Key Management in IoTs 25

Post-Quantum Group Key Management in IoTs

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25th International Multi Topic Conference, INMIC 2023

作者： Samiullah, Fouzia Gan, Ming-Lee Akleylek, Sedat Aun, Yichiet Universiti Tunku Abdul Rahman Faculty of Information and Communication Technology Perak Kampar31900 Malaysia University of Tartu Security and Theoretical Computer Science Estonia

ISBN: (纸本)9798350317701

In our interconnected society, effective group communication is crucial for facilitating the efficient and secure interchange of data between multiple endpoints, particularly within the complex landscapes of the IoTs and sensor networks. Group Key Management (GKM) arises as a fundamental element for ensuring safety amidst these complexities. However, the emergence of quantum computers has sparked worries about the vulnerability of current GKM schemes that depend on asymmetric cryptography, also known as public-key cryptography. This study investigates post-quantum GKM schemes, concentrating on their integration in the IoT context and addressing the underexplored field of GKM in the context of quantum computing advancements. By examining the security features of GKM protocols, particularly in relation to their resistance to quantum attacks, this study not only identifies current challenges but also outlines a path for future research directions, posing light on potential pathways in this changing landscape. © 2023 IEEE.

关键词： Public key cryptography

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A secure quantum homomorphic encryption ciphertext retrieval scheme

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Soft Computing 2025年第3期29卷 1497-1509页

作者： Cheng, Zhen-Wen Chen, Xiu-Bo Xu, Gang Chang, Yan Miao, Li-Hua Yang, Yi-Xian Wang, Ya-Lan School of Data and Computer Science Shandong Women’s University Jinan250300 China Information Security Center State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing100876 China School of Information Science and Technology North China University of Technology Beijing100144 China School of Cybersecurity Chengdu University of Information Technology Chengdu610225 China Huawei Technologies Co. Ltd Shenzhen518129 China Department of Computer Science Faculty of Engineering and Physical Sciences University of Surrey Surrey GuildfordGU2 7XH United Kingdom

In a recent paper (Gong et al. Quantum Inf Process 19:3, 2020), a novel ciphertext retrieval scheme based on the Grover algorithm and quantum homomorphic encryption was presented. In this scheme, when the server performs the operation of marking the solution on the user’s encrypted state in the Grover iteration, it needs to remove many gate-errors generated in the homomorphic evaluation of the T gate. And the server could judge this specific solution from the quantum circuit of marking the solution. It makes this scheme unable to achieve the low-cost and secure ciphertext retrieval. Therefore, we improve the Gong et al.’s scheme and propose a secure quantum homomorphic encryption ciphertext retrieval scheme. In our scheme, the trusted third party is introduced to cooperate with the server to execute the Grover algorithm. In each Grover iteration, the trusted third party can quickly mark the solution on the plaintext state, encrypt the marked state, and transmit it to the server. Then the server performs the remaining operations of this Grover iteration on the encrypted state. The trusted third party finally decrypts the iterated state. This cooperative approach ensures that the number of auxiliary qubits required and extra quantum gates executed in our scheme are lower than the Gong et al.’s scheme. By analyzing the security of our scheme, we confirm that the server and the trusted third party will not be informed of this solution. Thus, our scheme realizes the secure ciphertext retrieval with low computational overhead. We utilize IBM’s Qiskit framework to simulate our scheme, and the experimental result shows that our scheme is correct. It is worth noting that the low-cost and secure ciphertext retrieval will play a crucial role in modern information security and privacy protection. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.

关键词： Ciphertext

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