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A Verifiably Secure and Lightweight Device-to-Device (D2D) Authentication Protocol for the Resource-Constrained IoT Networks

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IEEE Access 2025年 13卷 92982-92996页

作者： Alzahrani, Naif University of Jeddah College of Computer Science and Engineering Department of Cybersecurity Saudi Arabia

Due to the growth of Internet of Things (IoT) devices on networks with limited resources, developing a safe and effective authentication method for Device-to-Device (D2D) communication has become necessary. Conventional authentication methods frequently have high communication and processing overhead, which makes them inappropriate for resource-constrained IoT networks. An innovative, lightweight, and verifiably secure D2D authentication protocol designed for resource-constrained IoT networks is proposed in this research. The protocol minimizes energy and computational costs while ensuring strong security by utilizing mutual authentication and effective cryptographic primitives. According to security analysis, the proposed protocol resists widespread attacks, such as DoS, replay, man-in-the-middle, and impersonation attacks. Additionally, the accuracy and security features of the protocol are confirmed by formal verification using commonly used techniques such as the ROR model and ProVerif validation. It is a feasible option for IoT deployments as performance examinations show considerable improvements over current systems regarding energy consumption, communication, computation, storage, and bandwidth utilization. © 2013 IEEE.

关键词： Authentication Protocol

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Preserving Data Secrecy and Integrity for Cloud Storage Using Smart Contracts and Cryptographic Primitives

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computers, Materials & Continua 2024年第5期79卷 2449-2463页

作者： Maher Alharby Cybersecurity Department College of Computer Science and EngineeringTaibah UniversityMedina42353Saudi Arabia

Cloud computing has emerged as a viable alternative to traditional computing infrastructures,offering various ***,the adoption of cloud storage poses significant risks to data secrecy and *** article presents an effective mechanism to preserve the secrecy and integrity of data stored on the public cloud by leveraging blockchain technology,smart contracts,and cryptographic *** proposed approach utilizes a Solidity-based smart contract as an auditor for maintaining and verifying the integrity of outsourced *** preserve data secrecy,symmetric encryption systems are employed to encrypt user data before outsourcing *** extensive performance analysis is conducted to illustrate the efficiency of the proposed ***,a rigorous assessment is conducted to ensure that the developed smart contract is free from vulnerabilities and to measure its associated running *** security analysis of the proposed system confirms that our approach can securely maintain the confidentiality and integrity of cloud storage,even in the presence of malicious *** proposed mechanism contributes to enhancing data security in cloud computing environments and can be used as a foundation for developing more secure cloud storage systems.

关键词： Cloud storage data secrecy data integrity smart contracts cryptography

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HybridEdge: A Lightweight and Secure Hybrid Communication Protocol for the Edge-Enabled Internet of Things

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computers, Materials & Continua 2025年第2期82卷 3161-3178页

作者： Amjad Khan Rahim Khan Fahad Alturise Tamim Alkhalifah Department of Computer Science Abdul Wali Khan UniversityMardan23200Pakistan Department of Cybersecurity College of ComputerQassim UniversityBuraydahSaudi Arabia Department of Computer Engineering College of ComputerQassim UniversityBuraydahSaudi Arabia

The Internet of Things (IoT) and edge-assisted networking infrastructures are capable of bringing data processing and accessibility services locally at the respective edge rather than at a centralized module. These infrastructures are very effective in providing a fast response to the respective queries of the requesting modules, but their distributed nature has introduced other problems such as security and privacy. To address these problems, various security-assisted communication mechanisms have been developed to safeguard every active module, i.e., devices and edges, from every possible vulnerability in the IoT. However, these methodologies have neglected one of the critical issues, which is the prediction of fraudulent devices, i.e., adversaries, preferably as early as possible in the IoT. In this paper, a hybrid communication mechanism is presented where the Hidden Markov Model (HMM) predicts the legitimacy of the requesting device (both source and destination), and the Advanced Encryption Standard (AES) safeguards the reliability of the transmitted data over a shared communication medium, preferably through a secret shared key, i.e., , and timestamp information. A device becomes trusted if it has passed both evaluation levels, i.e., HMM and message decryption, within a stipulated time interval. The proposed hybrid, along with existing state-of-the-art approaches, has been simulated in the realistic environment of the IoT to verify the security measures. These evaluations were carried out in the presence of intruders capable of launching various attacks simultaneously, such as man-in-the-middle, device impersonations, and masquerading attacks. Moreover, the proposed approach has been proven to be more effective than existing state-of-the-art approaches due to its exceptional performance in communication, processing, and storage overheads, i.e., 13%, 19%, and 16%, respectively. Finally, the proposed hybrid approach is pruned against well-known security attacks

关键词： Internet of Things information security authentication hidden Markov model multimedia

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Modeling and Comprehensive Review of Signaling Storms in 3GPP-Based Mobile Broadband Networks:Causes,Solutions,and Countermeasures

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computer Modeling in engineering & Sciences 2025年第1期142卷 123-153页

作者： Muhammad Qasim Khan Fazal Malik Fahad Alturise Noor Rahman Department of Computer Science Iqra National UniversityPeshawar25000Pakistan Department of Cybersecurity College of ComputerQassim UniversityBuraydah52555Saudi Arabia Department of Computer Science and Engineering Al-Fayha CollegeAl Jubayl31961Saudi Arabia

Control signaling is mandatory for the operation and management of all types of communication networks,including the Third Generation Partnership Project(3GPP)mobile broadband ***,they consume important and scarce network resources such as bandwidth and processing *** have been several reports of these control signaling turning into signaling storms halting network operations and causing the respective Telecom companies big financial *** paper draws its motivation from such real network disaster incidents attributed to signaling *** this paper,we present a thorough survey of the causes,of the signaling storm problems in 3GPP-based mobile broadband networks and discuss in detail their possible solutions and *** provide relevant analytical models to help quantify the effect of the potential causes and benefits of their corresponding *** important contribution of this paper is the comparison of the possible causes and solutions/countermeasures,concerning their effect on several important network aspects such as architecture,additional signaling,fidelity,etc.,in the form of a *** paper presents an update and an extension of our earlier conference *** our knowledge,no similar survey study exists on the subject.

关键词： Signaling storm problems control signaling load analytical modeling 3GPP networks smart devices diameter signaling mobile broadband data access data traffic mobility management signaling network architecture 5G mobile communication

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Enhanced Neutrosophic Set and Machine Learning Approach for Kidney Disease Prediction

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Neutrosophic Sets and Systems 2025年 80卷 465-477页

作者： Al-Doori, Humam M. Alkellezli, Tareef S. Abdelhafeez, Ahmed Eassaa, Mohamed Sawah, Mohamed S. El-Douh, Ahmed A. Cybersecurity Sciences Department College of Science Ashur University Baghdad Iraq Cybersecurity Engineering Department College of Engineering Ashur University Baghdad Iraq Computer Science Department Faculty of Information System and Computer Science October 6 University Giza12585 Egypt Applied Science Research Center Applied Science Private University Amman Jordan Department of Computer Science Faculty of Information Technology Ajloun National University P.O.43 Ajloun26810 Jordan Cybersecurity Technology Engineering Department College of Engineering Technology Ashur University Baghdad Iraq

Kidney disease (KD) is a gradually increasing global health concern. It is a chronic illness linked to higher rates of morbidity and mortality, a higher risk of cardiovascular disease and numerous other illnesses, and expensive medical expenses. The machine learning (ML) models are applied for KD prediction with higher accuracy and precision. The KD dataset has uncertainty and vague information, so, we used the neutrosophic set (NS) to deal with vague and uncertainty information in the KD dataset. The KD dataset is converted into the N-KD dataset with three membership functions: truth, indeterminacy, and falsity. Three ML models are used in this study such as logistic regression (LR), support vector machine (SVM), and k-nearest neighbor (KNN). These ML models are applied to the N-KD dataset. The results show the LR has higher accuracy and precision on the N-KD dataset than the original KD dataset. © 2025, University of New Mexico. All rights reserved.

关键词： Logistic regression

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Fake News Detection Using Machine Learning and Deep Learning Methods

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computers, Materials & Continua 2023年第11期77卷 2079-2096页

作者： Ammar Saeed Eesa Al Solami Department of Computer Science Comsats University IslamabadWah Cantt47010Pakistan Department of Cybersecurity College of Computer Science and EngineeringUniversity of JeddahJeddah21959Saudi Arabia

The evolution of the internet and its accessibility in the twenty-first century has resulted in a tremendous increase in the use of social media *** social media sources contribute to the propagation of fake news that has no real validity,but they accumulate over time and begin to appear in the feed of every consumer producing even more *** sustain the value of social media,such stories must be distinguished from the true *** a result,an automated system is required to save time and *** classification of fake news and misinformation from social media data corpora is the subject of this *** preprocessing and data improvement procedures are used to gather and preprocess two fake news *** text features are extracted using word embedding models Word2vec and Global Vectors for Word representation while textual features are extracted using n-gram approaches named Term Frequency-Inverse Document Frequency and Bag of Words from both datasets *** Encoder Representations from Transformers(BERT)is also employed to derive embedded representations from the input ***,three Machine Learning(ML)and two Deep Learning(DL)algorithms are utilized for fake news *** also carries out the classification of embedded outcomes generated by it in parallel with the ML and DL *** terms of overall performance,the DL-based Convolutional Neural Network stands out in the case of the first while BERT performs better in the case of the second dataset.

关键词： Machine learning deep learning fake news feature extraction

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A Color Image Encryption Scheme Based on Singular Values and Chaos

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computer Modeling in engineering & Sciences 2023年第10期137卷 965-999页

作者： Adnan Malik Muhammad Ali Faisal S.Alsubaei Nisar Ahmed Harish Kumar Electrical Engineering Department University of Engineering and TechnologyLahore54000Pakistan Department of Cybersecurity College of Computer Science and EngineeringUniversity of JeddahJeddah21959Saudi Arabia Computer Engineering Department University of Engineering and TechnologyLahore54000Pakistan Department of Computer Science College of Computer ScienceKing Khalid UniversityAbha61421Saudi Arabia

The security of digital images transmitted via the Internet or other public media is of the utmost *** encryption is a method of keeping an image secure while it travels across a non-secure communication medium where it could be intercepted by unauthorized *** study provides an approach to color image encryption that could find practical use in various *** proposed method,which combines four chaotic systems,employs singular value decomposition and a chaotic sequence,making it both secure and *** unified average change intensity,the number of pixels’change rate,information entropy analysis,correlation coefficient analysis,compression friendliness,and security against brute force,statistical analysis and differential attacks are all used to evaluate the algorithm’s *** a thorough investigation of the experimental data,it is concluded that the proposed image encryption approach is secure against a wide range of attacks and provides superior compression friendliness when compared to chaos-based alternatives.

关键词： Encryption image encryption chaos theory color image encryption singular value decomposition compression friendliness

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Blockchain-Enabled Medical Supply Chain Architecture for Rapid Response in Hajj 2nd

Blockchain-Enabled Medical Supply Chain Architecture for Rap...

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2nd International Conference on Cyber Intelligence and Information Retrieval, CIIR 2023

作者： Albalwy, Faisal Department of Cybersecurity College of Computer Science and Engineering Taibah University Madinah Saudi Arabia

ISBN: (纸本)9789819776023

The annual Hajj pilgrimage presents a unique logistical challenge for healthcare institutions, with the need to efficiently manage medical supplies for millions of participants. Traditional methods often fall short in transparency, efficiency, and rapid response. This paper proposes a novel Blockchain-Enabled Medical Supply Chain Architecture for Rapid Response in Hajj, a framework specifically tailored to address the special requirements of this large-scale event. Leveraging the decentralized nature of blockchain technology, this solution integrates smart contracts, Internet of Things (IoT) sensors, and mobile applications to automate supply chain operations and enable real-time monitoring. The proposed architecture emphasizes secure data management, transparent tracking, and automation, facilitating quick reactions to medical emergencies. Future work involves the implementation and comprehensive evaluation of this architecture, with a focus on real-world efficiency, responsiveness, and alignment with healthcare regulations. By modernizing the Hajj medical supply chain, this innovation aims to enhance pilgrim safety and set a benchmark for large-scale event healthcare logistics. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Supply chain management

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Adaptive Cyber Defense Technique Based on Multiagent Reinforcement Learning Strategies

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Intelligent Automation & Soft Computing 2023年第6期36卷 2757-2771页

作者： Adel Alshamrani Abdullah Alshahrani Department of Cybersecurity College of Computer Science and EngineeringUniversity of JeddahJeddahSaudi Arabia Department of Computer Science and Artificial Intelligence College of Computer Science and EngineeringUniversity of JeddahJeddahSaudi Arabia

The static nature of cyber defense systems gives attackers a sufficient amount of time to explore and further exploit the vulnerabilities of information technology *** this paper,we investigate a problem where multiagent sys-tems sensing and acting in an environment contribute to adaptive cyber *** present a learning strategy that enables multiple agents to learn optimal poli-cies using multiagent reinforcement learning(MARL).Our proposed approach is inspired by the multiarmed bandits(MAB)learning technique for multiple agents to cooperate in decision making or to work *** study a MAB approach in which defenders visit a system multiple times in an alternating fash-ion to maximize their rewards and protect their *** find that this game can be modeled from an individual player’s perspective as a restless MAB *** discover further results when the MAB takes the form of a pure birth process,such as a myopic optimal policy,as well as providing environments that offer the necessary incentives required for cooperation in multiplayer projects.

关键词： Multiarmed bandits reinforcement learning multiagents intrusion detection systems

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Machine Learning Enabled Novel Real-Time IoT Targeted DoS/DDoS Cyber Attack Detection System

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computers, Materials & Continua 2024年第9期80卷 3655-3683页

作者： Abdullah Alabdulatif Navod Neranjan Thilakarathne Mohamed Aashiq Department of Cybersecurity College of ComputerQassim UniversityBuraydah51452Saudi Arabia Department of ICT Faculty of TechnologyUniversity of ColomboColombo00700Sri Lanka Department of Computer Science and Engineering Faculty of EngineeringSouth Eastern University of Sri LankaOluvil32360Sri Lanka

The increasing prevalence of Internet of Things(IoT)devices has introduced a new phase of connectivity in recent years and,concurrently,has opened the floodgates for growing cyber *** the myriad of potential attacks,Denial of Service(DoS)attacks and Distributed Denial of Service(DDoS)attacks remain a dominant concern due to their capability to render services inoperable by overwhelming systems with an influx of *** IoT devices often lack the inherent security measures found in more mature computing platforms,the need for robust DoS/DDoS detection systems tailored to IoT is paramount for the sustainable development of every domain that IoT *** this study,we investigate the effectiveness of three machine learning(ML)algorithms:extreme gradient boosting(XGB),multilayer perceptron(MLP)and random forest(RF),for the detection of IoTtargeted DoS/DDoS attacks and three feature engineering methods that have not been used in the existing stateof-the-art,and then employed the best performing algorithm to design a prototype of a novel real-time system towards detection of such DoS/DDoS *** CICIoT2023 dataset was derived from the latest real-world IoT traffic,incorporates both benign and malicious network traffic patterns and after data preprocessing and feature engineering,the data was fed into our models for both training and validation,where findings suggest that while all threemodels exhibit commendable accuracy in detectingDoS/DDoS attacks,the use of particle swarmoptimization(PSO)for feature selection has made great improvements in the performance(accuracy,precsion recall and F1-score of 99.93%for XGB)of the ML models and their execution time(491.023 sceonds for XGB)compared to recursive feature elimination(RFE)and randomforest feature importance(RFI)*** proposed real-time system for DoS/DDoS attack detection entails the implementation of an platform capable of effectively processing and analyzing network traffic in *** inv

关键词： Machine learning Internet of Things(IoT),DoS,DDoS cybersecurity intrusion prevention network security,feature optimization,sustainability

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