Internet of Things and Cyber-Physical Systems最新文献

{"title":"Generative AI in cybersecurity: A comprehensive review of LLM applications and vulnerabilities","authors":"Mohamed Amine Ferrag ,&nbsp;Fatima Alwahedi ,&nbsp;Ammar Battah ,&nbsp;Bilel Cherif ,&nbsp;Abdechakour Mechri ,&nbsp;Norbert Tihanyi ,&nbsp;Tamas Bisztray ,&nbsp;Merouane Debbah","doi":"10.1016/j.iotcps.2025.01.001","DOIUrl":"10.1016/j.iotcps.2025.01.001","url":null,"abstract":"<div><div>This paper provides a comprehensive review of the future of cybersecurity through Generative AI and Large Language Models (LLMs). We explore LLM applications across various domains, including hardware design security, intrusion detection, software engineering, design verification, cyber threat intelligence, malware detection, and phishing detection. We present an overview of LLM evolution and its current state, focusing on advancements in models such as GPT-4, GPT-3.5, Mixtral-8x7B, BERT, Falcon2, and LLaMA. Our analysis extends to LLM vulnerabilities, such as prompt injection, insecure output handling, data poisoning, DDoS attacks, and adversarial instructions. We delve into mitigation strategies to protect these models, providing a comprehensive look at potential attack scenarios and prevention techniques. Furthermore, we evaluate the performance of 42 LLM models in cybersecurity knowledge and hardware security, highlighting their strengths and weaknesses. We thoroughly evaluate cybersecurity datasets for LLM training and testing, covering the lifecycle from data creation to usage and identifying gaps for future research. In addition, we review new strategies for leveraging LLMs, including techniques like Half-Quadratic Quantization (HQQ), Reinforcement Learning with Human Feedback (RLHF), Direct Preference Optimization (DPO), Quantized Low-Rank Adapters (QLoRA), and Retrieval-Augmented Generation (RAG). These insights aim to enhance real-time cybersecurity defenses and improve the sophistication of LLM applications in threat detection and response. Our paper provides a foundational understanding and strategic direction for integrating LLMs into future cybersecurity frameworks, emphasizing innovation and robust model deployment to safeguard against evolving cyber threats.</div></div>","PeriodicalId":100724,"journal":{"name":"Internet of Things and Cyber-Physical Systems","volume":"5 ","pages":"Pages 1-46"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143947102","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"LoRa for multihop communication in internet of underground things under fading environments","authors":"Irfana Ilyas Jameela Manzil ,&nbsp;Ruhul Amin Khalil ,&nbsp;Nasir Saeed","doi":"10.1016/j.iotcps.2025.05.001","DOIUrl":"10.1016/j.iotcps.2025.05.001","url":null,"abstract":"<div><div>This paper investigates the suitability of LoRa wireless technology for reliable underground-to-aboveground communication in the context of sustainable agricultural monitoring. We comprehensively analyze LoRa's performance in single-hop and multi-hop scenarios, considering complex environmental conditions and path loss. Mathematical expressions for the bit error rate (BER) are derived under both additive white Gaussian noise (AWGN) and Rayleigh fading, including multi-hop networks with decode-and-forward relays. Simulations under realistic Rayleigh fading scenarios validate our theoretical models. Our findings demonstrate that multi-hop LoRa networks significantly outperform single-hop systems in challenging underground environments, underscoring LoRa's potential for enhancing sustainability in various subterranean Internet of Underground Things (IoUT) agricultural applications.</div></div>","PeriodicalId":100724,"journal":{"name":"Internet of Things and Cyber-Physical Systems","volume":"5 ","pages":"Pages 87-94"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144205480","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"UMetaBE-DPPML: Urban metaverse & blockchain-enabled decentralised privacy-preserving machine learning verification and authentication with metaverse immersive devices","authors":"Kaya Kuru,&nbsp;Kaan Kuru","doi":"10.1016/j.iotcps.2025.02.001","DOIUrl":"10.1016/j.iotcps.2025.02.001","url":null,"abstract":"<div><div>It is anticipated that cybercrime activities will be widespread in the urban metaverse ecosystem due to its high economic value with new types of assets and its immersive nature with a variety of experiences. Ensuring reliable urban metaverse cyberspaces requires addressing two critical challenges, namely, cybersecurity and privacy protection. This study, by analysing potential cyberthreats in the urban metaverse cyberspaces, proposes a blockchain-based Decentralised Privacy-Preserving Machine Learning (DPPML) authentication and verification methodology, which uses the metaverse immersive devices and can be instrumented effectively against identity impersonation and theft of credentials, identity, or avatars. Blockchain technology and Federated Learning (FL) are merged in the developed DPPML approach not only to eliminate the requirement of a trusted third party for the verification of the authenticity of transactions and immersive actions, but also, to avoid Single Point of Failure (SPoF) and Generative Adversarial Networks (GAN) attacks by detecting malicious nodes. The developed methodology has been tested using Motion Capture Suits (MoCaps) in a co-simulation environment with the Proof-of-Work (PoW) consensus mechanism. The preliminary results suggest that the built techniques in the DPPML approach can prevent unreal transactions, impersonation, identity theft, and theft of credentials or avatars promptly before any transactions have been executed or immersive experiences have been shared with others. The proposed system will be tested with a larger number of nodes involving the Proof-of-Stake (PoS) consensus mechanism using several other metaverse immersive devices as a future job.</div></div>","PeriodicalId":100724,"journal":{"name":"Internet of Things and Cyber-Physical Systems","volume":"5 ","pages":"Pages 47-86"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144099446","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Advanced Machine Learning in Smart Grids: An overview","authors":"Hassan N. Noura ,&nbsp;Jean Paul A. Yaacoub ,&nbsp;Ola Salman ,&nbsp;Ali Chehab","doi":"10.1016/j.iotcps.2025.05.002","DOIUrl":"10.1016/j.iotcps.2025.05.002","url":null,"abstract":"<div><div>Adopting Advanced Machine Learning for Smart Grids (ML-SG) is a promising strategy that revolutionizes the energy industry to optimize energy usage, improve grid management, and foster sustainability. It also increases the efficiency, reliability, and sustainability of contemporary power systems. Furthermore, incorporating machine learning into smart grids has important practical ramifications and can help address some of the most pressing issues facing contemporary energy systems. By precisely forecasting consumption trends and facilitating dynamic pricing models that take into account current grid circumstances, Machine Learning (ML) can improve demand response tactics. Additionally, it is essential for preserving grid stability since it can promptly identify irregularities and react to system oscillations, preventing blackouts and equipment failures. Furthermore, through supply and demand balance, energy dispatch optimization, and solar and wind power forecasts, ML makes it easier to seamlessly integrate renewable energy sources. These characteristics facilitate the shift to a more robust, adaptable, and ecologically friendly energy infrastructure in addition to increasing operating efficiency. In this paper, we investigate the development of ML solutions that benefit from the enormous amounts of data generated by IoT devices in the smart grid. Furthermore, this study examines the benefits and drawbacks of the adoption of ML-SG and offers an outline of their use while highlighting the implications of integrating ML into smart grids. In addition, it explores and analyzes how ML algorithms can be used for load forecasting and enabling accurate and real-time decision making in smart grids. The objective of this work is to analyze smart grid operations at different levels, such as predicting energy demand, identifying abnormalities, and reducing cybersecurity threats by using sophisticated ML-based algorithms, especially discussing attacks and countermeasures against these ML models. This work concludes with suggestions and recommendations that highlight the importance of improving the security and accuracy of ML-SG, while shedding some light on future directions. In the future, this work aims to contribute to the development of efficient ML solutions for energy infrastructure to become more effective and sustainable, by discussing data science and ML issues related to smart grids.</div></div>","PeriodicalId":100724,"journal":{"name":"Internet of Things and Cyber-Physical Systems","volume":"5 ","pages":"Pages 95-142"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144205481","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Non-work conserving dynamic scheduling of moldable gang tasks on multicore systems","authors":"Tomoki Shimizu, Hiroki Nishikawa, Xiangbo Kong, Hiroyuki Tomiyama","doi":"10.1016/j.iotcps.2024.03.001","DOIUrl":"https://doi.org/10.1016/j.iotcps.2024.03.001","url":null,"abstract":"","PeriodicalId":100724,"journal":{"name":"Internet of Things and Cyber-Physical Systems","volume":"5 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140270324","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Constructing immersive toy trial experience in mobile augmented reality","authors":"Lingxin Yu, Jiacheng Zhang, Xinyue Wang, Siru Chen, Xuehao Qin, Zhifei Ding, Jiahao Han","doi":"10.1016/j.iotcps.2024.02.001","DOIUrl":"https://doi.org/10.1016/j.iotcps.2024.02.001","url":null,"abstract":"","PeriodicalId":100724,"journal":{"name":"Internet of Things and Cyber-Physical Systems","volume":"10 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139891880","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multi-objective optimization algorithms for intrusion detection in IoT networks: A systematic review","authors":"Shubhkirti Sharma, Vijay Kumar, K. Dutta","doi":"10.1016/j.iotcps.2024.01.003","DOIUrl":"https://doi.org/10.1016/j.iotcps.2024.01.003","url":null,"abstract":"","PeriodicalId":100724,"journal":{"name":"Internet of Things and Cyber-Physical Systems","volume":"27 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139871865","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"MalAware: A tabletop exercise for malware security awareness education and incident response training","authors":"Giddeon Angafor ,&nbsp;Iryna Yevseyeva ,&nbsp;Leandros Maglaras","doi":"10.1016/j.iotcps.2024.02.003","DOIUrl":"https://doi.org/10.1016/j.iotcps.2024.02.003","url":null,"abstract":"<div><p>Advancements in technology, including the Internet of Things (IoT) revolution, have enabled individuals and businesses to use systems and devices that connect, exchange data, and provide real-time information from far and near. Despite that, this interconnectivity and data sharing between systems and devices over the internet poses security and privacy risks as threat actors can intercept, steal, and use owners’ data for nefarious purposes. This paper discusses ’MalAware’, a ‘Malware Awareness Education’ and incident response (IR) scenario-based tabletop exercise and card game for malware threat mitigation training. It introduces the importance of incident management, highlights the dangers posed by malware for connected systems, and outlines the role of tabletop games and exercises in helping businesses mature their malware incident response capabilities. The study discusses the design of MalAware and summarises the results of 2 pilots undertaken to assess the concept, maintaining that the results highlighted the value of ‘MalAware’ as an essential tool to help students and staff master how to mitigate security threats caused by malware. It argues that MalAware can assist businesses in their IR preparedness endeavors, enabling incident management teams to review plans and processes to ensure they are fit for purpose. It enables staff to leverage scenario-based and simulated security breach examples, including role-play, to establish appropriate malware defences. MalAware’s practical hands-on exercises can assist trainees in gaining essential malware and other threat mitigation skills, helping to protect the security and privacy of IoTs.</p></div>","PeriodicalId":100724,"journal":{"name":"Internet of Things and Cyber-Physical Systems","volume":"4 ","pages":"Pages 280-292"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2667345224000063/pdfft?md5=61feca14037fa00f21581df14b5c4571&pid=1-s2.0-S2667345224000063-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140180017","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"IoT-enhanced smart road infrastructure systems for comprehensive real-time monitoring","authors":"Zhoujing Ye ,&nbsp;Ya Wei ,&nbsp;Songli Yang ,&nbsp;Pengpeng Li ,&nbsp;Fei Yang ,&nbsp;Biyu Yang ,&nbsp;Linbing Wang","doi":"10.1016/j.iotcps.2024.01.002","DOIUrl":"https://doi.org/10.1016/j.iotcps.2024.01.002","url":null,"abstract":"<div><p>With the rapid advancement of Internet of Things (IoT) technology, its applications in road infrastructure have garnered attention. However, challenges persist when applying IoT to road infrastructure monitoring, including insufficient durability of front-end sensors, pavement damage due to sensor embedding, and the redundancy of a vast amount of real-time data, hindering the long-term real-time monitoring of pavements. To address these challenges, this study developed a self-powered distributed intelligent pavement monitoring system based on IoT, encompassing a sensor network, cloud platform, communication network, and power supply system. Considering the specific characteristics of slipform paving for cement concrete pavements, an integrated paving process was proposed, merging embedded sensors with pavement material structures. Through on-site engineering monitoring, the system actively collects and analyzes various data types such as system energy consumption, temperature and humidity, environmental noise, wind speed and direction, and pavement structural vibrations, providing data support for pavement design, maintenance, and vehicle-road synergy applications. Future efforts will continue to promote the application of IoT technology in digital road maintenance, traffic safety, and optimized pavement material structure design.</p></div>","PeriodicalId":100724,"journal":{"name":"Internet of Things and Cyber-Physical Systems","volume":"4 ","pages":"Pages 235-249"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2667345224000026/pdfft?md5=e2593131eb914f50ce726004b9037d6b&pid=1-s2.0-S2667345224000026-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139718721","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Ransomware on cyber-physical systems: Taxonomies, case studies, security gaps, and open challenges","authors":"Mourad Benmalek","doi":"10.1016/j.iotcps.2023.12.001","DOIUrl":"10.1016/j.iotcps.2023.12.001","url":null,"abstract":"<div><p>Ransomware attacks have emerged as one of the most significant cyberthreats faced by organizations worldwide. In recent years, ransomware has also started to target critical infrastructure and Cyber-Physical Systems (CPS) such as industrial control systems, smart grids, and healthcare networks. The unique attack surface and safety-critical nature of CPS introduce new challenges in defending against ransomware. This paper provides a comprehensive overview of ransomware threats to CPS. We propose a dual taxonomy to classify ransomware attacks on CPS based on infection vectors, targets, objectives, and technical attributes. Through an analysis of 10 real-world incidents, we highlight attack patterns, vulnerabilities, and impacts of ransomware campaigns against critical systems and facilities. Based on the insights gained, we identify open research problems and future directions to improve ransomware resilience in CPS environments.</p></div>","PeriodicalId":100724,"journal":{"name":"Internet of Things and Cyber-Physical Systems","volume":"4 ","pages":"Pages 186-202"},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2667345223000561/pdfft?md5=4e1f20e6c28b32ae59f1f757ef9b4c6b&pid=1-s2.0-S2667345223000561-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139394158","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}