Computer Science Review最新文献

{"title":"The ML-based sensor data deception targeting cyber–physical systems: A review","authors":"Nektaria Kaloudi ,&nbsp;Jingyue Li","doi":"10.1016/j.cosrev.2025.100753","DOIUrl":"10.1016/j.cosrev.2025.100753","url":null,"abstract":"<div><div>The security of cyber–physical systems is crucial due to their critical applications. The increasing success of machine learning (ML) has raised growing concerns about its impact on the cybersecurity of cyber–physical systems. Although several studies have assessed the cybersecurity of cyber–physical systems, there remains a lack of systematic understanding of how ML techniques can contribute to the use of deception on these systems. In this study, we aim to systematize findings on the use of ML for sensor data deception in both attack and defense scenarios. We analyzed 13 offensive and 3 defensive approaches that leverage ML for sensor data deception targeting cyber–physical systems. We summarized the offensive and defensive sensor data deception implementations with impact on cyber–physical systems at the system level, and the mechanisms to defend offensive deception. Additionally, we provide key insights and outline challenges intended to guide future research on defending against ML-based cyber deception in cyber–physical systems.</div></div>","PeriodicalId":48633,"journal":{"name":"Computer Science Review","volume":"57 ","pages":"Article 100753"},"PeriodicalIF":13.3,"publicationDate":"2025-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143768232","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Empowering multimodal analysis with visualization: A survey","authors":"Jiachen Wang ,&nbsp;Zikun Deng ,&nbsp;Dazhen Deng ,&nbsp;Xingbo Wang ,&nbsp;Rui Sheng ,&nbsp;Yi Cai ,&nbsp;Huamin Qu","doi":"10.1016/j.cosrev.2025.100748","DOIUrl":"10.1016/j.cosrev.2025.100748","url":null,"abstract":"<div><div>Multimodal data, which encompasses text, audio, image, and other modalities, is a popular research target in the field of visualization research. Existing visualization techniques for multimodal data are scattered and categorized by application domains, such as multimodal model analysis or online education. It lacks a comprehensive review from the perspective of data that summarizes the methodologies, research gaps, and future trends for researchers and practitioners. In this study, we delve into existing visualization research, identifying their data modalities, applications, strengths, and limitations. Furthermore, we shed light on the potential challenges and opportunities for further research in this domain to advance intelligent visualizations for multimodal data.</div></div>","PeriodicalId":48633,"journal":{"name":"Computer Science Review","volume":"57 ","pages":"Article 100748"},"PeriodicalIF":13.3,"publicationDate":"2025-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143760346","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Artificial intelligence in COVID-19 research: A comprehensive survey of innovations, challenges, and future directions","authors":"Richard Annan,&nbsp;Letu Qingge","doi":"10.1016/j.cosrev.2025.100751","DOIUrl":"10.1016/j.cosrev.2025.100751","url":null,"abstract":"<div><div>The COVID-19 pandemic has accelerated the use of AI and ML in healthcare, improving diagnosis, treatment, and resource allocation. This survey examines the AI applications in disease detection, differential diagnosis, and post-COVID complication analysis. Our findings show that 53% of the reviewed studies focus on COVID-19 detection, while only 14% address post-COVID complications. This reveals a gap in long-term patient monitoring. Convolutional Neural Networks (CNNs) are the most frequently used models, appearing in 23% of reviewed studies as standalone architectures and even more often in hybrid models. Meanwhile, transformers and multimodal models remain underutilized. Each appears in only 4% of the studies, limiting the integration of diverse data sources, such as imaging, audio, and lab results. Federated learning, a privacy-preserving AI approach, appears in 9% of studies. However, it is still less common than centralized models. This restricts secure and collaborative AI development. Despite these progress, challenges such as data bias, limited model generalization, and ethical concerns persist. Advanced methods including transformer models and knowledge distillation offer potential solutions for improving computational efficiency. To strengthen AI-driven healthcare, this survey highlights three key needs: (1) broader adoption of multimodal AI, (2) development of computationally efficient and interpretable AI models, and (3) increased use of federated learning to support privacy-preserving AI training. By synthesizing insights from various studies, this paper provides a comprehensive evaluation of AI innovations in COVID-19 research and outlines key directions for future advancements in ethical and scalable AI-driven healthcare.</div></div>","PeriodicalId":48633,"journal":{"name":"Computer Science Review","volume":"57 ","pages":"Article 100751"},"PeriodicalIF":13.3,"publicationDate":"2025-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143760347","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Securing Tomorrow of Next-Generation Technologies with Biometrics, State-of-The-Art Techniques, Open Challenges, and Future Research Directions","authors":"Muhammad Adil ,&nbsp;Ahmed Farouk ,&nbsp;Aitizaz Ali ,&nbsp;Houbing Song ,&nbsp;Zhanpeng Jin","doi":"10.1016/j.cosrev.2025.100750","DOIUrl":"10.1016/j.cosrev.2025.100750","url":null,"abstract":"&lt;div&gt;&lt;div&gt;In the recent past, the increasing use of online applications, from simple food orders to complex banking services, has highlighted the need for strong and reliable network security measures. Traditional password-based authentication schemes have proven vulnerable to various cyber threats, including password guessing, keylogging, phishing, and credential theft, etc. To address these challenges, the research community has turned its attention to biometric technologies as a promising solution for Next-Generation Technologies security. These biometrics technologies use the physiological and behavioral characteristics of humans to verify or confirm their identity in many NGTs such as smartphones, computers, banking systems, and border control systems, etc. However, these NGTs faces several key challenges in the context of biometric systems. This is because the biometrics features change a lot with age, or once someone passes away, compared to the collected data when the person was alive. Therefore, these systems face multidirectional challenges. First, the attackers can use different techniques to compromise the security of an application. Secondly, the disparities inherent in biometric technologies pose significant challenges to their effective implementation because they rely on specific human features such as irises, fingerprints, and faces, etc. Considering that, we conducted a comprehensive survey over the past decade. This survey aimed to identify both the strengths and weaknesses of existing biometric techniques used in NGTs, with the goal of setting the path for future research that can enhance the security of these technologies. Furthermore, it is crucial to address the question of why this paper is necessary, given the presence of several survey papers on the same topic. To answer this question, our work offers several unique contributions based on the undermentioned factors: (i:) Firstly, it familiarizes the reader with the taxonomy of biometrics, followed by enabling technologies and use cases in the context of NGTs, which are ignored in most published papers. (ii:) Secondly, it meticulously examines the reliability of mostly used biometric authentication techniques by highlighting their strong and weak aspects to underscore the research gaps. (iii:) Thirdly, our paper thoroughly explores the requirements of biometric technologies in the context of NGTs to support the literature arguments regarding their inefficiency in meeting the demands of emerging technologies. Moreover, this work provides insights into both the achievements made thus far and the areas that still require attention to enhance the reliability of biometric systems. (iv:) Fourthly, it highlights the open challenges that current biometric methods struggle to overcome in futuristic technologies. (v:) Lastly, we propose research directions with the aim of addressing the highlighted open challenges and maintaining the trust of all stakeholders in biometric technolog","PeriodicalId":48633,"journal":{"name":"Computer Science Review","volume":"57 ","pages":"Article 100750"},"PeriodicalIF":13.3,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143738598","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Traffic load balancing in data center networks: A comprehensive survey","authors":"Guisheng Liu ,&nbsp;Yong Liu ,&nbsp;Qian Meng ,&nbsp;Ben Wang ,&nbsp;Kefei Chen ,&nbsp;Zhonghua Shen","doi":"10.1016/j.cosrev.2025.100749","DOIUrl":"10.1016/j.cosrev.2025.100749","url":null,"abstract":"<div><div>The rapid growth in scale and complexity of data centers has established effective load balancing as a critical requirement for optimizing network performance, resource utilization, and service quality. This survey provides a comprehensive examination of load balancing strategies in data center networks, addressing challenges, established techniques, and emerging trends. The study begins with fundamental concepts of data center networks, encompassing network topologies, load balancing definitions, granularity considerations, and inherent challenges. A systematic analysis of load balancing schemes follows, categorized into centralized and distributed approaches. Within centralized schemes, the analysis encompasses both machine learning-based approaches and traditional methodologies, while distributed schemes are examined through single-granularity and mixed-granularity implementations. The survey extends beyond previous reviews by offering comparative analyses of load balancing strategies, a systematic evaluation of performance metrics, and an assessment of their applicability across diverse data center scenarios. The discussion further explores future development trends driven by technological innovations across multiple dimensions, including hardware, software, architecture, and algorithms. This comprehensive technical review serves as a foundational resource for researchers and practitioners, facilitating advancements in efficient, scalable, and user-centric load balancing technologies for data center networks.</div></div>","PeriodicalId":48633,"journal":{"name":"Computer Science Review","volume":"57 ","pages":"Article 100749"},"PeriodicalIF":13.3,"publicationDate":"2025-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143725808","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Review of Distributed Quantum Computing: From single QPU to High Performance Quantum Computing","authors":"David Barral ,&nbsp;F. Javier Cardama ,&nbsp;Guillermo Díaz-Camacho ,&nbsp;Daniel Faílde ,&nbsp;Iago F. Llovo ,&nbsp;Mariamo Mussa-Juane ,&nbsp;Jorge Vázquez-Pérez ,&nbsp;Juan Villasuso ,&nbsp;César Piñeiro ,&nbsp;Natalia Costas ,&nbsp;Juan C. Pichel ,&nbsp;Tomás F. Pena ,&nbsp;Andrés Gómez","doi":"10.1016/j.cosrev.2025.100747","DOIUrl":"10.1016/j.cosrev.2025.100747","url":null,"abstract":"<div><div>The emerging field of quantum computing has shown it might change how we process information by using the unique principles of quantum mechanics. As researchers continue to push the boundaries of quantum technologies to unprecedented levels, distributed quantum computing raises as an obvious path to explore with the aim of boosting the computational power of current quantum systems. This paper presents a comprehensive survey of the current state of the art in the distributed quantum computing field, exploring its foundational principles, landscape of achievements, challenges, and promising directions for further research. From quantum communication protocols to entanglement-based distributed algorithms, each aspect contributes to the mosaic of distributed quantum computing, making it an attractive approach to address the limitations of classical computing. Our objective is to offer a comprehensive review that serves both experts in the field and researchers or enthusiasts in quantum computing looking for a starting point to explore the area of distributed quantum computing.</div></div>","PeriodicalId":48633,"journal":{"name":"Computer Science Review","volume":"57 ","pages":"Article 100747"},"PeriodicalIF":13.3,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143679142","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Future of cyberspace: A critical review of standard security protocols in the post-quantum era","authors":"Milad Taleby Ahvanooey ,&nbsp;Wojciech Mazurczyk ,&nbsp;Jun Zhao ,&nbsp;Luca Caviglione ,&nbsp;Kim-Kwang Raymond Choo ,&nbsp;Max Kilger ,&nbsp;Mauro Conti ,&nbsp;Rafael Misoczki","doi":"10.1016/j.cosrev.2025.100738","DOIUrl":"10.1016/j.cosrev.2025.100738","url":null,"abstract":"<div><div>Over the past three decades, standardizing organizations (e.g., the National Institute of Standards and Technology and Internet Engineering Task Force) have investigated the efficiency of cryptographic algorithms and provided (technical) guidelines for practitioners. For example, the (Datagram) Transport Layer Security “(D)TLS” 1.2/1.3 was designed to help industries implement and integrate such methods through underpinning infrastructures of Internet of Everything (IoE) environments with efficiency and efficacy in mind. The main goal underpinning such protocols is to protect the Internet connections between IoE machines from malicious activities such as unauthorized eavesdropping, monitoring, and tampering with messages. In theory, these protocols are supposed to be secure. Still, most existing implementations partially follow the standard features of (D)TLS 1.2/3, leaving them vulnerable to risks such as side-channel and network attacks. In this paper, we critically review the standard protocols deployed for the security management of data and connected machines, and also examine the recently discovered vulnerabilities that lead to successful zero-day attacks in IoE environments. Then, we discuss various potential countermeasures in the form of organizational policy enforcement strategies and mitigation approaches that can be used by cybersecurity practitioners, decision- and policy-makers. Finally, we identify both proactive and reactive solutions for further consideration and study, as well as propose alternative mechanisms and e-governance policies for standardizing organizations and engineers in future solution designs.</div></div>","PeriodicalId":48633,"journal":{"name":"Computer Science Review","volume":"57 ","pages":"Article 100738"},"PeriodicalIF":13.3,"publicationDate":"2025-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143642212","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Characterising harmful API uses and repair techniques: Insights from a systematic review","authors":"Lina Ochoa ,&nbsp;Muhammad Hammad ,&nbsp;Görkem Giray ,&nbsp;Önder Babur ,&nbsp;Kwabena Bennin","doi":"10.1016/j.cosrev.2025.100732","DOIUrl":"10.1016/j.cosrev.2025.100732","url":null,"abstract":"<div><div>API use has become prevalent in current times and its purposeful management is of foremost importance to avoid undesired effects on client code. A plethora of studies focusing on the isolated investigation of different types of harmful API uses (e.g., API misuse and security vulnerabilities) have been conducted before. However, a comprehensive overview of possible harmful API uses is required to help both library and client developers on the management of implemented and used APIs. Moreover, repairing such harmful uses remains a significant challenge in software development, yet recent studies indicate its widespread prevalence despite efforts to develop automatic repair techniques. This paper presents the first systematic review of 35 peer-reviewed studies on harmful API uses and their corresponding (semi-)automatic repair techniques. We categorise common types of harmful API uses in terms of the origin and root cause of events triggering the undesired use and the type of harm incurred on the client. We further analyse their repair approaches, assessing their strengths and weaknesses. Additionally, we investigate the evaluation processes and metrics employed in the outlined repair techniques. Our study contributes to advancing the state-of-the-art in harmful API repair research, by addressing open research problems and paving the way to improve and develop new repair techniques and tool capabilities.</div></div>","PeriodicalId":48633,"journal":{"name":"Computer Science Review","volume":"57 ","pages":"Article 100732"},"PeriodicalIF":13.3,"publicationDate":"2025-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143578907","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Intelligent visual analytics for food safety: A comprehensive review","authors":"Qinghui Zhang ,&nbsp;Yi Chen ,&nbsp;Xue Liang","doi":"10.1016/j.cosrev.2025.100739","DOIUrl":"10.1016/j.cosrev.2025.100739","url":null,"abstract":"<div><div>The emergence of food safety big data poses a huge challenge to data analysis and the application of technology. Intelligent visual analytics combines the advantages of artificial intelligence and visual analytics methods to process complex information more efficiently and accurately, providing technical support for intelligent food safety supervision. In this paper, we review the development and application of intelligent visual analytics for food safety over the past decade. First, we explore food safety data sources, data characteristics, and analytical tasks. Second, artificial intelligence methods and visualization techniques in food safety are presented respectively. Third, in-depth insights and applications of intelligent visual analytics methods from the perspective of food safety data characterization are provided, and typical cases are given. Finally, opportunities and challenges in intelligent visual analytics for food safety are proposed, including emerging technologies such as few-shot learning, automatic visualization generation, and large language models. The review aims to encourage researchers to propose more practical intelligent visual analytics solutions.</div></div>","PeriodicalId":48633,"journal":{"name":"Computer Science Review","volume":"57 ","pages":"Article 100739"},"PeriodicalIF":13.3,"publicationDate":"2025-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143550020","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A survey of Beluga whale optimization and its variants: Statistical analysis, advances, and structural reviewing","authors":"Sang-Woong Lee ,&nbsp;Amir Haider ,&nbsp;Amir Masoud Rahmani ,&nbsp;Bahman Arasteh ,&nbsp;Farhad Soleimanian Gharehchopogh ,&nbsp;Shengda Tang ,&nbsp;Zhe Liu ,&nbsp;Khursheed Aurangzeb ,&nbsp;Mehdi Hosseinzadeh","doi":"10.1016/j.cosrev.2025.100740","DOIUrl":"10.1016/j.cosrev.2025.100740","url":null,"abstract":"<div><div>Optimization, as a fundamental pillar in engineering, computer science, economics, and many other fields, plays a decisive role in improving the performance of systems and achieving desired goals. Optimization problems involve many variables, various constraints, and nonlinear objective functions. Among the challenges of complex optimization problems is the extensive search space with local optima that prevents reaching the global optimal solution. Therefore, intelligent and collective methods are needed to solve problems, such as searching for large problem spaces and identifying near-optimal solutions. Metaheuristic algorithms are a successful method for solving complex optimization problems. Usually, metaheuristic algorithms, inspired by natural and social phenomena, try to find optimal or near-optimal solutions by using random searches and intelligent explorations in the problem space. Beluga Whale Optimization (BWO) is one of the metaheuristic algorithms for solving optimization problems that has attracted the attention of researchers in recent years. The BWO algorithm tries to optimize the search space and achieve optimal solutions by simulating the collective behavior of whales. A study and review of published articles on the BWO algorithm show that this algorithm has been used in various fields, including optimization of mathematical functions, engineering problems, and even problems related to artificial intelligence. In this article, the BWO algorithm is classified according to four categories (combination, improvement, variants, and optimization). An analysis of 151 papers shows that the BWO algorithm has the highest percentage (49%) in the improvement field. The combination, variants, and optimization fields comprise 12%, 7%, and 32%, respectively.</div></div>","PeriodicalId":48633,"journal":{"name":"Computer Science Review","volume":"57 ","pages":"Article 100740"},"PeriodicalIF":13.3,"publicationDate":"2025-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143529171","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}