Computer Science Review最新文献

{"title":"Advancements in image encryption: A comprehensive review of design principles and performance metrics","authors":"Biswarup Yogi ,&nbsp;Ajoy Kumar Khan","doi":"10.1016/j.cosrev.2025.100759","DOIUrl":"10.1016/j.cosrev.2025.100759","url":null,"abstract":"<div><div>With the rise of digital image sharing in fields like healthcare, defence, and multimedia, strong image encryption is needed to protect sensitive information. This study provides a detailed overview of the analysis of image encryption algorithms, focusing on their design principles and performance metrics. This study covers different encryption methods, from traditional symmetric and asymmetric to modern chaos-based and quantum encryption. The design principles include substitution-permutation, diffusion, and key generation strategies. They are carefully evaluated to understand their actual input towards achieving high security. This study discusses key performance metrics such as encryption speed, sensitivity to key changes, statistical and differential attacks, computational complexity, and efficiency required to analyze the feasibility of various algorithms in practical applications. Explores issues such as the balance between security and resource limits, scalability, and adaptability to new threats. The analysis highlights the strengths and weaknesses of existing techniques, providing useful insights for developing next-generation encryption methods for specific applications. The combination of theoretical concepts along with performance evaluations presented in this work thus provides a significant and valuable reference source for research and practice aimed at designing effective yet secure image encryption algorithms in this rapidly growing world of technology.</div></div>","PeriodicalId":48633,"journal":{"name":"Computer Science Review","volume":"57 ","pages":"Article 100759"},"PeriodicalIF":13.3,"publicationDate":"2025-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143815567","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 on quantum-safe blockchain security infrastructure","authors":"Arya Wicaksana","doi":"10.1016/j.cosrev.2025.100752","DOIUrl":"10.1016/j.cosrev.2025.100752","url":null,"abstract":"<div><div>Security infrastructure is vital in blockchain for its decentralized and distributed characteristics. Blockchain security infrastructure comprises several components: cryptographic algorithms, consensus protocols, key and identity management, network architecture, and smart contract deployment and execution. These components are vulnerable to the advancement of quantum computing and the realization of more powerful quantum computers. Classical security countermeasures used across the entire blockchain security infrastructure are exposed to quantum computing attacks. The exploitation is catastrophic to the sustainability of blockchain research and applications. This paper outlines the blockchain security infrastructure and quantum-safe solutions. The promising quantum-resistant cryptographic algorithms released by the National Institute of Standards and Technology (NIST) are evaluated for their relevance and use in blockchain security infrastructure. This paper also discusses the practical implementation and adoption of quantum-safe solutions for blockchain security infrastructure, including recent developments on the quantum-safe blockchain. The holy grail in adopting quantum-safe solutions for blockchain security infrastructure solutions is without sacrificing scalability and decentralization.</div></div>","PeriodicalId":48633,"journal":{"name":"Computer Science Review","volume":"57 ","pages":"Article 100752"},"PeriodicalIF":13.3,"publicationDate":"2025-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143807674","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 large language models to edge intelligence: A survey of edge efficient LLMs and techniques","authors":"Rui Wang,&nbsp;Zhiyong Gao,&nbsp;Liuyang Zhang,&nbsp;Shuaibing Yue,&nbsp;Ziyi Gao","doi":"10.1016/j.cosrev.2025.100755","DOIUrl":"10.1016/j.cosrev.2025.100755","url":null,"abstract":"<div><div>Large language models (LLMs) have showcased exceptional capabilities across various natural language processing (NLP) tasks in recent years, such as machine translation, text summarization, and question answering. Despite their impressive performance, the deployment of these models on edge devices, such as mobile phones, IoT devices, and edge computing nodes, is significantly hindered by their substantial computational and memory requirements. This survey provides a comprehensive overview of the state-of-the-art techniques and strategies for enabling efficient inference of LLMs on edge devices. We explore approaches including the development of small language models (SLMs), model compression techniques, inference optimization strategies, and dedicated frameworks for edge deployment. Our goal is to highlight the advancements and ongoing challenges in this field, offering valuable insights for researchers and practitioners striving to bring the power of LLMs to edge environments.</div></div>","PeriodicalId":48633,"journal":{"name":"Computer Science Review","volume":"57 ","pages":"Article 100755"},"PeriodicalIF":13.3,"publicationDate":"2025-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143799152","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":"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}