Computer Science Review最新文献_第8页

A comprehensive review of vulnerabilities and AI-enabled defense against DDoS attacks for securing cloud services 全面评述漏洞和人工智能防御 DDoS 攻击以确保云服务安全

IF 13.3 1区计算机科学

Computer Science Review Pub Date : 2024-08-01 DOI: 10.1016/j.cosrev.2024.100661

Surendra Kumar , Mridula Dwivedi , Mohit Kumar , Sukhpal Singh Gill

{"title":"A comprehensive review of vulnerabilities and AI-enabled defense against DDoS attacks for securing cloud services","authors":"Surendra Kumar , Mridula Dwivedi , Mohit Kumar , Sukhpal Singh Gill","doi":"10.1016/j.cosrev.2024.100661","DOIUrl":"10.1016/j.cosrev.2024.100661","url":null,"abstract":"<div><p>The advent of cloud computing has made a global impact by providing on-demand services, elasticity, scalability, and flexibility, hence delivering cost-effective resources to end users in pay-as-you-go manner. However, securing cloud services against vulnerabilities, threats, and modern attacks remains a major concern. Application layer attacks are particularly problematic because they can cause significant damage and are often difficult to detect, as malicious traffic can be indistinguishable from normal traffic flows. Moreover, preventing Distributed Denial of Service (DDoS) attacks is challenging due to its high impact on physical computer resources and network bandwidth. This study examines new variations of DDoS attacks within the broader context of cyber threats and utilizes Artificial Intelligence (AI)-based approaches to detect and prevent such modern attacks. The conducted investigation determines that the current detection methods predominantly employ collectively, hybrid, and single Machine Learning (ML)/Deep Learning (DL) techniques. Further, the analysis of diverse DDoS attacks and their related defensive strategies is vital in safeguarding cloud infrastructure against the detrimental consequences of DDoS attacks. This article offers a comprehensive classification of the various types of cloud DDoS attacks, along with an in-depth analysis of the characterization, detection, prevention, and mitigation strategies employed. The article presents, an in-depth analysis of crucial performance measures used to assess different defence systems and their effectiveness in a cloud computing environment. This article aims to encourage cloud security researchers to devise efficient defence strategies against diverse DDoS attacks. The survey identifies and elucidates the research gaps and obstacles, while also providing an overview of potential future research areas.</p></div>","PeriodicalId":48633,"journal":{"name":"Computer Science Review","volume":"53 ","pages":"Article 100661"},"PeriodicalIF":13.3,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141910636","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}

引用次数: 0

A survey on the parameterized complexity of reconfiguration problems 关于重新配置问题参数化复杂性的调查

IF 13.3 1区计算机科学

Computer Science Review Pub Date : 2024-08-01 DOI: 10.1016/j.cosrev.2024.100663

Nicolas Bousquet , Amer E. Mouawad , Naomi Nishimura , Sebastian Siebertz

{"title":"A survey on the parameterized complexity of reconfiguration problems","authors":"Nicolas Bousquet , Amer E. Mouawad , Naomi Nishimura , Sebastian Siebertz","doi":"10.1016/j.cosrev.2024.100663","DOIUrl":"10.1016/j.cosrev.2024.100663","url":null,"abstract":"<div><p>A graph vertex-subset problem defines which subsets of the vertices of an input graph are feasible solutions. We view a feasible solution as a set of tokens placed on the vertices of the graph. A reconfiguration variant of a vertex-subset problem asks, given two feasible solutions of size <span><math><mi>k</mi></math></span>, whether it is possible to transform one into the other by a sequence of token slides (along edges of the graph) or token jumps (between arbitrary vertices of the graph) such that each intermediate set remains a feasible solution of size <span><math><mi>k</mi></math></span>. Many algorithmic questions present themselves in the form of reconfiguration problems: Given the description of an initial system state and the description of a target state, is it possible to transform the system from its initial state into the target one while preserving certain properties of the system in the process? Such questions have received a substantial amount of attention under the so-called combinatorial reconfiguration framework. We consider reconfiguration variants of three fundamental underlying graph vertex-subset problems, namely <span>Independent Set</span>, <span>Dominating Set</span>, and <span>Connected Dominating Set</span>. We survey both older and more recent work on the parameterized complexity of all three problems when parameterized by the number of tokens <span><math><mi>k</mi></math></span>. The emphasis will be on positive results and the most common techniques for the design of fixed-parameter tractable algorithms.</p></div>","PeriodicalId":48633,"journal":{"name":"Computer Science Review","volume":"53 ","pages":"Article 100663"},"PeriodicalIF":13.3,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141910596","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}

引用次数: 0

The interaction design of 3D virtual humans: A survey 三维虚拟人的交互设计：一项调查

IF 13.3 1区计算机科学

Computer Science Review Pub Date : 2024-07-17 DOI: 10.1016/j.cosrev.2024.100653

Xueyang Wang, Nan Cao, Qing Chen, Shixiong Cao

引用次数: 0

Mobile robot localization: Current challenges and future prospective 移动机器人定位：当前挑战与未来展望

IF 13.3 1区计算机科学

Computer Science Review Pub Date : 2024-07-05 DOI: 10.1016/j.cosrev.2024.100651

Inam Ullah , Deepak Adhikari , Habib Khan , M. Shahid Anwar , Shabir Ahmad , Xiaoshan Bai

{"title":"Mobile robot localization: Current challenges and future prospective","authors":"Inam Ullah , Deepak Adhikari , Habib Khan , M. Shahid Anwar , Shabir Ahmad , Xiaoshan Bai","doi":"10.1016/j.cosrev.2024.100651","DOIUrl":"https://doi.org/10.1016/j.cosrev.2024.100651","url":null,"abstract":"<div><p>Mobile Robots (MRs) and their applications are undergoing massive development, requiring a diversity of autonomous or self-directed robots to fulfill numerous objectives and responsibilities. Integrating MRs with the Intelligent Internet of Things (IIoT) not only makes robots innovative, trackable, and powerful but also generates numerous threats and challenges in multiple applications. The IIoT combines intelligent techniques, including artificial intelligence and machine learning, with the Internet of Things (IoT). The location information (localization) of the MRs triggers innumerable domains. To fully accomplish the potential of localization, Mobile Robot Localization (MRL) algorithms need to be integrated with complementary technologies, such as MR classification, indoor localization mapping solutions, three-dimensional localization, etc. Thus, this paper endeavors to comprehensively review different methodologies and technologies for MRL, emphasizing intelligent architecture, indoor and outdoor methodologies, concepts, and security-related issues. Additionally, we highlight the diverse MRL applications where information about localization is challenging and present the various computing platforms. Finally, discussions on several challenges regarding navigation path planning, localization, obstacle avoidance, security, localization problem categories, etc., and potential future perspectives on MRL techniques and applications are highlighted.</p></div>","PeriodicalId":48633,"journal":{"name":"Computer Science Review","volume":"53 ","pages":"Article 100651"},"PeriodicalIF":13.3,"publicationDate":"2024-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141542855","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}

引用次数: 0

Applicability of genetic algorithms for stock market prediction: A systematic survey of the last decade 遗传算法在股市预测中的适用性：过去十年的系统调查

IF 13.3 1区计算机科学

Computer Science Review Pub Date : 2024-07-03 DOI: 10.1016/j.cosrev.2024.100652

Ankit Thakkar, Kinjal Chaudhari

{"title":"Applicability of genetic algorithms for stock market prediction: A systematic survey of the last decade","authors":"Ankit Thakkar, Kinjal Chaudhari","doi":"10.1016/j.cosrev.2024.100652","DOIUrl":"https://doi.org/10.1016/j.cosrev.2024.100652","url":null,"abstract":"<div><p>Stock market is one of the attractive domains for researchers as well as academicians. It represents highly complex non-linear fluctuating market behaviours where traders, investors, and organizers look forward to reliable future predictions of the market indices. Such prediction problems can be computationally addressed using various machine learning, deep learning, sentiment analysis, as well as mining approaches. However, the internal parameters configuration can play an important role in the prediction performance; also, feature selection is a crucial task. Therefore, to optimize such approaches, the evolutionary computation-based algorithms can be integrated in several ways. In this article, we systematically conduct a focused survey on genetic algorithm (GA) and its applications for stock market prediction; GAs are known for their parallel search mechanism to solve complex real-world problems; various genetic perspectives are also integrated with machine learning and deep learning methods to address financial forecasting. Thus, we aim to analyse the potential extensibility and adaptability of GAs for stock market prediction. We review stock price and stock trend prediction, as well as portfolio optimization, approaches over the recent years (2013–2022) to signify the state-of-the-art of GA-based optimization in financial markets. We broaden our discussion by briefly reviewing other genetic perspectives and their applications for stock market forecasting. We balance our survey with the consideration of competitiveness and complementation of GAs, followed by highlighting the challenges and potential future research directions of applying GAs for stock market prediction.</p></div>","PeriodicalId":48633,"journal":{"name":"Computer Science Review","volume":"53 ","pages":"Article 100652"},"PeriodicalIF":13.3,"publicationDate":"2024-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141542853","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}

引用次数: 0

Reproducibility, Replicability and Repeatability: A survey of reproducible research with a focus on high performance computing 可重复性、可复制性和可重复性：以高性能计算为重点的可重复研究调查

IF 13.3 1区计算机科学

Computer Science Review Pub Date : 2024-07-03 DOI: 10.1016/j.cosrev.2024.100655

Benjamin Antunes, David R.C. Hill

引用次数: 0

A survey on computation offloading in edge systems: From the perspective of deep reinforcement learning approaches 边缘系统计算卸载调查：从深度强化学习方法的角度

IF 13.3 1区计算机科学

Computer Science Review Pub Date : 2024-06-29 DOI: 10.1016/j.cosrev.2024.100656

Peng Peng , Weiwei Lin , Wentai Wu , Haotong Zhang , Shaoliang Peng , Qingbo Wu , Keqin Li

{"title":"A survey on computation offloading in edge systems: From the perspective of deep reinforcement learning approaches","authors":"Peng Peng , Weiwei Lin , Wentai Wu , Haotong Zhang , Shaoliang Peng , Qingbo Wu , Keqin Li","doi":"10.1016/j.cosrev.2024.100656","DOIUrl":"https://doi.org/10.1016/j.cosrev.2024.100656","url":null,"abstract":"<div><p>Driven by the demand of time-sensitive and data-intensive applications, edge computing has attracted wide attention as one of the cornerstones of modern service architectures. An edge-based system can facilitate a flexible processing of tasks over heterogeneous resources. Hence, computation offloading is the key technique for systematic service improvement. However, with the proliferation of devices, traditional approaches have clear limits in handling dynamic and heterogeneous systems at scale. Deep Reinforcement Learning (DRL), as a promising alternative, has shown great potential with powerful high-dimensional perception and decision-making capability to enable intelligent offloading, but the great complexity in DRL-based algorithm design turns out to be an obstacle. In light of this, this survey provides a comprehensive view of DRL-based approaches to computation offloading in edge computing systems. We cover state-of-the-art advances by delving into the fundamental elements of DRL algorithm design with focuses on the target environmental factors, Markov Decision Process (MDP) model construction, and refined learning strategies. Based on our investigation, several open challenges are further highlighted from both the perspective of algorithm design and realistic requirements that deserve more attention in future research.</p></div>","PeriodicalId":48633,"journal":{"name":"Computer Science Review","volume":"53 ","pages":"Article 100656"},"PeriodicalIF":13.3,"publicationDate":"2024-06-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141485434","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}

引用次数: 0

A survey on detecting mental disorders with natural language processing: Literature review, trends and challenges 利用自然语言处理检测精神障碍的调查：文献综述、趋势与挑战

IF 13.3 1区计算机科学

Computer Science Review Pub Date : 2024-06-22 DOI: 10.1016/j.cosrev.2024.100654

Arturo Montejo-Ráez , M. Dolores Molina-González , Salud María Jiménez-Zafra , Miguel Ángel García-Cumbreras , Luis Joaquín García-López

{"title":"A survey on detecting mental disorders with natural language processing: Literature review, trends and challenges","authors":"Arturo Montejo-Ráez , M. Dolores Molina-González , Salud María Jiménez-Zafra , Miguel Ángel García-Cumbreras , Luis Joaquín García-López","doi":"10.1016/j.cosrev.2024.100654","DOIUrl":"https://doi.org/10.1016/j.cosrev.2024.100654","url":null,"abstract":"<div><p>For years, the scientific community has researched monitoring approaches for the detection of certain mental disorders and risky behaviors, like depression, eating disorders, gambling, and suicidal ideation among others, in order to activate prevention or mitigation strategies and, in severe cases, clinical treatment. Natural Language Processing is one of the most active disciplines dealing with the automatic detection of mental disorders. This paper offers a comprehensive and extensive review of research works on Natural Language Processing applied to the identification of some mental disorders. To this end, we have identified from a literature review, which are the main types of features used to represent the texts, the machine learning algorithms that are preferred or the most targeted social media platforms, among other aspects. Besides, the paper reports on scientific forums and projects focused on the automatic detection of these problems over the most popular social networks. Thus, this compilation provides a broad view of the matter, summarizing main strategies, and significant findings, but, also, recognizing some of the weaknesses in the research works published so far, serving as clues for future research.</p></div>","PeriodicalId":48633,"journal":{"name":"Computer Science Review","volume":"53 ","pages":"Article 100654"},"PeriodicalIF":13.3,"publicationDate":"2024-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1574013724000388/pdfft?md5=1aa9d3d86e8e2a92377e4b8afd982458&pid=1-s2.0-S1574013724000388-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141444644","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}

引用次数: 0

Adding relevance to rigor: Assessing the contributions of SLRs in Software Engineering through Citation Context Analysis 为严谨性添加相关性：通过引文上下文分析评估软件工程中 SLR 的贡献

IF 12.9 1区计算机科学

Computer Science Review Pub Date : 2024-06-18 DOI: 10.1016/j.cosrev.2024.100649

Oscar Díaz , Marcela Genero , Jeremías P. Contell , Mario Piattini

{"title":"Adding relevance to rigor: Assessing the contributions of SLRs in Software Engineering through Citation Context Analysis","authors":"Oscar Díaz , Marcela Genero , Jeremías P. Contell , Mario Piattini","doi":"10.1016/j.cosrev.2024.100649","DOIUrl":"https://doi.org/10.1016/j.cosrev.2024.100649","url":null,"abstract":"<div><p>Research in Software Engineering greatly benefits from Systematic Literature Reviews (SLRs), in view of the citations they receive. While there has been a focus on improving the quality of SLRs in terms of the process, it remains unclear if this emphasis on rigor has also led to an increase in relevance. This study introduces Citation Context Analysis for SLRs as a method to go beyond simple citation counting by examining the reasons behind citations. To achieve this, we propose the Resonance Scheme, which characterizes how referring papers use SLRs based on the outputs that SLRs can provide, either backward-oriented (such as synthesis or aggregating evidence) or forward-oriented (such as theory building or identifying research gaps). A proof-of-concept demonstrates that most referring papers appreciate SLRs for their synthesis efforts, while only a small number refer to forward-oriented outputs. This approach is expected to be useful for three stakeholders. First, SLR producers can use the scheme to capture the contributions of their SLRs. Second, SLR consumers, such as Ph.D. students looking for research gaps, can easily identify the contributions of interest. Third, SLR reviewers can use the scheme as a tool to assess the contributions that merit SLR publication.</p></div>","PeriodicalId":48633,"journal":{"name":"Computer Science Review","volume":"53 ","pages":"Article 100649"},"PeriodicalIF":12.9,"publicationDate":"2024-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141423843","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}

引用次数: 0

A comprehensive review on transformer network for natural and medical image analysis 用于自然和医学图像分析的变压器网络综述

IF 12.9 1区计算机科学

Computer Science Review Pub Date : 2024-06-14 DOI: 10.1016/j.cosrev.2024.100648

Ramkumar Thirunavukarasu , Evans Kotei

{"title":"A comprehensive review on transformer network for natural and medical image analysis","authors":"Ramkumar Thirunavukarasu , Evans Kotei","doi":"10.1016/j.cosrev.2024.100648","DOIUrl":"https://doi.org/10.1016/j.cosrev.2024.100648","url":null,"abstract":"<div><p>The Transformer network is the main application area for natural language processing. It has gained traction lately and exhibits potential in the field of computer vision. This cutting-edge method has proven to offer a significant impact on image analysis, a crucial area of computer vision. The transformer's outstanding performance in vision computing places it as an alternative to the convolutional neural network for vision tasks. Transformers have taken center stage in the field of natural language processing. Despite the outstanding performance of transformer networks in natural image processing, their implementation in medical image analysis is gradually gaining roots. This study focuses on the transformer application in natural and medical image analysis. The first part of the study provides an overview of the core concepts of the attention mechanism built into transformers for long-range feature extraction. The study again highlights the various transformer architectures proposed for natural and medical image tasks such as segmentation, classification, image registration and diagnosis. Finally, the paper presents limitations identified in proposed transformer networks for natural and medical image processing. It also highlights prospective study opportunities for further research to better the computer vision domain, especially medical image analysis. This study offers knowledge to scholars and researchers studying computer vision applications as they focus on creating innovative transformer network-based solutions.</p></div>","PeriodicalId":48633,"journal":{"name":"Computer Science Review","volume":"53 ","pages":"Article 100648"},"PeriodicalIF":12.9,"publicationDate":"2024-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141326092","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}

引用次数: 0