Computer Science Review最新文献_第4页

Recent advances in anomaly detection in Internet of Things: Status, challenges, and perspectives 物联网异常检测的最新进展：现状、挑战和前景

IF 13.3 1区计算机科学

Computer Science Review Pub Date : 2024-08-22 DOI: 10.1016/j.cosrev.2024.100665

Deepak Adhikari , Wei Jiang , Jinyu Zhan , Danda B. Rawat , Asmita Bhattarai

{"title":"Recent advances in anomaly detection in Internet of Things: Status, challenges, and perspectives","authors":"Deepak Adhikari , Wei Jiang , Jinyu Zhan , Danda B. Rawat , Asmita Bhattarai","doi":"10.1016/j.cosrev.2024.100665","DOIUrl":"10.1016/j.cosrev.2024.100665","url":null,"abstract":"<div><p>This paper provides a comprehensive survey of anomaly detection for the Internet of Things (IoT). Anomaly detection poses numerous challenges in IoT, with broad applications, including intrusion detection, fraud monitoring, cybersecurity, industrial automation, etc. Intensive attention has been received by network security analytics and researchers, particularly on anomaly detection in the network, deliberately crucial in network security. It is of critical importance to detect network anomalies timely. Due to various issues and resource-constrained features, conventional anomaly detection strategies cannot be implemented in the IoT. Hence, this paper attempts to highlight various recent techniques to detect anomalies in IoT and its applications. We also present anomalies at multiple layers of the IoT architecture. In addition, we discuss multiple computing platforms and highlight various challenges of anomaly detection. Finally, the potential future directions of the methods are suggested, leading to various open research issues to be analyzed afterward. With this survey, we hope that readers can get a better understanding of anomaly detection, as well as research trends in this domain.</p></div>","PeriodicalId":48633,"journal":{"name":"Computer Science Review","volume":"54 ","pages":"Article 100665"},"PeriodicalIF":13.3,"publicationDate":"2024-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142044588","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 systematic survey on fault-tolerant solutions for distributed data analytics: Taxonomy, comparison, and future directions 分布式数据分析容错解决方案系统调查：分类、比较和未来方向

IF 13.3 1区计算机科学

Computer Science Review Pub Date : 2024-08-01 DOI: 10.1016/j.cosrev.2024.100660

Sucharitha Isukapalli, Satish Narayana Srirama

{"title":"A systematic survey on fault-tolerant solutions for distributed data analytics: Taxonomy, comparison, and future directions","authors":"Sucharitha Isukapalli, Satish Narayana Srirama","doi":"10.1016/j.cosrev.2024.100660","DOIUrl":"10.1016/j.cosrev.2024.100660","url":null,"abstract":"<div><p>Fault tolerance is becoming increasingly important for upcoming exascale systems, supporting distributed data processing, due to the expected decrease in the Mean Time Between Failures (MTBF). To ensure the availability, reliability, dependability, and performance of the system, addressing the fault tolerance challenge is crucial. It aims to keep the distributed system running at a reduced capacity while avoiding complete data loss, even in the presence of faults, with minimal impact on system performance. This comprehensive survey aims to provide a detailed understanding of the importance of fault tolerance in distributed systems, including a classification of faults, errors, failures, and fault-tolerant techniques (reactive, proactive, and predictive). We collected a corpus of 490 papers published from 2014 to 2023 by searching in Scopus, IEEE Xplore, Springer, and ACM digital library databases. After a systematic review, 17 reactive models, 17 proactive models, and 14 predictive models were shortlisted and compared. A taxonomy of ideas behind the proposed models was also created for each of these categories of fault-tolerant solutions. Additionally, it examines how fault tolerance capability is incorporated into popular big data processing tools such as Apache Hadoop, Spark, and Flink. Finally, promising future research directions in this domain are discussed.</p></div>","PeriodicalId":48633,"journal":{"name":"Computer Science Review","volume":"53 ","pages":"Article 100660"},"PeriodicalIF":13.3,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141910592","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

Deep learning for hyperspectral image classification: A survey 用于高光谱图像分类的深度学习：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.2024.100658

Vinod Kumar , Ravi Shankar Singh , Medara Rambabu , Yaman Dua

{"title":"Deep learning for hyperspectral image classification: A survey","authors":"Vinod Kumar , Ravi Shankar Singh , Medara Rambabu , Yaman Dua","doi":"10.1016/j.cosrev.2024.100658","DOIUrl":"10.1016/j.cosrev.2024.100658","url":null,"abstract":"<div><p>Hyperspectral image (HSI) classification is a significant topic of discussion in real-world applications. The prevalence of these applications stems from the precise spectral information offered by each pixelś data in hyperspectral imaging (HS). Classical machine learning (ML) methods face challenges in precise object classification with HSI data complexity. The intrinsic non-linear relationship between spectral information and materials complicates the task. Deep learning (DL) has proven to be a robust feature extractor in computer vision, effectively addressing nonlinear challenges. This validation drives its integration into HSI classification, which proves to be highly effective. This review compares DL approaches to HSI classification, highlighting its superiority over classical ML algorithms. Subsequently, a framework is constructed to analyze current advances in DL-based HSI classification, categorizing studies based on a network using only spectral features, spatial features, or both spectral–spatial features. Moreover, we have explained a few recent advanced DL models. Additionally, the study acknowledges that DL demands a substantial number of labeled training instances. However, obtaining such a large dataset for the HSI classification framework proves to be time and cost-intensive. So, we also explain the DL methodologies, which work well with the limited training data availability. Consequently, the survey introduces techniques aimed at enhancing the generalization performance of DL procedures, offering guidance for the future.</p></div>","PeriodicalId":48633,"journal":{"name":"Computer Science Review","volume":"53 ","pages":"Article 100658"},"PeriodicalIF":13.3,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141891665","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

Unsupervised affinity learning based on manifold analysis for image retrieval: A survey 基于流形分析的图像检索无监督亲和性学习：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.2024.100657

V.H. Pereira-Ferrero , T.G. Lewis , L.P. Valem , L.G.P. Ferrero , D.C.G. Pedronette , L.J. Latecki

{"title":"Unsupervised affinity learning based on manifold analysis for image retrieval: A survey","authors":"V.H. Pereira-Ferrero , T.G. Lewis , L.P. Valem , L.G.P. Ferrero , D.C.G. Pedronette , L.J. Latecki","doi":"10.1016/j.cosrev.2024.100657","DOIUrl":"10.1016/j.cosrev.2024.100657","url":null,"abstract":"<div><p>Despite the advances in machine learning techniques, similarity assessment among multimedia data remains a challenging task of broad interest in computer science. Substantial progress has been achieved in acquiring meaningful data representations, but how to compare them, plays a pivotal role in machine learning and retrieval tasks. Traditional pairwise measures are widely used, yet unsupervised affinity learning approaches have emerged as a valuable solution for enhancing retrieval effectiveness. These methods leverage the dataset manifold to encode contextual information, refining initial similarity/dissimilarity measures through post-processing. In other words, measuring the similarity between data objects within the context of other data objects is often more effective. This survey provides a comprehensive discussion about unsupervised post-processing methods, addressing the historical development and proposing an organization of the area, with a specific emphasis on image retrieval. A systematic review was conducted contributing to a formal understanding of the field. Additionally, an experimental study is presented to evaluate the potential of such methods in improving retrieval results, focusing on recent features extracted from Convolutional Neural Networks (CNNs) and Transformer models, in 8 distinct datasets, and over 329.877 images analyzed. State-of-the-art comparison for Flowers, Corel5k, and ALOI datasets, the Rank Flow Embedding method outperformed all state-of-art approaches, achieving 99.65%, 96.79%, and 97.73%, respectively.</p></div>","PeriodicalId":48633,"journal":{"name":"Computer Science Review","volume":"53 ","pages":"Article 100657"},"PeriodicalIF":13.3,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141891666","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

Digital image watermarking using deep learning: A survey 使用深度学习的数字图像水印：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.2024.100662

Khalid M. Hosny, Amal Magdi, Osama ElKomy, Hanaa M． Hamza

引用次数: 0

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