{"title":"Distributed ledger technologies for authentication and access control in networking applications: A comprehensive survey","authors":"Fariba Ghaffari , Emmanuel Bertin , Noel Crespi , Julien Hatin","doi":"10.1016/j.cosrev.2023.100590","DOIUrl":"10.1016/j.cosrev.2023.100590","url":null,"abstract":"<div><p><span>The accelerated growth of networking technologies highlights the importance of Authentication and Access Control (AAC) as protection against associated attacks. Controlling access to resources, facilitating resource sharing, and managing user mobility are some of the notable capabilities provided by AAC methods. Centralized methods are the most common </span>deployment architectures, that can be threatened by several attacks at their central points. Emerging Distributed Ledger Technology (DLT) has attracted significant interest in the AAA community. The distributed nature of DLT and its immutability can bring unprecedented opportunities to resolve many of the challenges of conventional systems. We survey the state-of-the-art in deploying authentication and access control approaches via DLT for several networking use cases. More precisely, we explore DLT applications in (1) Authentication; (2) Access Control; and (3) Comprehensive AAC solutions. First, we present the challenges of centralized solutions and discuss the capability of DLT for their resolution. Then, we propose a taxonomy to categorize the existing methods. Analysis, comparison, and discussion on the advantages and disadvantages of these methods have been provided regarding different parameters such as DLT types, AAC approaches, security, reliability, scalability, etc. While DLT provides various benefits, several challenges remain for the migration to DLT-based AAC. In light of these general limitations, we propose some future directions, targeting the current lacunae and future needs.</p></div>","PeriodicalId":48633,"journal":{"name":"Computer Science Review","volume":"50 ","pages":"Article 100590"},"PeriodicalIF":12.9,"publicationDate":"2023-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48129408","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 review of IoT security and privacy using decentralized blockchain techniques","authors":"Vinay Gugueoth , Sunitha Safavat , Sachin Shetty , Danda Rawat","doi":"10.1016/j.cosrev.2023.100585","DOIUrl":"10.1016/j.cosrev.2023.100585","url":null,"abstract":"<div><p>IoT security is one of the prominent issues that has gained significant attention among the researchers in recent times. The recent advancements in IoT introduces various critical security issues and increases the risk of privacy leakage of IoT data. Implementation of Blockchain can be a potential solution for the security issues in IoT. This review deeply investigates the security threats and issues in IoT which deteriorates the effectiveness of IoT systems. This paper presents a perceptible description of the security threats, Blockchain based solutions, security characteristics and challenges introduced during the integration of Blockchain with IoT. An analysis of different consensus protocols, existing security techniques and evaluation parameters are discussed in brief. In addition, the paper also outlines the open issues and highlights possible research opportunities which can be beneficial for future research.</p></div>","PeriodicalId":48633,"journal":{"name":"Computer Science Review","volume":"50 ","pages":"Article 100585"},"PeriodicalIF":12.9,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42243609","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":"An empirical investigation of task scheduling and VM consolidation schemes in cloud environment","authors":"Sweta Singh , Rakesh Kumar , Dayashankar Singh","doi":"10.1016/j.cosrev.2023.100583","DOIUrl":"10.1016/j.cosrev.2023.100583","url":null,"abstract":"<div><p><span><span>Cloud computing has evolved as a new paradigm in Internet computing, offering services to the end-users and large-organizations, on-demand and pay-per-the-usage basis with high availability, elasticity, scalability and resiliency. In order to improve the performance of the </span>Cloud system, handling multiple heterogeneous tasks concurrently, an appropriate task scheduler is required. To meet the user’s requirements in terms of Quality of Service (QoS) parameters, the task </span>scheduling algorithm<span><span> should identify the order in which tasks should be executed. Energy efficiency is the significant challenge in today’s task scheduling to meet the prerequisite for green computing. By increasing resource utilization at the </span>data centers, virtual machine (VM) Consolidation is also recognized as the most widely used and promising approach in terms of energy consumption and system performance. However, excessive VM Consolidation could constitute a violation of the Service Level Agreement (SLA). The paper makes a contribution by outlining the numerous approaches that researchers have used thus far to achieve the goals of scheduling and VM Consolidation, assuring energy efficiency, and maintaining system performance. This would give readers a better understanding of the problems and the potential for improvement while assisting them in selecting the ideal scheduling algorithm with Consolidation technique. Additionally, the techniques are divided into three categories: those that primarily focus on task scheduling; those that target Consolidation; and complete computation, integrating task scheduling with VM Consolidation. Further classification for the scheduling algorithms include heuristic, meta-heuristic, greedy, and hybrid task scheduling algorithms. In addition to a summary of the benefits and drawbacks of the suggested algorithms, prospective research directions and recent developments in this area is also covered in this paper.</span></p></div>","PeriodicalId":48633,"journal":{"name":"Computer Science Review","volume":"50 ","pages":"Article 100583"},"PeriodicalIF":12.9,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47118855","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":"Integration of hyperspectral imaging and autoencoders: Benefits, applications, hyperparameter tunning and challenges","authors":"Garima Jaiswal , Ritu Rani , Harshita Mangotra , Arun Sharma","doi":"10.1016/j.cosrev.2023.100584","DOIUrl":"10.1016/j.cosrev.2023.100584","url":null,"abstract":"<div><p><span>Hyperspectral imaging (HSI) is a powerful tool that can capture and analyze a range of spectral bands, providing unparalleled levels of precision and accuracy in data analysis. Another technology gaining popularity in many industries is </span>Autoencoders<span> (AE). AE uses advanced deep learning algorithms<span><span> for encoding and decoding data, leading to highly precise and efficient neural network-based models. Within the domain of HSI, AE emerges as a potent approach to tackle the essential hurdles associated with data analysis and feature extraction. Combining both HSI and AE (HSI – AE) can lead to a revolution in various industries, including but not limited to healthcare and environmental monitoring, because of more efficient analysis approaches and decision-making. AE can be used to discover hidden patterns and insights in large-scale datasets, allowing researchers to make more informed decisions based on much better predictions. Similarly, HSI can benefit from the scalability and flexibility AE offers, leading to faster and more efficient data processing. This article aims to provide a comprehensive review of the integration of HSI - AE, covering the history and background knowledge, motivation, and combined benefits of HSI and AE. It examines the applicability of HSI-AE in many use-case domains, such as classification, hyperspectral </span>unmixing<span>, and anomaly detection. It also provides a hyperparameter tuning and an in-depth survey of their use. The article emphasizes crucial areas for future exploration, such as conducting further research to enhance AE’s performance in HSI applications and devising novel algorithms to overcome the distinctive challenges presented by HSI data. Overall, the culmination of the HSI with AE can be seen as offering a promising solution for challenges like data analysis management and pattern recognition, enabling accurate and efficient decision-making across industries.</span></span></span></p></div>","PeriodicalId":48633,"journal":{"name":"Computer Science Review","volume":"50 ","pages":"Article 100584"},"PeriodicalIF":12.9,"publicationDate":"2023-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48705588","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":"Comprehensive survey of the solving puzzle problems","authors":"Seçkin Yılmaz , Vasif V. Nabiyev","doi":"10.1016/j.cosrev.2023.100586","DOIUrl":"10.1016/j.cosrev.2023.100586","url":null,"abstract":"<div><p>Solving puzzle problems using computer-aided methods is becoming more common with applications in forensic science, restoration, banking system, and multimedia. However, only a few surveys have been published on this topic, the most recent being more than a decade old. The scope of 2D puzzle problems is extensive, and the number of computer-aided methods has increased in recent years. In this paper, we have presented a comprehensive survey to pave a roadmap for researchers dealing with puzzle problems. This study classifies 2D puzzle problems in a novel way, considering many examples such as dissection, combinatorial and double-sided puzzles and reclassifies computer-aided methods to cover the studies carried out in recent years. Various strategies (pre-grouping and global consistency approach) have been investigated to solve the puzzle problem effectively. The computer-aided methods have been examined deeply, including many recent methods related to squared jigsaw puzzles, torn photographs, banknotes, and fragmented documents, and they are compared to each other. In addition, new topics such as combining mosaic pieces and Islamic architectural motif puzzle problems have been proposed to the interest of researchers. In conclusion, our study shows many research opportunities that are not yet solved by any computer-aided method.</p></div>","PeriodicalId":48633,"journal":{"name":"Computer Science Review","volume":"50 ","pages":"Article 100586"},"PeriodicalIF":12.9,"publicationDate":"2023-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42925912","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}
Mohammad Hossein Tabatabaei, Roman Vitenberg, Narasimha Raghavan Veeraragavan
{"title":"Understanding blockchain: Definitions, architecture, design, and system comparison","authors":"Mohammad Hossein Tabatabaei, Roman Vitenberg, Narasimha Raghavan Veeraragavan","doi":"10.1016/j.cosrev.2023.100575","DOIUrl":"https://doi.org/10.1016/j.cosrev.2023.100575","url":null,"abstract":"<div><p>The explosive advent of the blockchain technology has led to hundreds of blockchain systems in the industry, thousands of academic papers published over the last few years, and an even larger number of new initiatives and projects. Despite the emerging consolidation efforts, the area remains highly turbulent without systematization, educational materials, or cross-system comparative analysis.</p><p>In this paper, we provide a systematic and comprehensive study of four popular yet widely different blockchain systems: Bitcoin<span>, Ethereum, Hyperledger Fabric, and IOTA. The study is presented as a cross-system comparison, which is organized by clearly identified aspects: definitions, roles of the participants, entities, and the characteristics and design of each of the commonly used layers in the cross-system blockchain architecture. Our exploration goes deeper compared to what is currently available in academic surveys and tutorials. For example, we provide the first extensive coverage of the storage layer in Ethereum and the most comprehensive explanation of the consensus protocol in IOTA. The exposition is due to the consolidation of fragmented information gathered from white and yellow papers, academic publications, blogs, developer documentation, communication with the developers, as well as additional analysis gleaned from the source code. We hope that this survey will help the readers gain in-depth understanding of the design principles behind blockchain systems and contribute towards systematization of the area.</span></p></div>","PeriodicalId":48633,"journal":{"name":"Computer Science Review","volume":"50 ","pages":"Article 100575"},"PeriodicalIF":12.9,"publicationDate":"2023-08-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49739122","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}
Panagiotis Bountakas, Apostolis Zarras, A. Lekidis, C. Xenakis
{"title":"Defense strategies for Adversarial Machine Learning: A survey","authors":"Panagiotis Bountakas, Apostolis Zarras, A. Lekidis, C. Xenakis","doi":"10.1016/j.cosrev.2023.100573","DOIUrl":"https://doi.org/10.1016/j.cosrev.2023.100573","url":null,"abstract":"","PeriodicalId":48633,"journal":{"name":"Computer Science Review","volume":"49 1","pages":"100573"},"PeriodicalIF":12.9,"publicationDate":"2023-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"54128374","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":"Advances in nature-inspired metaheuristic optimization for feature selection problem: A comprehensive survey","authors":"Maha Nssibi , Ghaith Manita , Ouajdi Korbaa","doi":"10.1016/j.cosrev.2023.100559","DOIUrl":"https://doi.org/10.1016/j.cosrev.2023.100559","url":null,"abstract":"<div><p>The main objective of feature selection is to improve learning performance by selecting concise and informative feature subsets, which presents a challenging task for machine learning<span> or pattern recognition applications due to the large and complex search space involved. This paper provides an in-depth examination of nature-inspired metaheuristic methods for the feature selection problem, with a focus on representation and search algorithms, as they have drawn significant interest from the feature selection community due to their potential for global search and simplicity. An analysis of various advanced approach types, along with their advantages and disadvantages, is presented in this study, with the goal of highlighting important issues and unanswered questions in the literature. The article provides advice for conducting future research more effectively to benefit this field of study, including guidance on identifying appropriate approaches to use in different scenarios.</span></p></div>","PeriodicalId":48633,"journal":{"name":"Computer Science Review","volume":"49 ","pages":"Article 100559"},"PeriodicalIF":12.9,"publicationDate":"2023-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49725247","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":"Aspect based sentiment analysis using deep learning approaches: A survey","authors":"Ganpat Singh Chauhan , Ravi Nahta , Yogesh Kumar Meena , Dinesh Gopalani","doi":"10.1016/j.cosrev.2023.100576","DOIUrl":"https://doi.org/10.1016/j.cosrev.2023.100576","url":null,"abstract":"<div><p>The wealth of unstructured text on the online web portal has made opinion mining<span> the most thrust area for researchers, academicians, and businesses to extract information for gathering, analyzing, and aggregating human emotions. The extraction of public sentiment from the text at an aspect level has contributed exceptionally to various businesses in the marketplace. In recent times, deep learning-based techniques have learned high-level linguistic features without high-level feature engineering. Therefore, this paper focuses on a rigorous survey on two primary subtasks, aspect extraction and aspect category detection of aspect-based sentiment analysis (ABSA) methods based on deep learning. The significant advancement in the ABSA sector is demonstrated by a thorough evaluation of state-of-the-art and latest aspect extraction methodologies.</span></p></div>","PeriodicalId":48633,"journal":{"name":"Computer Science Review","volume":"49 ","pages":"Article 100576"},"PeriodicalIF":12.9,"publicationDate":"2023-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49737724","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}