Journal of cloud computing (Heidelberg, Germany)最新文献

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Future of industry 5.0 in society: human-centric solutions, challenges and prospective research areas. 工业5.0在社会中的未来:以人为本的解决方案、挑战和前瞻性研究领域。

IF 4

Journal of cloud computing (Heidelberg, Germany) Pub Date : 2022-01-01 Epub Date: 2022-09-08 DOI: 10.1186/s13677-022-00314-5

Amr Adel

{"title":"Future of industry 5.0 in society: human-centric solutions, challenges and prospective research areas.","authors":"Amr Adel","doi":"10.1186/s13677-022-00314-5","DOIUrl":"https://doi.org/10.1186/s13677-022-00314-5","url":null,"abstract":"Industry 4.0 has been provided for the last 10 years to benefit the industry and the shortcomings; finally, the time for industry 5.0 has arrived. Smart factories are increasing the business productivity; therefore, industry 4.0 has limitations. In this paper, there is a discussion of the industry 5.0 opportunities as well as limitations and the future research prospects. Industry 5.0 is changing paradigm and brings the resolution since it will decrease emphasis on the technology and assume that the potential for progress is based on collaboration among the humans and machines. The industrial revolution is improving customer satisfaction by utilizing personalized products. In modern business with the paid technological developments, industry 5.0 is required for gaining competitive advantages as well as economic growth for the factory. The paper is aimed to analyze the potential applications of industry 5.0. At first, there is a discussion of the definitions of industry 5.0 and advanced technologies required in this industry revolution. There is also discussion of the applications enabled in industry 5.0 like healthcare, supply chain, production in manufacturing, cloud manufacturing, etc. The technologies discussed in this paper are big data analytics, Internet of Things, collaborative robots, Blockchain, digital twins and future 6G systems. The study also included difficulties and issues examined in this paper head to comprehend the issues caused by organizations among the robots and people in the assembly line.","PeriodicalId":520665,"journal":{"name":"Journal of cloud computing (Heidelberg, Germany)","volume":" ","pages":"40"},"PeriodicalIF":4.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9454409/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40355599","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 75

A malware detection system using a hybrid approach of multi-heads attention-based control flow traces and image visualization. 一种基于多头注意力控制流跟踪和图像可视化混合方法的恶意软件检测系统。

IF 4

Journal of cloud computing (Heidelberg, Germany) Pub Date : 2022-01-01 Epub Date: 2022-11-03 DOI: 10.1186/s13677-022-00349-8

Farhan Ullah, Gautam Srivastava, Shamsher Ullah

{"title":"A malware detection system using a hybrid approach of multi-heads attention-based control flow traces and image visualization.","authors":"Farhan Ullah, Gautam Srivastava, Shamsher Ullah","doi":"10.1186/s13677-022-00349-8","DOIUrl":"https://doi.org/10.1186/s13677-022-00349-8","url":null,"abstract":"Android is the most widely used mobile platform, making it a prime target for malicious attacks. Therefore, it is imperative to effectively circumvent these attacks. Recently, machine learning has been a promising solution for malware detection, which relies on distinguishing features. While machine learning-based malware scanners have a large number of features, adversaries can avoid detection by using feature-related expertise. Therefore, one of the main tasks of the Android security industry is to consistently propose cutting-edge features that can detect suspicious activity. This study presents a novel feature representation approach for malware detection that combines API-Call Graphs (ACGs) with byte-level image representation. First, the reverse engineering procedure is used to obtain the Java programming codes and Dalvik Executable (DEX) file from Android Package Kit (APK). Second, to depict Android apps with high-level features, we develop ACGs by mining API-Calls and API sequences from Control Flow Graph (CFG). The ACGs can act as a digital fingerprint of the actions taken by Android apps. Next, the multi-head attention-based transfer learning method is used to extract trained features vector from ACGs. Third, the DEX file is converted to a malware image, and the texture features are extracted and highlighted using a combination of FAST (Features from Accelerated Segment Test) and BRIEF (Binary Robust Independent Elementary Features). Finally, the ACGs and texture features are combined for effective malware detection and classification. The proposed method uses a customized dataset prepared from the CIC-InvesAndMal2019 dataset and outperforms state-of-the-art methods with 99.27% accuracy.","PeriodicalId":520665,"journal":{"name":"Journal of cloud computing (Heidelberg, Germany)","volume":" ","pages":"75"},"PeriodicalIF":4.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9633577/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40672084","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 9

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