{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,6]],"date-time":"2026-02-06T07:00:52Z","timestamp":1770361252216,"version":"3.49.0"},"reference-count":36,"publisher":"Wiley","issue":"3","license":[{"start":{"date-parts":[[2026,2,4]],"date-time":"2026-02-04T00:00:00Z","timestamp":1770163200000},"content-version":"vor","delay-in-days":3,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2026,2,1]],"date-time":"2026-02-01T00:00:00Z","timestamp":1769904000000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Concurrency and Computation"],"published-print":{"date-parts":[[2026,2]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:p>Cloud computing is the massive evolution in Information Technology (IT) that provides virtualized and scalable resources to an end\u2010user with minimum maintenance and cost. However, this environment is vulnerable to various attacks. These attacks impose heavy damages and affect the performance of the cloud. Therefore, timely detection of attacks in cloud computing is crucial. Hence, an efficient detection model is required for identifying various attacks in cloud computing. In this research, Shuffle Attention Convolutional Forward Fractional Network (SACFFNet) is presented to detect attacks in cloud computing. Initially, the cloud is simulated and next, a recorded log file is obtained from certain datasets. Thereafter, feature scaling is accomplished employing the minimum\u2010maximum method. Then, features are selected using chord distance and Support Vector Machine Recursive Feature Elimination (SVM\u2010RFE). Afterwards, data augmentation is done utilizing Synthetic Minority Oversampling Technique (SMOTE). Lastly, attacks are detected employing the newly designed SACFFNet. The SACFFNet is designed by integrating Shuffle Attention Network (SA\u2010Net) with Convolutional Neural Network (CNN) with the modification of layers based on Fractional Calculus (FC). Additionally, SACFFNet has attained 91.678% of accuracy, 92.884% of sensitivity, and 92.090% of specificity.<\/jats:p>","DOI":"10.1002\/cpe.70504","type":"journal-article","created":{"date-parts":[[2026,2,4]],"date-time":"2026-02-04T14:23:38Z","timestamp":1770215018000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["<scp>SACFFNet<\/scp>\n                    : Shuffle Attention Convolutional Forward Fractional Network Based Attack Detection in Cloud Computing"],"prefix":"10.1002","volume":"38","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2992-1781","authenticated-orcid":false,"given":"Aditya Kumar","family":"Shukla","sequence":"first","affiliation":[{"name":"Cisco Systems (India) Private Limited  Noida Uttar Pradesh India"}]},{"given":"Ashish","family":"Sharma","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering and Applications GLA University  Mathura Uttar Pradesh India"}]},{"given":"Sandeep Singh","family":"Sengar","sequence":"additional","affiliation":[{"name":"Department of Computer Science Cardiff School of Technologies, Cardiff Metropolitan University  Cardiff UK"}]}],"member":"311","published-online":{"date-parts":[[2026,2,4]]},"reference":[{"issue":"2","key":"e_1_2_8_2_1","first-page":"1","article-title":"Nsight Into Insiders and It: A Survey of Insider Threat Taxonomies, Analysis, Modeling, and Countermeasures","volume":"52","author":"Homoliak I.","year":"2019","journal-title":"CM Computing Surveys (CSUR)"},{"key":"e_1_2_8_3_1","doi-asserted-by":"publisher","DOI":"10.32604\/csse.2022.019940"},{"key":"e_1_2_8_4_1","volume-title":"IEEE INFOCOM 2019\u2010IEEE Conference on Computer Communications","author":"Zhou H.","year":"2019"},{"key":"e_1_2_8_5_1","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1155\/2023\/2039217","article-title":"Optimization Enabled Deep Learning\u2010Based DDoS Attack Detection in Cloud Computing","volume":"2023","author":"Balasubramaniam S.","year":"2023","journal-title":"International Journal of Intelligent Systems"},{"issue":"2","key":"e_1_2_8_6_1","first-page":"265","article-title":"Majority Vote\u2010Based Ensemble Approach for Distributed Denial of Service Attack Detection in Cloud Computing","volume":"11","author":"Alqarni A. 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