{"title":"Malicious behavior identification using Dual Attention Based dense bi-directional gated recurrent network in the cloud computing environment","authors":"Nandita Goyal , Kanika Taneja , Shivani Agarwal , Harsh Khatter","doi":"10.1016/j.cose.2025.104418","DOIUrl":null,"url":null,"abstract":"<div><div>The rapid expansion of novel computing technologies has driven organizations to collaborate through cloud-based platforms, making robust security frameworks to ensure integrity, security, and accessibility. This paper proposes a deep learning approach to enhance malicious behaviour detection in cloud environments. Initially, the input data undergoes pre-processing using Min-Max Normalization, Missing Value Imputation, and Data Reduction to eliminate noise and inconsistencies. Feature selection is performed using the Improved Cheetah Optimization (ICO) algorithm. Finally, a Dual Attention-Based Dense Bi-Directional Gated Recurrent Unit (DA-Dense-BiGRU) is then employed to detect and classify malicious activity. The proposed approach is evaluated on five distinct datasets, achieving good accuracy rates of 99.35 %, 99.5 %, 99.4 %, 99.2 %, and 98.8 %. These results indicate the model's ability to detect harmful activities and improve security monitoring in cloud computing environments.</div></div>","PeriodicalId":51004,"journal":{"name":"Computers & Security","volume":"154 ","pages":"Article 104418"},"PeriodicalIF":4.8000,"publicationDate":"2025-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Computers & Security","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0167404825001075","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}
引用次数: 0
Abstract
The rapid expansion of novel computing technologies has driven organizations to collaborate through cloud-based platforms, making robust security frameworks to ensure integrity, security, and accessibility. This paper proposes a deep learning approach to enhance malicious behaviour detection in cloud environments. Initially, the input data undergoes pre-processing using Min-Max Normalization, Missing Value Imputation, and Data Reduction to eliminate noise and inconsistencies. Feature selection is performed using the Improved Cheetah Optimization (ICO) algorithm. Finally, a Dual Attention-Based Dense Bi-Directional Gated Recurrent Unit (DA-Dense-BiGRU) is then employed to detect and classify malicious activity. The proposed approach is evaluated on five distinct datasets, achieving good accuracy rates of 99.35 %, 99.5 %, 99.4 %, 99.2 %, and 98.8 %. These results indicate the model's ability to detect harmful activities and improve security monitoring in cloud computing environments.
期刊介绍:
Computers & Security is the most respected technical journal in the IT security field. With its high-profile editorial board and informative regular features and columns, the journal is essential reading for IT security professionals around the world.
Computers & Security provides you with a unique blend of leading edge research and sound practical management advice. It is aimed at the professional involved with computer security, audit, control and data integrity in all sectors - industry, commerce and academia. Recognized worldwide as THE primary source of reference for applied research and technical expertise it is your first step to fully secure systems.