Malicious behavior identification using Dual Attention Based dense bi-directional gated recurrent network in the cloud computing environment

IF 4.8 2区 计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS
Nandita Goyal , Kanika Taneja , Shivani Agarwal , Harsh Khatter
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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.
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来源期刊
Computers & Security
Computers & Security 工程技术-计算机:信息系统
CiteScore
12.40
自引率
7.10%
发文量
365
审稿时长
10.7 months
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.
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