Automatic intrusion detection model with secure data storage on cloud using adaptive cyclic shift transposition with enhanced ANFIS classifier

Abstract

Cloud computing has emerged as a pivotal technology in the computer electronics industry, offering users significant computing power and ample storage space. Security threats pose significant challenges to the progression of cloud computing, hindering its growth in the industry. Detecting intrusions is crucial for protecting cloud environments from harmful attacks. However, due to the complexity and vast amount of network data, building effective intrusion detection systems (IDS) for cloud setups is difficult. Traditional IDS have struggled to effectively mitigate these risks. To overcome these problems, we propose a novel feature selection technique with deep learning classifier-based intrusion detection and avoidance in a cloud environment. The suggested model is divided into four phases: feature selection, pre-processing, classification, and encryption. The initial step involves gathering the data from the dataset and pre-processing it. The Adaptive Walrus Optimization Algorithm (AWO) is then used to choose select optimal features, aiming to mitigate computational complexity and reduce time consumption. These selected features are then fed into an enhanced Adaptive Neuro-Fuzzy Inference System (EANFIS) classifier for accurate classification of normal and intruded data. Following classification, normal data undergoes encryption using the Adaptive Cyclic Shift Transposition (ACST) Algorithm to bolster security.For experimental evaluation two datasets used namely, KDDCup-99 and NSL-KDD. The proposed method notably achieves impressive accuracy rates of 98.47 % for the NSL KDD dataset and 98.97 % for the KDD-CUP99 dataset.