A novel intrusion detection system in cloud infrastructure using deep learning technique

Abstract

One of the business strategies for selling computer resources with services and technology for better use of computing infrastructures is Cloud computing (CC). Nowadays, every IT company prefers cloud computing because it provides consumers with flexible, pay-per-use services. Due to its open and distributed structure, which is susceptible to attackers, thereby, privacy and security is a key obstacle to its sustainability. The most prevalent approach for detecting assaults on the cloud is known to be Intrusion Detection System (IDS). This article aims to propose a novel intrusion pattern detection system (IPDS) in cloud computing that includes three stages: (1) pre-processing, (2) feature extraction, and (3) classification. At first, pre-processing is performed on the input data via Z-score normalization and then feature extraction is performed along with statistical and higher-order statistical features. Subsequently, the extracted features are given to the classification phases that use the Optimized Quantum Neural Network (QNN) classifier. The hidden neuron optimization is performed by Cubic Chaotic Map integrated Cat and Mouse Based Optimization (CC-CMBO) Algorithm to make the classification more exact. Finally, the results of the proposed work are assessed to those of standard systems with respect to various measures.