Securing Cloud Environments: A Convolutional Neural Network (CNN) approach to Intrusion Detection System

Abstract

Cloud-computing has become an essential portion of recent IT structure, contribution scalable resources and on-demand services to users. However, the increasing reliance on cloud environments has raised concerns about security, especially with the rise of sophisticated cyber threats. Intrusion detection systems (IDS) play a crucial role in detecting and mitigating possible security breaches. In this studies proposes a approach to enhance intrusion detection in cloud computing through the CNN. This deep learning architecture adapted for the unique challenges in cloud computing security. Unlike traditional IDS methods that rely on rule-based or signature-based approaches, the CNN-based intrusion detection system presented in this research leverages the network's capability to automatically learn hierarchical features from raw data. This study is involves the collection of diverse and representative datasets from cloud environments, including normal network traffic and various types of attacks. The CNN is trained on these datasets to learn the inherent patterns of legitimate activities and deviations indicative of potential intrusions. The proposed system demonstrates its adaptability to evolving threats by continuously updating its knowledge through regular retraining with new data. The evaluation of the CNN-based intrusion detection system is conducted through comprehensive experiments, comparing its performance against traditional methods. The results indicate that the CNN-based approach outperforms conventional IDS techniques, demonstrating its potential as a robust and efficient solution for intrusion detection in cloud computing environments.