Enhancing Cloud Security Through AI‐Driven Intrusion Detection Utilizing Deep Learning Methods and Autoencoder Technology
P. V. Sivarambabu, Richa Agrawal, Arepalli Tirumala, Shaik Mahaboob Subani, Veeraswamy Parisae, Saihemanth Nukala · 2025
As cloud environments become increasingly prevalent, securing sensitive information from security attacks has become a critical challenge. Intrusion detection systems (IDS) play a crucial role in safeguarding cloud networks and preserving data integrity. Traditional signature-based detection approaches have limitations in handling emerging attack variants, necessitating the adoption of deep learning techniques (DLT) for more robust cloud security. In this article, we propose an AI-based IDS using DLT, specifically an autoencoder (AE) as a feature extractor, an autoencoder is a type of artificial intelligence (AI) system. Cloud traffic attributes are transformed into essential features by the AE, capturing relevant patterns and behaviors. The features are subsequently categorized using three binary classifiers: convolutional neural network (CNN), recurrent neural network (RNN), and long short-term memory (LSTM). The evaluation is conducted on the CICIDS 2018 dataset, an actual real-time cloud attack dataset, to assess the performance of the suggested approach in comparison to established methods. The outcomes reveal the effectiveness of the proposed methodology, attaining accuracy rates of 98.55%, 94.33%, and 95.08% for AE coupled with CNN, RNN, and LSTM, respectively. These results outperform existing intrusion detection methods, highlighting the efficacy of DLT in securing cloud environments. This article underscores the significance of adopting DLT-based intrusion detection for cloud security. The proposed behavior-based approach provides enhanced protection against security threats, offering a valuable solution for organizations relying on cloud infrastructures. The findings emphasize the potential of DLT in advancing cloud security, adapting to evolving attack challenges, and maintaining data confidentiality in cloud environments.