Enhancing the Security and Integrity of Intrusion Detection Systems based on CNN and LSTM Approach for Cloud Computing

As cloud computing environments become increasingly prevalent, ensuring the security and integrity of these systems is paramount. Traditional intrusion detection systems (IDS) often fall short in addressing the dynamic and complex nature of cloud infrastructures. This study presents a enhanced security and integrity of Deep Learning (DL) approach for intrusion detection in cloud computing environments, leveraging Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks. By incorporating an attention mechanism into the deep learning framework, the suggested system is able to recognize temporal patterns that point to intrusions and efficiently record spatial characteristics. Comprehensive tests are carried out on standard cloud computing datasets, proving that the system performs better than traditional techniques in identifying a broad variety of intrusion types. The findings point to the possibility of using deep learning methods to greatly enhance the responsiveness and resilience of IDS in cloud environments, opening up opportunities for more reliable and secure cloud computing services.