Several sectors have embraced Cloud Computing (CC) due to its inherent characteristics, such as scalability and flexibility. However, despite these advantages, security concerns remain a significant challenge for cloud providers. CC introduces new vulnerabilities, including unauthorized access, data breaches, and insider threats. The shared infrastructure of cloud systems makes them attractive targets for attackers. The integration of robust security mechanisms becomes crucial to address these security challenges. One such mechanism is an Intrusion Detection System (IDS), which is fundamental in safeguarding networks and cloud environments. An IDS monitors network traffic and system activities. In recent years, researchers have explored the use of Machine Learning (ML) and Deep Learning (DL) approaches to enhance the performance of IDS. ML and DL algorithms have demonstrated their ability to analyze large volumes of data and make accurate predictions. By leveraging these techniques, IDSs can adapt to evolving threats, detect previous attacks, and reduce false positives. This article proposes a novel IDS model based on DL algorithms like the Radial Basis Function Neural Network (RBFNN) and Random Forest (RF). The RF classifier is used for feature selection, and the RBFNN algorithm is used to detect intrusion in CC environments. Moreover, the datasets Bot-IoT and NSL-KDD have been utilized to validate our suggested approach. To evaluate the impact of our approach on an imbalanced dataset, we relied on Matthew’s Correlation Coefficient (MCC) as a normalized measure. Our method achieves accuracy (ACC) higher than 92% using the minimum features, and we managed to increase the MCC from 28% to 93%. The contributions of this study are twofold. Firstly, it presents a novel IDS model that leverages DL algorithms, demonstrating an improved ACC higher than 92% using minimal features and a substantial increase in MCC from 28% to 93%. Secondly, it addresses the security challenges specific to CC environments, offering a promising solution to enhance security in cloud systems. By integrating the proposed IDS model into cloud environments, cloud providers can benefit from enhanced security measures, effectively mitigating unauthorized access and potential data breaches. The utilization of DL algorithms, RBFNN, and RF has shown remarkable potential in detecting intrusions and strengthening the overall security posture of CC.

Cybersecurity has evolved significantly over the years, with a growing interest in biologically inspired models that emulate the immune system’s defense mechanisms. This paper provides a comparative analysis of various immunity-based approaches in cybersecurity, tracking their progression from their inception to the present. It explores the strengths and limitations of these methods across different cybersecurity areas, such as intrusion detection, malware analysis, and network protection. By reviewing foundational research, recent advancements, and existing challenges, this study aims to offer a well-rounded perspective on the effectiveness and constraints of immunity-driven strategies in protecting modern digital infrastructure. Additionally, it highlights emerging trends and future directions, stressing the importance of integrating these approaches with machine learning and other advanced technologies to strengthen cybersecurity resilience.