Evolving Threats and AI Solutions for Modern Hybrid Cloud Architectures
Chelimala Anjani, R M Balajee, Gottipati Divya, Yenduri Siva Sree, Keerthi Padmanabham, Sathy Srithar · 2023
In today’s world, with the enhancement of technology and an exponential growth in the economy security to resources has become a paramount concern in the coming days. Identity detection has played a significant role in safeguarding the digital Eco space and are a critical component of modern hybrid cloud. The traditional approaches often fall short in identifying the threats in hybrid cloud. As organizations prefer using hybrid cloud for a long-lasting flexibility and scalability, it is seen that there are some malicious activities that challenge the demand of innovation. This study proposes an enhanced intrusion detection approach in hybrid cloud that emphasizes the relationship between the Artificial Intelligence (AI) and cloud as a dynamic security paradigm. Further, the research focus is extended on machine learning and AI to create a dynamic normal behaviour of each workload. This baseline is continuously updated to keep a track of all the behaviour patterns, ensuring the accuracy is maintained Additionally, our approach with machine learning specifically deep learning to identify an effective intrusion detection system for hybrid clouds. Unlike traditional approaches where rule-based IDS are used, the proposed approach includes a deep neural network architecture that learns the malicious activity and analyses the changing patterns of traffic.