A hybrid metaheuristic federated learning approach based attack detection system for multi-cloud environment

Multi-cloud strategy allows distribution of workloads across multiple cloud platforms. These cloud platforms that are served include, public, private, as well as hybrid clouds. This gives organizations more flexibility than they’d have, if they depended on a single cloud vendor and in turn, it helps them optimise costs, dodge vendor lock in and reinforce operational resilience. As a result, while multi-cloud deployments have their inherent complexity, this increases risk of attack surface and it presents new and evolving cloud security challenges. A holistic security approach is needed for multi-cloud security that addresses diverse security vulnerabilities in multi-cloud enterprise world and enforces security across heterogeneous platforms in a consistent way. In this work we present a Hybrid Metaheuristic Federated Learning Approach Based Attack Detection System (HMFLA-ADS) for multi-Cloud scenarios. The use of Federated Learning (FL) in the HMFLA-ADS model makes it possible to collaborative train across various cloud platforms while retaining data privacy and safe. The HMFLA-ADS technique achieves better attack detection by aggregating knowledge from locally trained models at a Cloud Exchange Point (a federated node deployed in each cloud region) without requiring data sharing in the centralized manner. A deep Stacked Sparse Autoencoder (SSAE) for attack detection and a hybride Fish School Search (FSS) based strategy for hyperparameter tuning are used in the study. Experimental validation of the HMFLA ADS framework is carried out on NSL-KDD, CICIDS-2017 and BoT-IoT Datasets and results are measured through various evaluation metrics. The findings show that HMFLA-ADS framework has better performance than existing IDS models, obtaining maximum detection accuracy up to 99.52%.