A BLOCKCHAIN ASSISTED ADAPTIVE BOOSTING GRAPH LSTM FRAMEWORK WITH FIREFLY OPTIMIZATION FOR ROBUST MALICIOUS ACTIVITY PREDICTION IN CLOUD ENVIRONMENTS

Cloud infrastructures with their unmatched scalability and flexibility are becoming the target of advanced malicious operations, which carry serious security threats to confidential data and critical services.
Conventional types of detection techniques usually fight with a dynamic, voluminous, and complicated nature of the cloud-based threats that cause a high false alarm percentage or failure to identify. In this paper, a new communication system called Blockchain-Assisted Graph-LSTM Framework with Attention and Firefly Optimization (BAG-LSTMAFO) is suggested to ensure a robust malicious activity-detecting system in cloud environments. The framework uses blockchain technology to provide tamper-proof recording of activities within the system and exchange threat intelligence to increase the integrity and auditability of data.
Graph Long Short-Term Memory (G-LSTM) networks are a type of network that models the intricate spatiotemporal interrelations of cloud system interactions, which are modelled as dynamic graphs. To enhance the interpretability and accuracy of detection an attention mechanism is incorporated to enable the model to concentrate on the most salient features and time steps that may be indicative of malicious behaviour. Moreover, Firefly Optimization is applied to optimize the hyperparameters of G-LSTM model
automatically which guarantees optimal performance and generalization. The synergistic solution will aim to
attain a high detection rate, lower false positives, and offer a flexible and adaptive defence system to changing
cyber threats in the complex cloud infrastructures.