Enhancing Advanced Persistent Threat Detection with Federated Learning and Neural Networks for Secure Cloud Computer Environment

Rapid internet expansion and global cloud storage use have heightened the risk of stealthy, persistent, multi-stage Advanced Persistent Threats (APTs).  Distributed and resource-limited cloud environments make identifying these stealthy and dynamic threats difficult for traditional Intrusion Detection Systems (IDSs).  This paper present FedNN-APT, a Federated Learning (FL) and hybrid Neural Network (NN) APT detection system to overcome these difficulties.  High detection accuracy and distributed, privacy-preserving training over several cloud devices are achieved by this approach. FedNN-APT integrates Gated Recurrent Units (GRUs) and Convolutional Neural Networks (CNNs) to learn temporal and spatial APT behavior features effectively. The framework trains local models on partitioned datasets using GRU-CNN, 1D-CNN, and GRU-Recurrent Neural Network (RNN) models, then selects the optimal model for federated aggregation. The final global model is collaboratively built while preserving data confidentiality. The system is evaluated using an APT Malware dataset consisting of 11,107 samples.  Experimental findings reveal that the hybrid GRU-CNN model outperforms other models with an average