Adaptive Intrusion Detection System for Cloud Security Using Deep Learning

Cloud computing environments are increasingly vulnerable to sophisticated cyber threats due to the rapid growth of cloud-native infrastructures, high-volume network traffic, and evolving attack strategies such as zero-day exploits, adversarial attacks, and polymorphic malware. Traditional intrusion detection systems often struggle to maintain high detection accuracy, scalability, adversarial robustness, and real-time processing capability in dynamic cloud environments. To address these challenges, this study proposes an adaptive Intrusion Detection System-as-a-Service (IDSaaS) framework using a Modified Attention-Gate based BiLSTM-GRU architecture integrated with GAN-assisted synthetic anomaly generation. The proposed framework combines Bidirectional Long Short-Term Memory networks for temporal dependency learning, Gated Recurrent Units for computational efficiency, a custom attention mechanism for dynamic feature prioritization, a modified recurring gate structure for optimized information retention, and a Revamping Input Sequence Weighing Structure (RISWS) loss function for improved anomaly classification performance. Experiments were conducted using the CICIDS-2018, UNSW-NB15, SCAPY-based, and SCADANet datasets within an AWS cloud deployment environment. The proposed model achieved strong intrusion detection performance with 97.91