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<p>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% multi-class detection accuracy, 98.67% precision, 97.93% recall, and 98.18% F1-score while maintaining low inference latency and high throughput suitable for real-time deployment. Additional evaluation on the SCADANet industrial cybersecurity dataset achieved 99.69% test accuracy, further validating the adaptability of the framework across heterogeneous cloud and industrial network environments. Throughput analysis demonstrated stable processing capability of approximately 48,000 packets per second and 12,500 flows per second under high traffic conditions. The findings confirm that the proposed IDSaaS framework provides an efficient, scalable, and adaptive solution for real-time cloud intrusion detection and significantly enhances the reliability and resilience of modern cloud and industrial cybersecurity infrastructures.</p>

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Adaptive intrusion detection system for cloud security using deep learning

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月20日

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  • Unik B. Lokhande,
  • Kavita Sonawane

摘要

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% multi-class detection accuracy, 98.67% precision, 97.93% recall, and 98.18% F1-score while maintaining low inference latency and high throughput suitable for real-time deployment. Additional evaluation on the SCADANet industrial cybersecurity dataset achieved 99.69% test accuracy, further validating the adaptability of the framework across heterogeneous cloud and industrial network environments. Throughput analysis demonstrated stable processing capability of approximately 48,000 packets per second and 12,500 flows per second under high traffic conditions. The findings confirm that the proposed IDSaaS framework provides an efficient, scalable, and adaptive solution for real-time cloud intrusion detection and significantly enhances the reliability and resilience of modern cloud and industrial cybersecurity infrastructures.