TY  - JOUR
AU  - Haider, Zeeshan Ali
AU  - Zeb, Asim
AU  - Rahman, Taj
AU  - Khan, Fida Muhammad
AU  - Khan, Inam Ullah
AU  - Sohail, Qaisar
AU  - Bilal, Hazrat
AU  - Khan, Muhammad Abbas
AU  - Ullah, Inam
PY  - 2025
DA  - 2025/05/19
TI  - Optimizing Cloud Security with a Hybrid BiLSTM-BiGRU Model for Efficient Intrusion Detection
JO  - ICCK Transactions on Sensing, Communication, and Control
T2  - ICCK Transactions on Sensing, Communication, and Control
JF  - ICCK Transactions on Sensing, Communication, and Control
VL  - 2
IS  - 2
SP  - 106
EP  - 121
DO  - 10.62762/TSCC.2024.433246
UR  - https://www.icck.org/article/abs/TSCC.2024.433246
KW  - cloud security
KW  - network intrusion detection
KW  - deep learning
KW  - BiLSTM-BiGRU
KW  - hybrid models
KW  - cybersecurity
KW  - cloud computing
AB  - To address evolving security challenges in cloud computing, this study proposes a hybrid deep learning architecture integrating Bidirectional Long Short-Term Memory (BiLSTM) and Bidirectional Gated Recurrent Units (BiGRU) for cloud intrusion detection. The BiLSTM-BiGRU model synergizes BiLSTM's long-term dependency modeling with BiGRU's efficient gating mechanisms, achieving a detection accuracy of 96.7% on the CIC-IDS 2018 dataset. It outperforms CNN-LSTM baselines by 2.2% accuracy, 3.3% precision, 3.6% recall, and 3.6% F1-score, demonstrating consistently superior performance across all evaluation metrics. The architecture demonstrates operational efficiency through 20% reduced computational latency and 15% lower memory footprint compared to conventional models, enabled by residual memory preservation and parallel processing capabilities. Experimental results further validate strong detection capability across diverse attack categories, including DDoS (97.3%), DoS (98.0%), and brute-force attacks (96.1%), establishing a methodological framework for real-time cloud security services. This work advances hybrid deep learning applications in trusted computing environments through optimized temporal feature extraction and resource-aware threat detection.
SN  - 3068-9287
PB  - Institute of Central Computation and Knowledge
LA  - English
ER  - 

@article{Haider2025Optimizing,
  author = {Zeeshan Ali Haider and Asim Zeb and Taj Rahman and Fida Muhammad Khan and Inam Ullah Khan and Qaisar Sohail and Hazrat Bilal and Muhammad Abbas Khan and Inam Ullah},
  title = {Optimizing Cloud Security with a Hybrid BiLSTM-BiGRU Model for Efficient Intrusion Detection},
  journal = {ICCK Transactions on Sensing, Communication, and Control},
  year = {2025},
  volume = {2},
  number = {2},
  pages = {106-121},
  doi = {10.62762/TSCC.2024.433246},
  url = {https://www.icck.org/article/abs/TSCC.2024.433246},
  abstract = {To address evolving security challenges in cloud computing, this study proposes a hybrid deep learning architecture integrating Bidirectional Long Short-Term Memory (BiLSTM) and Bidirectional Gated Recurrent Units (BiGRU) for cloud intrusion detection. The BiLSTM-BiGRU model synergizes BiLSTM's long-term dependency modeling with BiGRU's efficient gating mechanisms, achieving a detection accuracy of 96.7\% on the CIC-IDS 2018 dataset. It outperforms CNN-LSTM baselines by 2.2\% accuracy, 3.3\% precision, 3.6\% recall, and 3.6\% F1-score, demonstrating consistently superior performance across all evaluation metrics. The architecture demonstrates operational efficiency through 20\% reduced computational latency and 15\% lower memory footprint compared to conventional models, enabled by residual memory preservation and parallel processing capabilities. Experimental results further validate strong detection capability across diverse attack categories, including DDoS (97.3\%), DoS (98.0\%), and brute-force attacks (96.1\%), establishing a methodological framework for real-time cloud security services. This work advances hybrid deep learning applications in trusted computing environments through optimized temporal feature extraction and resource-aware threat detection.},
  keywords = {cloud security, network intrusion detection, deep learning, BiLSTM-BiGRU, hybrid models, cybersecurity, cloud computing},
  issn = {3068-9287},
  publisher = {Institute of Central Computation and Knowledge}
}