A REVIEW STUDY ON THE DEVELOPMENT OF LSTM-BASED INTRUSION DETECTION SYSTEM IN CLOUD COMPUTING ENVIRONMENTS
DOI:
https://doi.org/10.29121/shodhkosh.v7.i10s.2026.8147Keywords:
Intrusion Detection Systems, Long Short-Term Memory, Cloud Computing, Recurrent Neural Networks, Network Traffic, Virtualized EnvironmentsAbstract [English]
Cloud computing environments have become integral to modern IT infrastructure, offering scalable and flexible resources to users worldwide. Despite their advantages, cloud computing environments face significant challenges such as data security vulnerabilities and privacy concerns. Additionally, the complexity of managing dynamic, distributed resources increases the risk of cyberattacks and system breaches. The review of this study is to develop an LSTM-based intrusion detection system to enhance security in cloud computing environments. It aims to accurately detect and prevent cyber threats by analyzing network traffic patterns in real-time. This study examines the critical need for advanced intrusion detection systems (IDS) in cloud computing, focusing on the rising security threats and limitations of traditional IDS techniques. It highlights the advantages of Long Short-Term Memory (LSTM) networks for detecting sequential attack patterns and compares LSTM with other deep learning methods. The study examines various LSTM architectures, hybrid models, and feature engineering approaches used in IDS research, alongside key evaluation metrics. Publicly available datasets like NSL-KDD and CICIDS2017 are discussed, emphasizing challenges in data collection and benchmarking. Finally, the review outlines practical applications of LSTM-based IDS in real-time cloud environments, stressing their role in improving security across IaaS, PaaS, and SaaS platforms. Future research should focus on emerging more robust LSTM models that handle evolving cyber threats in cloud environments.
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