International Journal of Emerging Research in Engineering, Science, and Management
Vol. 5, Issue 2, pp. 148-163, Apr-Jun 2026.
https://doi.org/10.58482/ijeresm.v5i2.8

Received: 14 Mar 2026 | Revised: 16 Jun 2026 | Accepted: 22 Jun 2026 | Published: 26 Jun 2026

This work is licensed under a Creative Commons Attribution 4.0 International License.

Intrusion Mitigation-Based Adaptive Resource Allocation Using LSTM-RBF-FFO in Cloud Environments

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1Mudavath Yuvaraj Naik

2C. Sivakumar

1Research Scholar, School of Computing, Department of CSE, Mohan Babu University, Tirupati, India.

2Associate Professor, School of Computing, Department of CSE, Mohan Babu University, Tirupati, India.

Abstract: Cloud computing systems are becoming increasingly dynamic, requiring effective security mechanisms and efficient resource allocation strategies. As cyber threats become more sophisticated, cloud systems must not only detect and mitigate intrusions but also adaptively allocate resources to maintain system performance. Existing optimization-based approaches, such as the Radial Basis Function Firefly (RBF-FF) algorithm, have shown promise in balancing resource utilization and security in cloud environments. However, static RBF models do not adapt effectively to evolving intrusion patterns and workload variations in real time, resulting in delayed threat response and suboptimal resource utilization. Existing approaches generally address intrusion detection and resource allocation independently, limiting their ability to respond effectively to dynamic cloud environments. This paper proposes a Hybrid LSTM-RBF Firefly Optimization (HLR-FFO) framework that integrates Long Short-Term Memory (LSTM) networks, Radial Basis Function (RBF) neural networks, and an adaptive Firefly Optimization algorithm. The LSTM component learns temporal patterns from system logs and network traffic to improve intrusion prediction, while the RBF layer performs rapid and accurate classification. The Firefly Optimization algorithm employs an adaptive brightness decay factor to dynamically allocate cloud resources based on real-time user demand, VM capacity, and detected risk levels. Simulation results obtained over 1000 optimization iterations indicate that HLR-FFO achieves a detection accuracy of 99.1%, a false positive rate of 0.09%, and a false negative rate of 0.17%, demonstrating improved performance compared with GRU-PSO, CNN-LSTM-GA, and RBF-FF under the considered simulation settings. The proposed framework achieves an internal processing latency of 18.7 ms while reducing end-to-end response latency to 103 ms, demonstrating its ability to provide faster response while maintaining effective security and resource utilization.

Keywords: Cloud security, LSTM-RBF hybrid model, Firefly optimization, Intrusion detection, Adaptive resource allocation.

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