A Deep Learning and Optimization-Aided Intrusion Detection Framework for Adaptive Threat Detection in Dynamic Public Cloud Environments
Keywords:
Intrusion Detection Systems, Cloud Computing, Deep Learning, Deep Belief Networks, Cat Swarm Optimization, Long Short-Term Memory, Network Security, Cyber ThreatsAbstract
The article proposes a novel way to combine cutting-edge Deep Learning (DL) algorithms and optimization strategies to improve the effectiveness of Intrusion Detection Systems (IDS) in cloud environments. The proposed approach aims to address common issues including overfitting, imbalanced data, and the identification of unknown attack types by combining Deep Belief Networks (DBN) with Cat Swarm Optimization assisted Long Short-Term Memory (CSO-LSTM). Utilizing DBN for feature extraction lowers dimensionality, allowing for a more thorough examination of network data, and LSTM takes care of temporal factors that are essential for identifying multi-stage assaults. System performance is improved with LSTM weight optimization using Cat Swarm Optimization. The DBN architecture and the energy dependence model provide a strong basis for identifying latent patterns in the NSL-KDD dataset. This research tries to improve the adaptability and accuracy of intrusion detection systems (IDS) by integrating deep learning (DL) and optimization approaches in a comprehensive manner, in addition to addressing the current limits in IDS for cloud environments. The findings show encouraging gains in precision and flexibility, indicating the possibility for the suggested framework to overcome current intrusion detection for cloud environments constraints.
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