Cloud Security Redefined: Intrusion Detection System Powered by Deep Learning
Keywords:
Cloud Computing, Deep Learning, Intrusion Detection Systems, Jarratt-Butterfly Optimization, Long Short-Term Memory, Morlet Wavelet Kernel.Abstract
Cloud Computing (CC) provides unparalleled scalability and adaptability, making it integral across industries. However, these benefits come with significant security challenges, such as unauthorized access, data breaches, and insider threats. The shared infrastructure of cloud systems is especially attractive to malicious actors. Addressing these vulnerabilities necessitates robust security mechanisms, with Intrusion Detection Systems (IDS) being a key solution. IDS monitor network and system activities to detect potential intrusions. The integration of machine learning (ML) and deep learning (DL) has enabled IDS to adapt to emerging threats while minimizing false alarms. This study proposes an innovative IDS model incorporating the Morlet Wavelet Kernel Function and an MLSTM (Modified Long Short-Term Memory) classifier. The Jarratt-Butterfly Optimization Algorithm (JBOA) is utilized for feature selection to enhance classification accuracy. Tested on the comprehensive BoT-IoT dataset, the model demonstrates superior performance compared to existing techniques.
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