Innovative Cloud Security Solutions: Hybrid RNN and CNN Models for Intrusion Detection

Section: Research Paper
Issue
Vol. 21 No. 2 (2024): Volume 21 Issue 2
Published
Jun 25, 2025
Pages
24-35

Abstract

Cloud computing infrastructures have moved to the very heart of global business operations, which also places them in prime position for numerous advanced cyber threat challengers. This makes traditional predefined rule-based and known signature-based intrusion detection systems (IDS) almost useless in this era of advanced threats, including zero-day attacks, APTs - Advanced Persistent Threats exploiting polymorphic malware. The paper introduces a revolutionary hybrid model which uses the power of Recurrent Neural Networks (RNN) and Convolutional Neural Networks (CNN) to evolve cloud-based Intrusion detection system. This hybrid method uses the RNN to encode and learn from time-series data, acquiring memory over temporal anomalies in information; besides this it makes extensive use of Convolutional neural networks for spatial feature extraction at high-throughput which becomes essential for detecting these patterns across multitudes that suggest intrusions. In well-defined cloud setting, The overall effectiveness of this model is assessed by testing it under numerous attack scenarios. The results indicate that this model not only outperforms standard IDS in terms of detectio. but also demonstrates outstanding resilience against zero-day and emergent threats. This increased detection efficiency is obviously necessary to ensure the security and reliability of cloud services, allowing more stringent defense mechanisms which remains essential in modern dynamically evolving cyber threat landscapes

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Authors

ahmed akber mohamed

Azad University Tehran, Tehran, Iran

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akber mohamed, ahmed, & احمد. (2025). Innovative Cloud Security Solutions: Hybrid RNN and CNN Models for Intrusion Detection. IRAQI JOURNAL OF STATISTICAL SCIENCES, 21(2), 24–35. Retrieved from https://rjps.uomosul.edu.iq/index.php/stats/article/view/21013