Cybersecurity In Cloud Computing AI-Driven Intrusion Detection And Mitigation Strategies

Author(s)	Mr. GURU PRASATH S, Mrs. SHAMINI J, Mr. JEBA KUMAR S, Mr. MOHAMMED MUZZAMMIL R, Mr. RAGUPATHI V
Country	India
Abstract	The rapid adoption of cloud computing has introduced significant security challenges, making systems vulnerable to sophisticated cyber-attacks. Traditional intrusion detection systems (IDS) are insufficient in detecting unknown and evolving threats. This paper proposes an AI-driven intrusion detection framework using a hybrid Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) model. The CNN component extracts spatial features from network traffic, while LSTM captures temporal dependencies. The model is trained using the NSL-KDD dataset with optimized feature selection. Experimental results demonstrate high accuracy, improved detection rate, and reduced false positives. A Streamlit-based dashboard enables real-time monitoring and visualization. The proposed system provides a scalable and efficient solution for cloud security.
Keywords	Cloud Computing, Cybersecurity, Intrusion Detection System, Deep Learning, CNN, LSTM, NSL-KDD, Artificial Intelligence
Field	Engineering
Published In	Volume 8, Issue 3, May-June 2026
Published On	2026-05-07

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