DEEP LEARNING-BASED INTRUSION DETECTION SYSTEMS FOR CLOUD SECURITY
Abstract
With the rise of extensive use of cloud computing, the requirement of good cybersecurity is becoming indispensable. Most traditional intrusion detection systems (IDS) suffer from feature engineering that is based on manual features and high false positive rates. An alternative way is deep learning, which automates the detection of threats by learning advanced features. The basic idea of this study is a hybrid deep learning-based IDS that combines Convolutional Neural Networks (CNNs) for spatial feature extraction and Long Short-Term Memory (LSTM) networks for sequential dependency analysis. The model is designed to improve detection accuracy, reduce false positives, and make real-time feasibility in the cloud security environment. A complete experiment exam was conducted that compares the performance of the proposed CNN- LSTM hybrid model to traditional ML models (SVM, RF) and deep learning models (CNN, LSTM). The key metrics, including accuracy, precision, recall, F1-score, computational time, and false alarm rate (FAR), were used to assess performance. The performance (i.e., 96.4% accuracy, 96.1% recall, 3.6% false alarm rate) of the proposed model is better than that for conventional methods. In addition, it was found to be feasible in cloud environments with a processing time of 5.9s. By examining a significant set of experiments, the results establish that hybrid deep learning architectures are more effective than deep learning and support-based systems for cloud intrusion detection. To increase its applicability to the real world, future work will be to explore adversarial robustness and explainability.
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