Data Analytics Approaches for Effective Threat Identification in Cloud Databases
DOI:
https://doi.org/10.63282/3050-922X.IJERET-V7I2P101Keywords:
Cybersecurity, Cloud Computing, Internet of Things (IoT), Threat Detection, Distributed Security, Machine LearningAbstract
The threats are also becoming more sophisticated and multifaceted in the world as the technology advances, including the data breaching, insecure interfaces, shared technology vulnerability, and distributed attacks. One of the most pressing problems in today's IT infrastructure is ensuring the safety and dependability of cloud computing by detecting threats in server databases. This study constructs and evaluates intrusion detection models using the CICIDS2017 dataset, which contains both benign and harmful traffic. Extensive data preprocessing steps were used to improve the dataset's quality and address the problem of class imbalance. Data purification, normalisation, feature selection, and SMOTE data balancing were all part of these processes. Some common measures used for training and testing LSTM models include accuracy (ACC), precision (PRE), recall (REC), and F1-score (F1). In the best trial result, the model achieved an ACC of 98.51%, PRE of 99.0%, recall of 98.0%, and F1 of 99.0%, proving that it is trustworthy in differentiating between benign and malicious traffic. Training and validation curves demonstrate successful learning and high generalization, and there is not a great deal of overfitting. The analysis of the confusion matrices also confirms the high rates of detection of all types of attacks where most of them were over 95 percent correct. The LSTM has an obvious advantage when compared to traditional models (MLP, SVM, XGBoost, DeepGFL): it is more effective, as it can identify intricate temporal patterns in network traffic that cannot be identified by other models. Finally, to improve the efficacy of intrusion detection systems in the modern cloud-based environment, the proposed LSTM model provides a strong and efficient method for detecting threats in cloud databases.
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