An Improved design for cloud ids using SMOTE Integration

This project presents an enhanced design for cloud intrusion detection systems (IDS) through the integration of Synthetic Minority Over-sampling Technique (SMOTE) to address class imbalance in network traffic data. The study utilizes a comprehensive dataset characterized by features including duration, protocol type, service, and various packet statistics, which collectively inform the detection capabilities of the IDS. Existing algorithms such as Logistic Regression (LR), Support Vector Machine (SVM), Decision Tree (DT), and Random Forest (RF) were evaluated for their effectiveness in classifying network intrusions. To further improve detection accuracy and robustness, we propose advanced algorithms: AdaBoost, Stacking Classifier, and Naive Bayes. These algorithms leverage the enriched dataset generated through SMOTE, which enhances the representation of minority classes in the data. Our experiments demonstrate that the proposed methods outperform traditional classifiers in terms of precision, recall, and F1-score, leading to a more reliable and effective cloud IDS framework. The findings suggest that integrating SMOTE with ensemble learning techniques significantly enhances intrusion detection capabilities, paving the way for more secure cloud environments. This research contributes to the field of cybersecurity by proposing an innovative approach that effectively mitigates the challenges posed by imbalanced datasets in intrusion detection.

Keywords: Cloud IDS, SMOTE, Class Imbalance, Network Traffic, Logistic Regression, Support Vector Machine, Random Forest, AdaBoost, Stacking Classifier, Cybersecurity.