Supervised learning for Attack Detection in Cloud
Abstract
In this study, we approach a supervised learning algorithm to detect attacks in cloud computing. We categorize “Normal” and “Attack” statuses on the dataset. The model evaluation process uses the kappa statistic, the F1-score, recall, accuracy, and precision. The system has a very high detection and efficiency rate, with a detection rate of over 99%. A total of 9594 cases and 44 distinct columns are included in the dataset. The study's results were displayed using a ROC curve and a confusion matrix. This study focuses on implementing a supervised learning algorithm for detecting attacks in cloud computing environments. The main objective is distinguishing between "Normal" and "Attack" statuses based on a carefully curated dataset. Several metrics, such as the kappa statistic, F1-score, recall, accuracy, and precision, are employed to evaluate the model's performance. The dataset utilized in this research comprises 9594 cases and encompasses 44 distinct columns, each representing specific features relevant to cloud computing security. Through a rigorous evaluation process, the algorithm demonstrates exceptional efficiency, achieving a remarkable detection rate of over 99%. Such high accuracy in identifying attacks is crucial for ensuring the integrity and security of cloud-based systems. The significance of this study lies in its successful application of a supervised learning approach to tackle cloud computing security challenges effectively. The model's high detection rate and efficiency indicate its potential for real-world deployment in cloud-based systems, contributing to enhanced threat detection and mitigation. These results hold promising implications for bolstering the security measures of cloud computing platforms and safeguarding sensitive data and services from potential attacks.
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