Supervised learning for Attack Detection in Cloud
Journal Title: International Journal of Experimental Research and Review - Year 2023, Vol 31, Issue 2
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.
Authors and Affiliations
Animesh Kumar, Sandip Dutta, Prashant Pranav
Keywords
Related Articles
- EP ID EP719465
- DOI https://doi.org/10.52756/10.52756/ijerr.2023.v31spl.008
- Views 82
- Downloads 0
Trend of urbanization in Chakdaha urban area in preceding few decades
Urbanization has not been uniform all over the world. Different ages have witnessed localized growth. The dawn of modern age saw the spread of urbanization to almost all the parts of the world. With the spread of globali...
Machine Learning-based maternal health risk prediction model for IoMT framework
The Internet of Things (IoT) is vital as it offers extensive applicability in various fields, including healthcare. In the context of the risk level during pregnancy, to monitor and predict abnormalities, IoT devices pro...
Phytochemical analysis of groundnut (Arachis hypogea L.)
Groundnut is among the major world oil seed crops which are the rich source of edible oils and provide a more valuable source of fat, protein, carbohydrate, minerals and some vitamins than any other nut, yet they are oft...
A review on phyto-remediation by aquatic macrophytes: A natural promising tool for sustainable management of ecosystem
Heavy metal pollution is a significant source of pollution in the environment. Heavy metal contamination in aquifers endangers public health and the freshwater and marine ecosystems. Traditional wastewater treatment met...
Water bird diversity in Winter and Summer season of Motijheel lake, Murshidabad, West Bengal, India
Wetlands are the most preferred habitats for water birds. They support the birds by way of feeding, roosting, nesting and rearing young ones. Birds are essential animal group of an ecosystem and maintain a trophic level....