Traffic Classification with Machine Learning for Enhancing Cloud Security

Cloud computing has revolutionized business operations by facilitating rapid access to numerous resources and services. With this convenience, however, comes the challenge of protecting the cloud environment from cyberattacks. Traditional security measures have proven insufficient to combat modern security threats as cloud traffic increases. The use of machine learning techniques to provide intelligent security solutions that detect and prevent cyber-attacks in real-time has shown great promise in addressing this issue. This paper investigates machine learning algorithms for improving cloud security via traffic classification. Traffic classification aims to determine the type of traffic traversing a cloud network, whether Bot or Not Bot. In order to analyze network traffic patterns, identify anomalies, and accurately classify traffic as standard or malicious, machine learning algorithms are trained. These techniques can assist cloud providers, and businesses detect and prevent cyberattacks in real-time, enhancing the cloud environment's security. In addition, the paper highlights some obstacles associated with deploying machine learning algorithms in the cloud. These obstacles include the need for vast quantities of labeled data, specialized hardware requirements, and the possibility of false positives and negatives. The paper discusses some strategies for overcoming these challenges and achieving adequate cloud security using machine learning techniques. The proposed method classifies a large amount of data using multiple ML algorithms and Blended Ensemble with an accuracy of . %.