To Study Traffic Behaviors for Detecting Unusual Activities by Analyzing HTTP Packets

Cloud computing refers to the on-demand resource utilization, storage capability, and networking solutions over the Internet. Although, cloud computing is one of the critical mechanisms that provide the online storage solution with a subscription to the cloud environment. However, there are several security, privacy, and data breach issues with cloud computing. Network intrusion detection systems (NIDS) play a vital role in identifying and mitigating potential security threats. Traditional NIDS are not sufficient for complex and dynamic cloud problems, particularly with the widespread use of Hypertext Transfer Protocol (HTTP) as a primary communication protocol. This article proposes a behavioral intrusion detection system based on machine learning techniques to identify attacks by analyzing the HTTP traffic. The system is designed to monitor and analyze network traffic, specifically focusing on HTTP-based communications, to detect and prevent various types of network intrusions. The proposed NIDS employs a combination of supervised and unsupervised machine learning algorithms to handle the diverse nature of cloud-based network traffic. To enhance the accuracy and efficiency of the NIDS, feature engineering techniques are applied to extract relevant features from HTTP network traffic, including packet payload analysis, header inspection, and traffic flow characteristics. The results demonstrate that the system achieves high detection accuracy while maintaining low false-positive rates, showcasing its potential as a reliable and efficient solution for securing cloud-based networks.