Published Paper Details:

In today's interconnected world, ensuring the security of cloud-based systems is paramount. Traditional intrusion detection systems (IDS) often struggle to keep pace with the evolving landscape of cyber threats. This project proposes a novel approach to enhancing cloud security through the integration of machine learning techniques into intrusion detection. By harnessing the power of machine learning algorithms, our system can adapt and learn from patterns in network traffic data, enabling it to detect anomalous behavior indicative of potential intrusions. Leveraging the scalability and flexibility of cloud computing, our solution offers real-time monitoring and analysis of network traffic across distributed cloud environments. Key features of our cloud-based intrusion detection system include automated threat detection, rapid response mechanisms, and customizable alerting capabilities. Through continuous learning and refinement, the system improves its detection accuracy over time, bolstering the resilience of cloud infra structures against emerging cyber threats. In summary, our project presents a proactive and adaptive approach to safeguarding cloud-based systems, combining the strengths of machine learning and cloud computing to create a robust Defense mechanism against intrusions. Protecting cloud-based systems from unauthorized access, data breaches, and other malicious activities is a critical concern for organizations worldwide. Traditional intrusion detection systems (IDS) often fall short in effectively identifying and mitigating these threats in dynamic cloud environments .To address this gap, our project focuses on leveraging the Random Forest algorithm to enhance cloud-based intrusion detection capabilities. High Accuracy Random Forest excels in handling complex, high- dimensional data and can effectively distinguish between normal and malicious network activities with high accuracy.