ANOMALIES INTRUSION DETECTION IN CLOUD COMPUTING USING MACHINE LEARNING

Abstract

Abstract: With the rapid adoption of cloud computing, ensuring the security of sensitive data and resources has become a major concern. Traditional security methods often fall short in the dynamic and distributed nature of cloud environments. This paper presents an approach for detecting anomalies and intrusions in cloud computing using machine learning techniques. By using CSE-CIC IDS 2018 Dataset the system is able to identify abnormal behaviors and malicious activities that may indicate security threats. The proposed method analyzes cloud traffic, system logs, and user behavior to uncover potential intrusions, with the goal of enhancing the overall security posture of cloud systems. Experimental results show that machine learning-based anomaly detection offers high detection accuracy and low false positive rates, making it an effective tool for proactive security in cloud environments.