Intelligent Threat Detection in Cloud Environments: Integrating Machine Learning with Blockchain for Enhanced Digital Security

Abstract

This paper presents an intelligent threat detection framework for cloud environments by integrating machine learning techniques with blockchain technology to enhance digital security and data integrity. With the rapid adoption of cloud computing, enterprises and users face major concerns from security threats and data breaches. Traditional security mechanisms often fail to provide real-time, tamper-proof threat detection in dynamic cloud environments. Several studies use machine learning models for intrusion detection; however, they face data manipulation risks and centralized vulnerabilities. Blockchain solutions enhance data integrity but lack intelligent threat analysis when used independently. The system combines supervised machine learning algorithms for threat classification with a blockchain layer to ensure secure, immutable logging of security events. Accurate detection is enabled by this hybrid approach while transparency and trust are maintained across cloud nodes. It is demonstrated by experimental evaluation that higher detection accuracy, precision, and recall are achieved by the proposed model compared to conventional methods. Additionally, minimal computational overhead is shown by the integration of blockchain while data security is significantly improved.