AN AI-DRIVEN BLOCKCHAIN-BASED CYBERSECURITY FRAMEWORK FOR SECURE CLOUD COMPUTING ENVIRONMENTS

Abstract

Cloud computing has emerged as a foundational technology for modern digital infrastructure due to its scalability, flexibility, and cost-efficiency. However, the increasing adoption of cloud platforms has introduced significant cybersecurity challenges, including unauthorized access, data breaches, Distributed Denial-of-Service (DDoS) attacks, spoofing, insider threats, and data tampering. Traditional cloud security mechanisms suffer from centralized vulnerabilities, limited scalability, and inadequate real-time attack detection. To address these limitations, this paper proposes an AI-Driven Blockchain-Based Cybersecurity Framework (AIBCF) for secure cloud computing environments. The proposed framework integrates blockchain technology with a hybrid Convolutional Neural Network–Long Short-Term Memory (CNN-LSTM) model to provide decentralized trust management, intelligent intrusion detection, and adaptive threat mitigation. Blockchain ensures secure authentication, immutable transaction logging, and smart contract-based enforcement, while the CNN-LSTM model performs real-time cyberattack detection and classification. Experimental evaluation on the CICIDS2017 dataset under DDoS, spoofing, brute force, and infiltration scenarios achieved 98.2% accuracy, 97.6% precision, 97.1% recall, and 97.3% F1-score, with a false positive rate of 1.8%, outperforming existing machine learning and blockchain-based baselines. Ten-fold cross-validation confirmed stable results (accuracy: 98.2% ± 0.4%). The findings indicate that integrating blockchain with AI-driven mechanisms significantly improves cloud security, reliability, and adaptive defense capabilities.