ENHANCING CLOUD SECURITY INCIDENT RESPONSE WITH ADAPTIVE MACHINE LEARNING PROACTIVE AND REACTIVE CYBERSECURITY SOLUTIONS

Abstract

The rapid adoption of cloud computing has amplified security concerns, with incidents like data breaches and distributed denial-of-service (DDoS) attacks threatening system integrity and availability. This article proposes a comprehensive framework for improving security incident response in cloud systems using adaptive machine learning (ML) models. Motivated by the limitations of traditional reactive security measures, which often fail to address sophisticated threats in real-time, the research aims to integrate proactive threat prediction and reactive incident mitigation. The methodology employs a hybrid approach, combining supervised ML (Random Forest, SVM) for threat classification and unsupervised ML (Autoencoders) for anomaly detection, validated through experiments on the AWS cloud platform. Key achievements include a 92% accuracy in threat detection and a 40% reduction in incident response time compared to baseline methods. Limitations include high computational costs for real-time ML processing and challenges in handling encrypted traffic. This framework offers a scalable, adaptive solution for robust cloud security.

The graphical abstract illustrates the proposed framework for cloud security incident response. A central cloud icon represents the cloud system, connected to nodes symbolizing data centers, virtual machines, and user endpoints. A layered structure depicts ML models: a green layer for proactive threat prediction and a blue layer for reactive incident response. Red arrows indicate attack vectors (e.g., DDoS, malware), countered by a shield icon symbolizing ML-driven defense. A line graph at the bottom showcases reduced response times, while a pie chart highlights detection accuracy. The design uses blue for cloud infrastructure, green for proactive measures, and red for threats, ensuring visual clarity.