AI-Enabled System for Efficient Cyber Incident Detection and Response in Cloud Environments: Safeguarding Against Systematic Attacks

Abstract

The growing trend of relying on cloud computing for data storage and enterprise applications has resulted in the drastic growth of cyber threats targeting cloud environments. Attacks are also highly evasive, which makes it hard for traditional rulebased security systems to detect them, resulting in high false positive rates and longer response times. In this regard, this study presents an AI empowered cyber incident detection and response system capable of improving threat reconnaissance, attack mitigation, and automated incident handling in the context of cloud infrastructures.Our proposed framework employs machine learning (ML), deep learning (DL), and realtime anomaly detection models to identify and respond to cyber incidents in cloud environments. Using hybrid AI, the system constantly analyzes network traffic, access logs and behavioral patterns to identify advanced threats such as zeroday attacks, insider threats and largescale distributed denialofservice (DDoS) attacks. For response mechanism, it uses reinforcement learning based adaptive security policies to autonomously contain and mitigate the cyber incidents based on minimal human intervention.Performance evaluation conducted using real datascapes in the murky realm of cloud security highlighting that the proposed mechanism handles prevalent cyber incidents with 96% accuracy and reduces false positives by 30% and improves incident responses time up to 50% against existing SIEM systems.