# AI-Enabled System for Efficient and Effective Cyber Incident Detection and Response in Cloud Environments

arXiv: https://arxiv.org/abs/2404.05602 · HTML: https://mlsanity.com/paper/2404.05602/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Apr 2024.

## Built on by (most cited first, 7 of 7)

- [Advanced Persistent Threats (APT) Attribution Using Deep Reinforcement Learning](https://mlsanity.com/paper/2410.11463/) (2024)
- [Reinforcement Learning for an Efficient and Effective Malware Investigation during Cyber Incident Response](https://mlsanity.com/paper/2408.01999/) (2024)
- [A Robust Cross-Domain IDS using BiGRU-LSTM-Attention for Medical and Industrial IoT Security](https://mlsanity.com/paper/2508.12470/) (2025)
- [A Novel Reinforcement Learning Model for Post-Incident Malware Investigations](https://mlsanity.com/paper/2410.15028/) (2024)
- [MalVol-25: A Diverse, Labelled and Detailed Volatile Memory Dataset for Malware Detection and Response Testing and Validation](https://mlsanity.com/paper/2507.03993/) (2025)
- [Hierarchical Graph Neural Network for Compressed Speech Steganalysis](https://mlsanity.com/paper/2507.21591/) (2025)
- [A Relay-Chain-Powered Ciphertext-Policy Attribute-Based Encryption in Intelligent Transportation Systems](https://mlsanity.com/paper/2508.16189/) (2025)

## Abstract (arXiv)

The escalating sophistication and volume of cyber threats in cloud environments necessitate a paradigm shift in strategies. Recognising the need for an automated and precise response to cyber threats, this research explores the application of AI and ML and proposes an AI-powered cyber incident response system for cloud environments. This system, encompassing Network Traffic Classification, Web Intrusion Detection, and post-incident Malware Analysis (built as a Flask application), achieves seamless integration across platforms like Google Cloud and Microsoft Azure. The findings from this research highlight the effectiveness of the Random Forest model, achieving an accuracy of 90% for the Network Traffic Classifier and 96% for the Malware Analysis Dual Model application. Our research highlights the strengths of AI-powered cyber security. The Random Forest model excels at classifying cyber threats, offering an efficient and robust solution. Deep learning models significantly improve accuracy, and their resource demands can be managed using cloud-based TPUs and GPUs. Cloud environments themselves provide a perfect platform for hosting these AI/ML systems, while container technology ensures both efficiency and scalability. These findings demonstrate the contribution of the AI-led system in guaranteeing a robust and scalable cyber incident response solution in the cloud.