# Cloud Security Leveraging AI: A Fusion-Based AISOC for Malware and Log Behaviour Detection

arXiv: https://arxiv.org/abs/2512.14935 · HTML: https://mlsanity.com/paper/2512.14935/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 1 of its 23 references that are in the map.
- First posted on arXiv 16 Dec 2025.

## Builds on (most cited first, 1 of 1)

- [Benchmark Static API Call Datasets for Malware Family Classification](https://mlsanity.com/paper/2111.15205/) (2021)

## Abstract (arXiv)

Cloud Security Operations Center (SOC) enable cloud governance, risk and compliance by providing insights visibility and control. Cloud SOC triages high-volume, heterogeneous telemetry from elastic, short-lived resources while staying within tight budgets. In this research, we implement an AI-Augmented Security Operations Center (AISOC) on AWS that combines cloud-native instrumentation with ML-based detection. The architecture uses three Amazon EC2 instances: Attacker, Defender, and Monitoring. We simulate a reverse-shell intrusion with Metasploit, and Filebeat forwards Defender logs to an Elasticsearch and Kibana stack for analysis. We train two classifiers, a malware detector built on a public dataset and a log-anomaly detector trained on synthetically augmented logs that include adversarial variants. We calibrate and fuse the scores to produce multi-modal threat intelligence and triage activity into NORMAL, SUSPICIOUS, and HIGHCONFIDENCEATTACK. On held-out tests the fusion achieves strong macro-F1 (up to 1.00) under controlled conditions, though performance will vary in noisier and more diverse environments. These results indicate that simple, calibrated fusion can enhance cloud SOC capabilities in constrained, cost-sensitive setups.