# Machine Learning for Anomaly Detection and Categorization in Multi-cloud Environments

arXiv: https://arxiv.org/abs/1812.05443 · HTML: https://mlsanity.com/paper/1812.05443/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Oct 2018.

## Built on by (most cited first, 6 of 6)

- [RUAD: unsupervised anomaly detection in HPC systems](https://mlsanity.com/paper/2208.13169/) (2022)
- [Orthogonal variance-based feature selection for intrusion detection systems](https://mlsanity.com/paper/2110.12627/) (2021)
- [Data Curation and Quality Assurance for Machine Learning-based Cyber Intrusion Detection](https://mlsanity.com/paper/2105.10041/) (2021)
- [Machine Learning for Real-Time Anomaly Detection in Optical Networks](https://mlsanity.com/paper/2306.10741/) (2023)
- [A Review of Machine Learning-based Security in Cloud Computing](https://mlsanity.com/paper/2309.04911/) (2023)
- [How to integrate cloud service, data analytic and machine learning technique to reduce cyber risks associated with the modern cloud based infrastructure](https://mlsanity.com/paper/2405.11601/) (2024)

## Abstract (arXiv)

Recently, advances in machine learning techniques have attracted the attention of the research community to build intrusion detection systems (IDS) that can detect anomalies in the network traffic. Most of the research works, however, do not differentiate among different types of attacks. This is, in fact, necessary for appropriate countermeasures and defense against attacks. In this paper, we investigate both detecting and categorizing anomalies rather than just detecting, which is a common trend in the contemporary research works. We have used a popular publicly available dataset to build and test learning models for both detection and categorization of different attacks. To be precise, we have used two supervised machine learning techniques, namely linear regression (LR) and random forest (RF). We show that even if detection is perfect, categorization can be less accurate due to similarities between attacks. Our results demonstrate more than 99% detection accuracy and categorization accuracy of 93.6%, with the inability to categorize some attacks. Further, we argue that such categorization can be applied to multi-cloud environments using the same machine learning techniques.