# Research on Dynamic Data Flow Anomaly Detection based on Machine Learning

arXiv: https://arxiv.org/abs/2409.14796 · HTML: https://mlsanity.com/paper/2409.14796/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 10 references that are in the map.
- First posted on arXiv 23 Sep 2024.

## Builds on (most cited first, 2 of 2)

- [Smart Anomaly Detection in Sensor Systems: A Multi-Perspective Review](https://mlsanity.com/paper/2010.14946/) (2020)
- [A Critical Review of Common Log Data Sets Used for Evaluation of Sequence-based Anomaly Detection Techniques](https://mlsanity.com/paper/2309.02854/) (2023)

## Built on by (most cited first, 2 of 2)

- [Research on reinforcement learning based warehouse robot navigation algorithm in complex warehouse layout](https://mlsanity.com/paper/2411.06128/) (2024)
- [Machine Learning-Based Cloud Computing Compliance Process Automation](https://mlsanity.com/paper/2502.16344/) (2025)

## Abstract (arXiv)

The sophistication and diversity of contemporary cyberattacks have rendered the use of proxies, gateways, firewalls, and encrypted tunnels as a standalone defensive strategy inadequate. Consequently, the proactive identification of data anomalies has emerged as a prominent area of research within the field of data security. The majority of extant studies concentrate on sample equilibrium data, with the consequence that the detection effect is not optimal in the context of unbalanced data. In this study, the unsupervised learning method is employed to identify anomalies in dynamic data flows. Initially, multi-dimensional features are extracted from real-time data, and a clustering algorithm is utilised to analyse the patterns of the data. This enables the potential outliers to be automatically identified. By clustering similar data, the model is able to detect data behaviour that deviates significantly from normal traffic without the need for labelled data. The results of the experiments demonstrate that the proposed method exhibits high accuracy in the detection of anomalies across a range of scenarios. Notably, it demonstrates robust and adaptable performance, particularly in the context of unbalanced data.