# Evaluation of an Anomaly Detector for Routers using Parameterizable Malware in an IoT Ecosystem

arXiv: https://arxiv.org/abs/2111.00097 · HTML: https://mlsanity.com/paper/2111.00097/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 12 references that are in the map.
- First posted on arXiv 29 Oct 2021.

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

- [Deep Learning for Anomaly Detection: A Survey](https://mlsanity.com/paper/1901.03407/) (2019)
- [Machine Learning DDoS Detection for Consumer Internet of Things Devices](https://mlsanity.com/paper/1804.04159/) (2018)

## Built on by (most cited first, 1 of 1)

- [Simulation of Attacker Defender Interaction in a Noisy Security Game](https://mlsanity.com/paper/2212.04281/) (2022)

## Abstract (arXiv)

This work explores the evaluation of a machine learning anomaly detector using custom-made parameterizable malware in an Internet of Things (IoT) Ecosystem. It is assumed that the malware has infected, and resides on, the Linux router that serves other devices on the network, as depicted in Figure 1. This IoT Ecosystem was developed as a testbed to evaluate the efficacy of a behavior-based anomaly detector. The malware consists of three types of custom-made malware: ransomware, cryptominer, and keylogger, which all have exfiltration capabilities to the network. The parameterization of the malware gives the malware samples multiple degrees of freedom, specifically relating to the rate and size of data exfiltration. The anomaly detector uses feature sets crafted from system calls and network traffic, and uses a Support Vector Machine (SVM) for behavioral-based anomaly detection. The custom-made malware is used to evaluate the situations where the SVM is effective, as well as the situations where it is not effective.