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

TL;DR

This paper evaluates a machine learning-based anomaly detector for IoT routers using custom malware with adjustable exfiltration parameters, assessing its effectiveness in identifying malicious behavior in a controlled testbed.

Contribution

It introduces a parameterizable malware framework and evaluates the performance of a behavior-based SVM anomaly detector in an IoT environment.

Findings

SVM detects malware exfiltration effectively under certain conditions

Detection performance varies with malware exfiltration rate and size

The testbed provides a controlled environment for IoT security evaluation

Abstract

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.