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This work explores the evaluation of a machine learning anomaly detectorusing custom-made parameterizable malware in an Internet of Things (IoT)Ecosystem. It is assumed that the malware has infected, and resides on, theLinux 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 abehavior-based anomaly detector. The malware consists of three types ofcustom-made malware: ransomware, cryptominer, and keylogger, which all haveexfiltration capabilities to the network. The parameterization of the malwaregives the malware samples multiple degrees of freedom, specifically relating tothe rate and size of data exfiltration. The anomaly detector uses feature setscrafted from system calls and network traffic, and uses a Support VectorMachine (SVM) for behavioral-based anomaly detection. The custom-made malwareis used to evaluate the situations where the SVM is effective, as well as thesituations where it is not effective.

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