Machine Learning Framework to Analyze IoT Malware Using ELF and Opcode Features
Journal
Digital Threats: Research and Practice
Journal Volume
1
Journal Issue
1
Date Issued
2020
Author(s)
Abstract
Threats to devices that are part of the Internet of Things (IoT) are on the rise. Owing to the overwhelming diversity of IoT hardware and software, as well as its variants, conventional anti-virus techniques based on the Windows paradigm cannot be applied directly to counter threats to the IoT devices. In this article, we propose a framework that can efficiently analyze IoT malware in a wide range of environments. It consists of a universal feature representation obtained by static analysis of the malware and a machine learning scheme that first detects the malware and then classifies it into a known category. The framework was evaluated by applying it to a recently developed dataset consisting of more than 6,000 IoT malware samples collected from the HoneyPot project. The results show that the proposed method can obtain near-optimal accuracy in terms of the detection and classification of malware targeting IoT devices. © 2020 Owner/Author.
Subjects
ELF analysis; IoT security; machine learning; malware classification; malware detection; opcode analysis
Other Subjects
Computer viruses; Machine learning; Static analysis; Turing machines; Viruses; Anti virus; Counter threat; Feature representation; Hardware and software; Honeypots; Internet of thing (IOT); Near-optimal; Internet of things
Type
journal article