IoT malware: An attribute-based taxonomy, detection mechanisms and challenges.

Area of Science:

• Computer Science
• Cybersecurity
• Network Security

Background:

• The Internet of Things (IoT) has rapidly digitized society, improving supply chains but also creating new vulnerabilities.
• The diversity of IoT devices makes them attractive targets for malware authors.
• Current research often lacks a deep understanding of IoT malware's multifaceted nature.

Purpose of the Study:

• To establish a foundational understanding of IoT malware by creating a detailed taxonomy.
• To categorize and analyze various aspects of IoT malware, including attack vectors, characteristics, and detection methods.
• To provide insights into challenges and guide future research in IoT malware detection and mitigation.

Main Methods:

• Development of an IoT malware taxonomy encompassing 100 attributes.
• Categorization based on attack types, surfaces, distribution, victim devices, characteristics, access mechanisms, programming languages, and protocols.
• Mapping the taxonomy to 77 identified IoT malwares (2008-2022) and reviewing existing detection works.

Main Results:

• A structured taxonomy of IoT malware with 100 distinct attributes.
• Analysis and mapping of 77 real-world IoT malware instances.
• Identification of key areas and challenges in current IoT malware detection research.

Conclusions:

• A comprehensive understanding of IoT malware is crucial for effective security enhancement.
• The proposed taxonomy provides a standardized framework for analyzing and comparing IoT malware.
• Further research is needed to address the evolving landscape of IoT threats and improve detection efficacy.