A Dataset of Research Papers on IoT Attacks and Security Models (2005-2025) - Systematic Review Corpus

Overview

The Dataset of Research Papers on IoT Attacks and Security Models (2005-2025) published on Zenodo in June 2025 provides a curated corpus of scholarly literature for systematic review and meta-analysis of IoT security research over two decades.

Compilation Methodology

• Systematic search conducted across five major scholarly databases: IEEE Xplore, Elsevier ScienceDirect, Springer, ACM Digital Library, and MDPI.
• Search period: 2005 (early IoT emergence) to 2025 (mature IoT security field).
• Keyword-based queries focused on IoT attacks, security models, threat detection, and defense mechanisms across all network layers.
• Relevance filtering applied to select papers most pertinent to IoT network security systematic review.

Systematic Review Context

• Dataset compiled for the paper "A Survey of the Security of IoT Network Layers", providing a comprehensive review of security challenges and solutions across physical, data link, network, transport, and application layers.
• Inclusion criteria: Primary research, surveys, and tutorials on IoT-specific security issues (not general network security).
• Exclusion criteria: Duplicate papers, non-peer-reviewed sources, and papers outside the IoT security scope.

Dataset Contents

• Bibliographic records: Authors, titles, abstracts, keywords, publication venues, years, DOIs.
• Attack taxonomy data: Classification of discussed attack types (DDoS, eavesdropping, spoofing, malware, physical attacks, etc.).
• Defense mechanism categories: Cryptography, authentication, intrusion detection, access control, secure protocols.
• Network layer mapping: Tagging papers by which IoT network layer(s) they address.
• Temporal trends: Publication year distribution showing evolution of IoT security research focus.

Use Cases

• Systematic literature reviews: Supporting comprehensive reviews of IoT security sub-domains (e.g., edge security, authentication protocols).
• Research gap identification: Discovering underexplored attack vectors, defense mechanisms, or network layers.
• Trend analysis: Tracking how research priorities have shifted from early IoT (connectivity, scalability) to mature IoT (AI-driven threats, privacy).
• Citation network analysis: Identifying foundational papers, influential authors, and research communities in IoT security.
• Educational purposes: Providing PhD students and early-career researchers with curated entry points into IoT security literature.