CyberdeceptionAtlas · Question → testES

Library / Paper

Paper

Securing smart cities through machine learning: A honeypot‐driven approach to attack detection in Internet of Things ecosystems

The work addresses security in Internet of Things (IoT) ecosystems in smart cities by using honeypot data and machine learning and neural network algorithms for attack detection. The authors set out to identify datasets from IoT-targeted honeypots, evaluate threat detection algorithms, and propose security solutions, analyzing real-world cyber-attack data from diverse honeypots simulating IoT devices.

HoneypotIoT

What we know

The work addresses security in Internet of Things (IoT) ecosystems in smart cities by using honeypot data and machine learning and neural network algorithms for attack detection. The authors set out to identify datasets from IoT-targeted honeypots, evaluate threat detection algorithms, and propose security solutions, analyzing real-world cyber-attack data from diverse honeypots simulating IoT devices.

Evidence limits

Review based solely on the abstract deposited in Crossref, not on the full text or independent validation of results.

Reviewed: author abstract. Full-text results were not analyzed.

Sources and provenance

  1. Securing smart cities through machine learning: A honeypot‐driven approach to attack detection in Internet of Things ecosystems
    abstract · 2026-10-11

Reviewed: 2026-10-13. This record may change when new evidence is found.

RIS · BibTeX