A4 Refereed article in a conference publication
Experimental Implementation of a Low Cost Real-Time Threat Intelligence Solution for Smart Home Security
Authors: Addison Samuel Kwaku; Mohammad, Tahir; Isoaho, Jouni
Editors: Shakshuki, Elhadi; Yasar, Ansar
Conference name: International Conference on Ambient Systems, Networks and Technologies
Publisher: Elsevier BV
Publication year: 2025
Journal: Procedia Computer Science
Book title : The 16th International Conference on Ambient Systems, Networks and Technologies Networks (ANT)/ the 8th International Conference on Emerging Data and Industry 4.0 (EDI40)
Journal name in source: Procedia Computer Science
Volume: 257
First page : 575
Last page: 582
eISSN: 1877-0509
DOI: https://doi.org/10.1016/j.procs.2025.03.074(external)
Web address : https://doi.org/10.1016/j.procs.2025.03.074(external)
Self-archived copy’s web address: https://research.utu.fi/converis/portal/detail/Publication/492313466(external)
The growing adoption of the Internet of Things (IoT) in smart home technology has revolutionized modern living, offering convenience and enhancing quality of life. However, integrating IoT devices into daily life has also introduced complex cybersecurity challenges. Existing approaches to smart home security often rely on static measures that struggle to keep pace with evolving threats. Although threat intelligence has a wide range of applications in organizational cybersecurity, its use in smart home environments is mostly under-explored. This study uses a Raspberry Pi to explore a proactive solution by integrating low-cost realtime threat intelligence using the Malware Information Sharing Platform (MISP) with intrusion detection and prevention systems (IDS/IPS) powered by Suricata. Simulating diverse cyber threats, including brute-force, denial-of-service (DoS) attacks, and mal-ware infection the research evaluates how real-time updates from threat intelligence platforms enhance detection and mitigation. The findings reveal that incorporating dynamic threat intelligence drastically improves response accuracy, achieving a 99.9% detection and prevention rate. This approach outperformed traditional methods by enabling rapid adaptation to new attack patterns. The study takes a practical, adaptive method in the protection of smart homes and takes the aspect of IoT security a step ahead with real-time use of threat intelligence.
Downloadable publication This is an electronic reprint of the original article. |  |
