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Vol.13, No.2, May 2024. ISSN: 2217-8309 eISSN: 2217-8333
TEM Journal
TECHNOLOGY, EDUCATION, MANAGEMENT, INFORMATICS Association for Information Communication Technology Education and Science |
IoT-Based Machine Learning System for Physical Intrusion Detection Using Dynamic Temperature and Humidity Observations With Raspberry Pi
Kemal Hajdarevic
© 2024 Kemal Hajdarevic, published by UIKTEN. This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. (CC BY-NC-ND 4.0)
Citation Information: TEM Journal. Volume 13, Issue 2, Pages 918-925, ISSN 2217-8309, DOI: 10.18421/TEM132-08, May 2024.
Received: 13 January 2024. Revised: 25 March 2024.
Abstract:
This paper presents a system that is able to detect physical intrusion in a specific space based on temperature and humidity change. This specific space was housing hardware components important for information security management infrastructure. Presented system is able to predict that two spaces are connected and that there is a physical breach in protected space. The presented prediction approach involves identifying patterns in historical data, where the subsequent outcomes are already known in advance, and validating these patterns using more recent data. System is implemented using k-Nearest Neighbours, Random Forest, and Support Vector Machine algorithms in Python programming language on Raspberry Pi. Real observed data to predict if specific temperature and humidity indicates intrusion were used. This approach can be used to detect intrusions in the room or in other closed space. More specifically thermal equilibrium phenomenon between two spaces after barrier between them are opened was monitored. Through process of supervised learning using labelled data, system was able to detect intrusion by using k-nearest neighbours, random forest, and support vector machine with different accuracy. Presented model shows better results using k-nearest neighbours and support vector machine with accuracy of 100% compared to random forest with accuracy of 95%. The system is low cost because of cheap Raspberry Pi controller and sensors.
Keywords –KNN, random forest, support vector machine, machine learning, Raspberry Pi. |
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