Enhancing smart home security: anomaly detection and face recognition in smart home IoT devices using logit-boosted CNN models
Authors:
- Asif Rahim,
- Yanru Zhong,
- Tariq Ahmad,
- Sadique Ahmad,
- Paweł Pławiak,
- Mohamed Hammad
Abstract
Internet of Things (IoT) devices for the home have made a lot of people’s lives better, but their popularity has also raised privacy and safety concerns. This study explores the application of deep learning models for anomaly detection and face recognition in IoT devices within the context of smart homes. Six models, namely, LR-XGB-CNN, LR-GBC-CNN, LR-CBC-CNN, LR-HGBC-CNN, LR-ABC-CNN, and LR-LGBM-CNN, were proposed and evaluated for their performance. The models were trained and tested on labeled datasets of sensor readings and face images, using a range of performance metrics to assess their effectiveness. Performance evaluations were conducted for each of the proposed models, revealing their strengths and areas for improvement. Comparative analysis of the models showed that the LR-HGBC-CNN model consistently outperformed the others in both anomaly detection and face recognition tasks, achieving high accuracy, precision, recall, F1 score, and AUC-ROC values. For anomaly detection, the LR-HGBC-CNN model achieved an accuracy of 94%, a precision of 91%, a recall of 96%, an F1 score of 93%, and an AUC-ROC of 0.96. In face recognition, the LR-HGBC-CNN model demonstrated an accuracy of 88%, precision of 86%, recall of 90%, F1 score of 88%, and an AUC-ROC of 0.92. The models exhibited promising capabilities in detecting anomalies, recognizing faces, and integrating these functionalities within smart home IoT devices. The study’s findings underscore the potential of deep learning approaches for enhancing security and privacy in smart homes. However, further research is warranted to evaluate the models’ generalizability, explore advanced techniques such as transfer learning and hybrid methods, investigate privacy-preserving mechanisms, and address deployment challenges.
- Record ID
- CUT46e74fca739b4266a02f5cdd72ad404a
- Publication categories
- ;
- Author
- Journal series
- Sensors, ISSN , e-ISSN 1424-8220, Biweekly
- Issue year
- 2023
- Vol
- 23
- No
- 15
- Pages
- [1-43]
- Article number
- 6979
- Other elements of collation
- schem.; tab.; wykr.; Bibliografia (na s.) - 41-43; Bibliografia (liczba pozycji) - 45; Oznaczenie streszczenia - Abstr.; Numeracja w czasopiśmie - Vol. 23, Iss. 15
- Substantive notes
- This article belongs to the Special Issue: Deep Learning Based Face Recognition and Feature Extraction
- Keywords in English
- face recognition, anomaly detection, logistic regression (LR), convolutional neural network (CNN), gradient-boosting classifier, machine learning
- ASJC Classification
- ; ; ;
- DOI
- DOI:10.3390/s23156979 Opening in a new tab
- URL
- https://www.mdpi.com/1424-8220/23/15/6979 Opening in a new tab
- Language
- eng (en) English
- License
- Score (nominal)
- 100
- Score source
- journalList
- Score
- Publication indicators
- Citation count
- 11
- Additional fields
- Indeksowana w: Web of Science, Scopus
- Uniform Resource Identifier
- https://cris.pk.edu.pl/info/article/CUT46e74fca739b4266a02f5cdd72ad404a/
- URN
urn:pkr-prod:CUT46e74fca739b4266a02f5cdd72ad404a
* presented citation count is obtained through Internet information analysis, and it is close to the number calculated by the Publish or PerishOpening in a new tab system.