{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,3]],"date-time":"2026-06-03T15:44:03Z","timestamp":1780501443676,"version":"3.54.1"},"reference-count":45,"publisher":"MDPI AG","issue":"15","license":[{"start":{"date-parts":[[2023,8,6]],"date-time":"2023-08-06T00:00:00Z","timestamp":1691280000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Internet of Things (IoT) devices for the home have made a lot of people\u2019s 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\u2019s 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\u2019 generalizability, explore advanced techniques such as transfer learning and hybrid methods, investigate privacy-preserving mechanisms, and address deployment challenges.<\/jats:p>","DOI":"10.3390\/s23156979","type":"journal-article","created":{"date-parts":[[2023,8,6]],"date-time":"2023-08-06T10:01:53Z","timestamp":1691316113000},"page":"6979","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":57,"title":["Enhancing Smart Home Security: Anomaly Detection and Face Recognition in Smart Home IoT Devices Using Logit-Boosted CNN Models"],"prefix":"10.3390","volume":"23","author":[{"given":"Asif","family":"Rahim","sequence":"first","affiliation":[{"name":"School of Computer and Information Security, Guilin University of Electronic Technology, Guilin 541004, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yanru","family":"Zhong","sequence":"additional","affiliation":[{"name":"Guangxi Key Laboratory of Intelligent Processing of Computer Images and Graphic, Guilin University of Electronic Technology, Guilin 541004, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5062-1791","authenticated-orcid":false,"given":"Tariq","family":"Ahmad","sequence":"additional","affiliation":[{"name":"School of Information and Communication Engineering, Guilin University of Electronic Technology, Guilin 541004, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sadique","family":"Ahmad","sequence":"additional","affiliation":[{"name":"EIAS: Data Science and Blockchain Laboratory, College of Computer and Information Sciences, Prince Sultan University, Riyadh 11586, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4317-2801","authenticated-orcid":false,"given":"Pawe\u0142","family":"P\u0142awiak","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Faculty of Computer Science and Telecommunications, Cracow University of Technology, Warszawska 24 Str., 31-155 Krakow, Poland"},{"name":"Institute of Theoretical and Applied Informatics, Polish Academy of Sciences, Ba\u0142tycka 5, 44-100 Gliwice, Poland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6506-3083","authenticated-orcid":false,"given":"Mohamed","family":"Hammad","sequence":"additional","affiliation":[{"name":"EIAS Data Science Lab, College of Computer and Information Sciences, Prince Sultan University, Riyadh 11586, Saudi Arabia"},{"name":"Department of Information Technology, Faculty of Computers and Information, Menoufia University, Shibin El-Kom 32511, Egypt"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,8,6]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Alsheakh, H., and Bhattacharjee, S. (2020, January 10\u201313). Towards a Unified Trust Framework for Detecting IoT Device Attacks in Smart Homes. Proceedings of the 2020 IEEE 17th International Conference on Mobile Ad Hoc and Sensor Systems (MASS), Delhi, India.","DOI":"10.1109\/MASS50613.2020.00080"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"42","DOI":"10.1007\/s10916-019-1158-z","article-title":"Smart home-based IoT for real-time and secure remote health monitoring of triage and priority system using body sensors. Multi-driven systematic review","volume":"43","author":"Talal","year":"2019","journal-title":"J. Med. Syst."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"TAlrawi, O., Lever, C., Antonakakis, M., and Monrose, F. (19, January 19\u201323). Sok: Security evaluation of home-based iot deployments. Proceedings of the MIn2019 IEEE symposium on security and privacy (sp), San Francisco, CA, USA.","DOI":"10.1109\/SP.2019.00013"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"10350","DOI":"10.3390\/s150510350","article-title":"WSN-and IOT-based smart homes and their extension to smart buildings","volume":"15","author":"Ghayvat","year":"2015","journal-title":"Sensors"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"124167","DOI":"10.1109\/ACCESS.2022.3224806","article-title":"A Comprehensive Survey of Security Issues of Smart Home System: \u201cSpear\u201d and \u201cShields\u201d, Theory and Practice","volume":"10","author":"Yang","year":"2022","journal-title":"IEEE Access"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Paudel, R., Muncy, T., and Eberle, W. (2019, January 9\u201312). Detecting dos attack in smart home iot devices using a graph-based approach. Proceedings of the 2019 IEEE international conference on big data (big data), Los Angeles, CA, USA.","DOI":"10.1109\/BigData47090.2019.9006156"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Kumar, S., Benedict, S., and Ajith, S. (2019, January 28\u201329). Application of natural language processing and IoTCloud in smart Homes. Proceedings of the 2019 2nd International Conference on Intelligent Communication and Computational Techniques (ICCT), Jaipur, India.","DOI":"10.1109\/ICCT46177.2019.8969066"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Eleyan, A., and Fallon, J. (2020, January 20\u201322). IoT-based home automation using android application. Proceedings of the 2020 International Symposium on Networks, Computers and Communications (ISNCC), Montreal, QC, Canada.","DOI":"10.1109\/ISNCC49221.2020.9297320"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Stolojescu-Crisan, C., Crisan, C., and Butunoi, B.P. (2021). An IoT-based smart home automation system. Sensors, 21.","DOI":"10.3390\/s21113784"},{"key":"ref_10","first-page":"28","article-title":"Smart home automation using IOT and its low cost implementation","volume":"10","author":"Shah","year":"2020","journal-title":"Int. J. Eng. Manuf. IJEM"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Shukla, V.K., and Singh, B. (2019, January 4\u20136). Conceptual framework of smart device for smart home management based on rfid and iot. Proceedings of the 2019 Amity International Conference on Artificial Intelligence (AICAI), Dubai, United Arab Emirates.","DOI":"10.1109\/AICAI.2019.8701301"},{"key":"ref_12","first-page":"787","article-title":"FamilyGuard. A Security Architecture for Anomaly Detection in Home Networks","volume":"22","author":"Miani","year":"2022","journal-title":"Sensors"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Geneiatakis, D., Kounelis, I., Neisse, R., Nai-Fovino, I., Steri, G., and Baldini, G. (2017, January 22\u201326). Security and privacy issues for an IoT based smart home. Proceedings of the 2017 40th International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), Opatija, Croatia.","DOI":"10.23919\/MIPRO.2017.7973622"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"606","DOI":"10.3390\/s17030606","article-title":"Erratum: Sandeep P.; et al. A Novel Secure IoT-Based Smart Home Automation System Using a Wireless Sensor Network","volume":"17","author":"Pirbhulal","year":"2017","journal-title":"Sensors"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"100568","DOI":"10.1016\/j.iot.2022.100568","article-title":"IoT Anomaly Detection methods and applications: A survey","volume":"19","author":"Chatterjee","year":"2022","journal-title":"Internet Things"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"32","DOI":"10.3390\/designs3030032","article-title":"Control of smart home operations using natural language processing, voice recognition and IoT technologies in a multi-tier architecture","volume":"3","author":"Alexakis","year":"2019","journal-title":"Designs"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"85","DOI":"10.2991\/ijndc.k.190326.004","article-title":"Design and implementation of an IoT-based smart home security system","volume":"7","author":"Hoque","year":"2019","journal-title":"Int. J. Netw. Distrib. Comput."},{"key":"ref_18","first-page":"2453","article-title":"Home automation system based on IoT","volume":"62","author":"HAbdulraheem","year":"2020","journal-title":"Technol. Rep. Kansai Univ."},{"key":"ref_19","first-page":"3","article-title":"Internet of things (IoT) of smart home: Privacy and security","volume":"182","author":"Shouran","year":"2019","journal-title":"Int. J. Comput. Appl."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"817","DOI":"10.3390\/s18030817","article-title":"Cyber and physical security vulnerability assessment for IoT-based smart homes","volume":"18","author":"Ali","year":"2018","journal-title":"Sensors"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1097","DOI":"10.3390\/en13051097","article-title":"HEMS-IoT: A big data and machine learning-based smart home system for energy saving","volume":"13","year":"2020","journal-title":"Energies"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Alsakran, F., Bendiab, G., Shiaeles, S., and Kolokotronis, N. (2019, January 18\u201321). Intrusion detection systems for smart home IoT devices: Experimental comparison study. Proceedings of the In Security in Computing and Communications: 7th International Symposium, SSCC 2019, Trivandrum, India. Revised Selected Papers.","DOI":"10.1007\/978-981-15-4825-3_7"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"903","DOI":"10.1109\/TII.2020.2968927","article-title":"An Enhanced Efficient Approach For Spam Detection In IOT Devices Using Machine Learning","volume":"17","author":"Preethi","year":"2021","journal-title":"IEEE Trans. Ind. Inform."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Spanos, G., Giannoutakis, K.M., Votis, K., and Tzovaras, D. (2019, January 11\u201313). Combining statistical and machine learning techniques in IoT Anomaly Detection for smart homes. Proceedings of the 2019 IEEE 24th International Workshop on Computer Aided Modeling and Design of Communication Links and Networks (CAMAD), Limassol, Cyprus.","DOI":"10.1109\/CAMAD.2019.8858490"},{"key":"ref_25","first-page":"72","article-title":"Evaluating machine learning techniques for activity classification in smart home environments","volume":"12","author":"Alshammari","year":"2018","journal-title":"Int. J. Inf. Commun. Eng."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"344","DOI":"10.3390\/info11070344","article-title":"Ensemble-based spam detection in smart home IoT devices time series data using machine learning techniques","volume":"11","author":"Zainab","year":"2020","journal-title":"Information"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Rani, P.J., Bakthakumar, J., Kumaar, B.P., Kumaar, U.P., and Kumar, S. (2017, January 23). Ensemble-based spam detection in smart home IoT devices time series data using machine learning techniquesVoice controlled home automation system using natural language processing (NLP) and internet of things (IoT). Proceedings of the 2017 Third International Conference on Science Technology Engineering & Management (ICONSTEM), Chennai, India.","DOI":"10.1109\/ICONSTEM.2017.8261311"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Li, T., Hong, Z., and Yu, L. (2020, January 9\u201311). Machine learning-based intrusion detection for iot devices in smart home. Proceedings of the 2020 IEEE 16th International Conference on Control & Automation (ICCA), Singapore.","DOI":"10.1109\/ICCA51439.2020.9264406"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Gordon, H., Batula, C., Tushir, B., Dezfouli, B., and Liu, Y. (2021, January 12\u201316). Securing smart homes via software-defined networking and low-cost traffic classification. Proceedings of the 2021 IEEE 45th Annual Computers, Software, and Applications Conference (COMPSAC), Madrid, Spain.","DOI":"10.1109\/COMPSAC51774.2021.00143"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Khare, S., and Totaro, M. (2020, January 24\u201326). Ensemble learning for detecting attacks and anomalies in IoT smart home. Proceedings of the 2020 3rd International Conference on Data Intelligence and Security (ICDIS), South Padre Island, TX, USA.","DOI":"10.1109\/ICDIS50059.2020.00014"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"4598","DOI":"10.3390\/math10234598","article-title":"Intelligent Deep Learning for Anomaly-Based Intrusion Detection in IoT Smart Home Networks","volume":"10","author":"Butt","year":"2022","journal-title":"Mathematics"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"9042","DOI":"10.1109\/JIOT.2019.2926365","article-title":"A supervised intrusion detection system for smart home IoT devices","volume":"6","author":"Anthi","year":"2019","journal-title":"IEEE Internet Things"},{"key":"ref_33","first-page":"45","article-title":"Smart Home Security System using Iot, Face Recognition, and Raspberry Pi","volume":"176","author":"Dhobale","year":"2020","journal-title":"IEEE Int. J. Comput. Appl."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"e4053","DOI":"10.1002\/ett.4053","article-title":"Anomaly traffic detection and correlation in smart home automation IoT systems","volume":"33","author":"Gajewski","year":"2022","journal-title":"IEEE Ransactions Emerg. Telecommun. Technol."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Nguyen, T.D., Marchal, S., Miettinen, M., Fereidooni, H., Asokan, N., and Sadeghi, A.R. (2019, January 7\u201310). D\u00cfoT: A federated self-learning Anomaly Detection system for IoT. Proceedings of the 2019 IEEE 39th International conference on distributed computing systems (ICDCS), Dallas, TX, USA.","DOI":"10.1109\/ICDCS.2019.00080"},{"key":"ref_36","first-page":"372","article-title":"An Intelligent Approach for Preserving the Privacy and Security of a Smart Home Based on IoT Using LogitBoost Techniques","volume":"49","author":"Rahim","year":"2022","journal-title":"J. Hunan Univ. Nat. Sci."},{"key":"ref_37","first-page":"39","article-title":"A Deep Learning-Based Intelligent Face Recognition Method in the Internet of Home Things for Security Applications","volume":"49","author":"Rahim","year":"2022","journal-title":"J. Hunan Univ. Nat. Sci."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Hosni, A.I.E., Li, K., and Ahmad, S. (2019, January 12\u201315). DARIM: Dynamic approach for rumor influence minimization in online social networks. Proceedings of the Neural Information Processing: 26th International Conference, ICONIP 2019, Sydney, Australia.","DOI":"10.1007\/978-3-030-36711-4_52"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Hosni, A.I.E., Li, K., and Ahmed, S. (2018, January 13\u201316). HISBmodel: A rumor diffusion model based on human individual and social behaviors in online social networks. Proceedings of the Neural Information Processing: 25th International Conference, ICONIP 2018, Siem Reap, Cambodia.","DOI":"10.1007\/978-3-030-04179-3_2"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"33148","DOI":"10.1109\/ACCESS.2023.3263155","article-title":"Human Activity Recognition Based on Deep-Temporal Learning Using Convolution Neural Networks Features and Bidirectional Gated Recurrent Unit With Features Selection","volume":"11","author":"Ahmad","year":"2023","journal-title":"IEEE Access"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Ahmad, T., and Wu, J. (2023). SDIGRU: Spatial and deep features integration using multilayer gated recurrent unit for human activity recognition. IEEE Trans. Comput. Soc. Syst., 1\u201313.","DOI":"10.1109\/TCSS.2023.3249152"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"1182","DOI":"10.3390\/bios12121182","article-title":"Feature-based information retrieval of multimodal biosignals with a self-similarity matrix: Focus on automatic segmentation","volume":"12","author":"Rodrigues","year":"2022","journal-title":"Biosensors"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Xue, T., and Liu, H. (2021, January 21). Hidden Markov Model and its application in human activity recognition and fall detection: A review. Proceedings of the International Conference in Communications, Signal Processing, and Systems, Chang Bai Shan, China.","DOI":"10.1007\/978-981-19-0390-8_108"},{"key":"ref_44","unstructured":"Kayan, H. (2023, May 07). AnoML-IoT\u2014The Anomaly Detection Dataset. Available online: https:\/\/www.kaggle.com\/datasets\/hkayan\/anomliot."},{"key":"ref_45","unstructured":"Nam, S. (2023, May 07). Real and Fake Face Detection. Available online: https:\/\/www.kaggle.com\/datasets\/ciplab\/real-and-fake-face-detection."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/15\/6979\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T20:26:41Z","timestamp":1760128001000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/15\/6979"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,8,6]]},"references-count":45,"journal-issue":{"issue":"15","published-online":{"date-parts":[[2023,8]]}},"alternative-id":["s23156979"],"URL":"https:\/\/doi.org\/10.3390\/s23156979","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,8,6]]}}}