An Intelligent Approach for Preserving the Privacy and Security of a Smart Home Based on IoT Using LogitBoost Techniques
Abstract
Development and use of IoT devices have grown significantly in recent years. Many departments such as smart homes, smart healthcare, smart sports analysis, and different smart industries use IoT-based devices. In IoT devices, traffic is a very important part. IoT device traffic is distinct from traditional device traffic in various respects. In this study, 41 Internet-of-Things (IoT) devices were used. IoT devices provided 13 network traffic attributes to construct a multiclass classification model. Pre-processing techniques such as Normalization and Scaling of Dataset were used to pre-process the raw data acquired. Features can be extracted from text data using feature engineering algorithms. After stratification, the dataset contains 117,423 feature vectors utilized to develop the classification model further. Multiple performance metrics were used to demonstrate how well LogitBoost algorithms perform in this research. Using ensemble-based hybrid machine learning models to detect network anomalies in this research is an early step in developing an intrusion detection system (IDS). The main objective of this study is to detect attacks and anomalies in an IoT environment in a smart home. We have proposed a novel approach to developing LogitBoost algorithms, i.e., Logi-XGB, Logi-GBC, Logi-ABC, Logi-CBC, Logi-LGBM, and Logi-HGBC. After applying LogitBoost algorithms to the dataset for the classification, Logi-XGB scored 80.20% accuracy, and Logi-GBC scored 77.80% accuracy. Logi-ABC scored 80.33% accuracy. Logi-CBC scored the highest accuracy of 85.66%. Logi-LGBM and Logi-HGBC scored the same accuracy of 81.37%. Compared with previous LogitBoost algorithms implemented in previous studies, our proposed Logi-CBC has scored the highest accuracy on the given dataset.
Keywords: logistic regression, boosting models, machine learning.
https://doi.org/10.55463/issn.1674-2974.49.4.39
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KHARE S., and TOTARO M. Ensemble Learning for Detecting Attacks and Anomalies in IoT Smart Home. Proceedings of the 3rd International Conference on Data Intelligence and Security, South Padre Island, Texas, 2020, pp. 56–63. http://dx.doi.org/10.1109/ICDIS50059.2020.00014
MACHORRO-CANO I., ALOR-HERNÁNDEZ G., PAREDES-VALVERDE M. A., RODRÍGUEZ-MAZAHUA L., SÁNCHEZ-CERVANTES J. L., and OLMEDO-AGUIRRE J. O. HEMS-IoT: A big data and machine learning-based smart home system for energy saving. Energies, 2020, 13(5): 1097. https://doi.org/10.3390/en13051097
SPANOS G., GIANNOUTAKIS K. M., VOTIS K., and TZOVARAS D. Combining statistical and machine learning techniques in IoT anomaly detection for smart homes. Proceedings of the IEEE 24th International Workshop on Computer Aided Modeling and Design of Communication Links and Networks (CAMAD), Limassol, 2019, pp. 1–6. https://doi.org/10.1109/CAMAD.2019.8858490
SIVANATHAN A., GHARAKHEILI H. H., LOI F., RADFORD A., WIJENAYAKE C., VISHWANATH A., and SIVARAMAN V. Classifying IoT Devices in Smart Environments Using Network Traffic Characteristics. IEEE Transactions on Mobile Computing, 2019, 18(8): 1745–1759. https://doi.org/10.1109/TMC.2018.2866249
STOLOJESCU-CRISAN C., CRISAN C., and BUTUNOI B. P. An iot-based smart home automation system. Sensors, 2021, 21(11): 3784. https://doi.org/10.3390/s21113784
YAR H., IMRAN A. S., KHAN Z. A., SAJJAD M., and KASTRATI Z. Towards Smart Home Automation Using IoT-Enabled Edge-Computing Paradigm. Sensors, 2021, 21(14): 4932. https://doi.org/10.3390/s21144932
MAHMUD S., AHMED S., and SHIKDER K. A smart home automation and metering system using internet of things (IoT). Proceedings of the International Conference on Robotics, Electrical and Signal Processing Techniques, Dhaka, 2019, pp. 451–454. https://doi.org/10.1109/ICREST.2019.8644232
ULLAH I., and MAHMOUD Q. H. Network Traffic Flow Based Machine Learning Technique for IoT Device Identification. Proceedings of the IEEE International Systems Conference, Vancouver, 2021, pp. 1-8. https://doi.org/10.1109/SysCon48628.2021.9447099
RATHORE A. S., XU C., ZHU W., DAIYAN A., WANG K., LIN F., REN K., and XU W. Scanning the Voice of Your Fingerprint with Everyday Surfaces. IEEE Transactions on Mobile Computing, 2021. https://doi.org/10.1109/TMC.2021.3049217
CVITIĆ I., PERAKOVIĆ D., PERIŠA M., and GUPTA B. Ensemble machine learning approach for classification of IoT devices in smart home. International Journal of Machine Learning and Cybernetics, 2021, 12(11): 3179–3202. https://doi.org/10.1007/s13042-020-01241-0
ZAINAB A., REFAAT S. S., and BOUHALI O. Ensemble-based spam detection in smart home IOT devices time series data using machine learning techniques. Information, 2020, 11(7): 344. https://doi.org/10.3390/info11070344
MAKKAR A., and KUMAR N. An efficient deep learning-based scheme for web spam detection in IoT environment. Future Generation Computer Systems, 2020, 108: 467–487. https://doi.org/10.1016/j.future.2020.03.004
IBRAHIM A. Forecasting the Early Market Movement in Bitcoin Using Twitter's Sentiment Analysis: An Ensemble-based Prediction Model. Proceedings of the IEEE International IOT, Electronics and Mechatronics Conference, Toronto, 2021, pp. 1-5. https://doi.org/10.1109/IEMTRONICS52119.2021.9422647
LI T., HONG Z., and YU L. Machine Learning-based Intrusion Detection for IoT Devices in Smart Home. Proceedings of the IEEE 16th International Conference on Control & Automation, Singapore, 2020, pp. 277–282. https://doi.org/10.1109/ICCA51439.2020.9264406
ANTHI E., WILLIAMS L., SLOWINSKA M., THEODORAKOPOULOS G., and BURNAP P. A Supervised Intrusion Detection System for Smart Home IoT Devices. IEEE Internet of Things Journal, 2019, 6(5): 9042–9053. https://doi.org/10.1109/JIOT.2019.2926365
MANIRIHO P., NIYIGABA E., BIZIMANA Z., TWIRINGIYIMANA V., MAHORO L. J., and AHMAD T. Anomaly-Based Intrusion Detection Approach for IoT Networks Using Machine Learning. Proceedings of the International Conference on Computer Engineering, Network, and Intelligent Multimedia, Surabaya, 2020, pp. 303–308. https://doi.org/10.1109/CENIM51130.2020.9297958
SAHU N. K., and MUKHERJEE I. Machine learning based anomaly detection for IoT network: (Anomaly detection in IoT network). Proceedings of the 4th International Conference on Trends in Electronics and Informatics, Tirunelveli, 2020, pp. 787-794. https://doi.org/10.1109/ICOEI48184.2020.9142921
AÏVODJI U. M., GAMBS S., and MARTIN A. IOTFLA: A secured and privacy-preserving smart home architecture implementing federated learning. Proceedings of the IEEE Security and Privacy Workshops, San Francisco, California, 2019, pp. 175–180. https://doi.org/10.1109/SPW.2019.00041
HSU H. T., JONG G. J., CHEN J. H., and JHE C. G. Improve IoT security system of smart-home by using support vector machine. Proceedings of the IEEE 4th International Conference on Computer and Communication Systems, Singapore, 2019, pp. 674–677. https://doi.org/10.1109/CCOMS.2019.8821678
ALALADE E. D. Intrusion Detection System in Smart Home Network Using Artificial Immune System and Extreme Learning Machine Hybrid Approach. Proceedings of the IEEE 6th World Forum on Internet of Things, New Orleans, Louisiana, 2020, pp. 20–21. https://doi.org/10.1109/WF-IoT48130.2020.9221151
PANDIMURUGAN V., JAIN A., and SINHA Y. IoT based face recognition for smart applications using machine learning. Proceedings of the 3rd International Conference on Intelligent Sustainable Systems, Thoothukudi, 2020, pp. 1263–1266. https://doi.org/10.1109/ICISS49785.2020.9316089
HOU S., and HUANG X. Use of Machine Learning in Detecting Network Security of Edge Computing System. Proceedings of the IEEE 4th International Conference on Big Data Analytics, Suzhou, 2019, pp. 252–256. https://doi.org/10.1109/ICBDA.2019.8713237
LIU Z., DONG X., XUE J., LI H., and CHEN Y. Adaptive Neural Control for a Class of Pure-Feedback
Nonlinear Systems via Dynamic Surface Technique. IEEE Transactions on Neural Networks and Learning Systems, 2016, 27(9): 1969–1975. https://doi.org/10.1109/TNNLS.2015.2462127
SINGLA K., BOSE J., and KATARIYA S. Machine Learning for Secure Device Personalization Using Blockchain. Proceedings of the International Conference on Advances in Computing, Communications and Informatics, Bangalore, 2018, pp. 67–73. https://doi.org/10.1109/ICACCI.2018.8554476
NAKAMURA M. Improving Health and Quality of Life in One-Person Households Using IoT and Machine Learning. Proceedings of the IEEE 17th International Conference on Software Engineering Research, Management and Applications, Honolulu, Hawaii, 2019, pp. 1–1. https://doi.org/10.1109/sera.2019.8886791
ALI B., and AWAD A. I. Cyber and physical security vulnerability assessment for IoT-based smart homes. Sensors, 2018, 18(3): 817. https://doi.org/10.3390/s18030817
LEI X., TU G. H., LI C. Y., XIE T., and ZHANG M. SecWIR: Securing smart home IoT communications via wi-fi routers with embedded intelligence. Proceedings of the 18th International Conference on Mobile Systems, Applications, and Services, Toronto, 2020, pp. 260–272. https://doi.org/10.1145/3386901.3388941
AWAN N., KHAN S., RAHMANI M. K. I., TAHIR M., ALAM N., ALTURKI R., and ULLAH I. Machine Learning-Enabled Power Scheduling in IoT-Based Smart Cities. Computers, Materials & Continua, 2021, 67(2): 2449–2462. https://doi.org/10.32604/cmc.2021.014386
HOQUE M. A., and DAVIDSON C. Design and implementation of an IoT-based smart home security system. International Journal of Networked and Distributed Computing, 2019, 7(2): 85–92. https://doi.org/10.2991/ijndc.k.190326.004
PERDANA D., PAMUNGKAS P. A., and IRAWAN A. I. Smart Door System Prototype with a Control Based on Biometric Palmprint and the Internet of Things. Journal of Southwest Jiaotong University, 2020, 55(6). https://doi.org/10.35741/issn.0258-2724.55.6.33
AL-MASHHADI H. M., and HASSAN K. R. Design and Implementation of a Smart Integrated Framework to Monitor and Control the Smart City using the Internet of Things. Journal of Southwest Jiaotong University, 2019, 54(6). https://doi.org/10.35741/issn.0258-2724.54.6.61
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