IoT-based Smart Home Security System with Machine Learning Models

Abstract

The Internet of Things (IoT) has various applications in practice, such as smart homes and buildings, traffic management, industrial management, and smart farming. On the other hand, security issues are raised by the growing use of IoT applications. Researchers develop machine learning models that focus on better classification accuracy and decreasing model response time to solve this security problem. In this study, we made a comparative evaluation of machine learning algorithms for intrusion detection systems on IoT networks using the DS2oS dataset. The dataset was first processed to feature extraction using the info gain attribute evaluation feature extraction approach. The original dataset (12 attributes), the dataset (6 attributes) produced using the info gain approach, and the dataset (11 attributes) obtained by eliminating the timestamp attribute was then formed. These datasets were subjected to performance testing using several machine learning methods and test choices (crossfold-10, percentage split). The test performance results are presented, and an evaluation is performed, such as accuracy, precision, recall, and F1 score. According to the test results, it has been observed that high accuracy detection rates are achieved for IoT devices with limited processing power.

Keywords

• Information Security
• IoT
• Intrusion Detection System
• Machine Learning

References

1. M. Hasan, M. M. Islam, M. I. I. Zarif, and M. Hashem, “Attack and anomaly detection in iot sensors in iot sites using machine learning approaches,” Internet of Things, vol. 7, p. 100059, 2019.
2. S. Latif, Z. Zou, Z. Idrees, and J. Ahmad, “A novel attack detection scheme for the industrial internet of things using a lightweight random neural network,” IEEE Access, vol. 8, pp. 89 337–89 350, 2020.
3. P. Kumar, G. P. Gupta, and R. Tripathi, “A distributed ensemble design based intrusion detection system using fog computing to protect the internet of things networks,” Journal of Ambient Intelligence and Humanized Computing, vol. 12, no. 10, pp. 9555–9572, 2021.
4. D. K. Reddy, H. S. Behera, J. Nayak, P. Vijayakumar, B. Naik,and P. K. Singh, “Deep neural network based anomaly detection in internet of things network traffic tracking for the applications of future smart cities,” Transactions on Emerging Telecommunications Technologies, vol. 32, no. 7, p. e4121, 2021.
5. Y. Cheng, Y. Xu, H. Zhong, and Y. Liu, “Leveraging semisupervised hierarchical stacking temporal convolutional network for anomaly detection in iot communication,” IEEE Internet of Things Journal, vol. 8, no. 1, pp. 144–155, 2021.
6. M. M. Rashid, J. Kamruzzaman, M. M. Hassan, T. Imam, S. Wibowo, S. Gordon, and G. Fortino, “Adversarial training for deep learning-based cyberattack detection in iot-based smart city applications,” Computers & Security, p. 102783, 2022.
7. B. Weinger, J. Kim, A. Sim, M. Nakashima, N. Moustafa, and K. J. Wu, “Enhancing iot anomaly detection performance for federated learning,” Digital Communications and Networks, 2022.
8. L. Chen, Y. Li, X. Deng, Z. Liu, M. Lv, and H. Zhang, “Dual auto-encoder gan-based anomaly detection for industrial control system,” Applied Sciences, vol. 12, no. 10, p. 4986, 2022.

9. I. Mukherjee, N. K. Sahu, and S. K. Sahana, “Simulation and modeling for anomaly detection in iot network using machine learning,” International Journal of Wireless Information Networks, pp. 1–17, 2023.
10. N. Amraoui and B. Zouari, “Anomalous behavior detection based approach for authenticating smart home system users,” International Journal of Information Security, vol. 21, no. 3, pp. 611–636, 2022.
11. S. Lysenko, K. Bobrovnikova, V. Kharchenko, and O. Savenko, “Iot multi-vector cyberattack detection based on machine learning algorithms: Traffic features analysis, experiments, and efficiency,” Algorithms, vol. 15, no. 7, p. 239, 2022.
12. K. F. Hassan and M. E. Manaa, “Detection and mitigation of ddos attacks in internet of things using a fog computing hybrid approach,” Bulletin of Electrical Engineering and Informatics, vol. 11, no. 3, 2022.
13. R. V. Mendonc¸a, J. C. Silva, R. L. Rosa, M. Saadi, D. Z. Rodriguez, and A. Farouk, “A lightweight intelligent intrusion detection system for industrial internet of things using deep learning algorithms,” Expert Systems, vol. 39, no. 5, p. e12917, 2022.
14. O. A. Wahab, “Intrusion detection in the iot under data and concept drifts: Online deep learning approach,” IEEE Internet of Things Journal, 2022.
15. T.-T.-H. Le, H. Kim, H. Kang, and H. Kim, “Classification and explanation for intrusion detection system based on ensemble trees and shap method,” Sensors, vol. 22, no. 3, p. 1154, 2022.
16. M. Shobana, C. Shanmuganathan, N. P. Challa, and S. Ramya, “An optimized hybrid deep neural network architecture for intrusion detection in real-time iot networks,” Transactions on Emerging Telecommunications Technologies, p. e4609, 2022.
17. M.-O. Pahl and F.-X. Aubet, “All eyes on you: Distributed multi-dimensional iot microservice anomaly detection,” in 2018 14th International Conference on Network and Service Management (CNSM). IEEE, 2018, pp. 72–80.
18. F. Aubet and M. Pahl, “Ds2os traffic traces,” 2018. [Online]. Available: https://www.kaggle.com/datasets/francoisxa/ds2ostrafostraffic
19. S. Jadhav, H. He, and K. Jenkins, “Information gain directed genetic algorithm wrapper feature selection for credit rating,” Applied Soft Computing, vol. 69, pp. 541–553, 2018.
20. N. Japkowicz and M. Shah, Evaluating learning algorithms: a classification perspective. Cambridge University Press, 2011.
21. T. R. Patil, “Mrs. ss sherekar,” performance analysis of j48 and j48 classification algorithm for data classification”,” International Journal of Computer Science And Applications, vol. 6, no. 2, 2013.
22. X. Deng, Q. Liu, Y. Deng, and S. Mahadevan, “An improved method to construct basic probability assignment based on the confusion matrix for classification problem,” Information Sciences, vol. 340, pp. 250–261, 2016.
23. D. K. Reddy, H. S. Behera, J. Nayak, P. Vijayakumar, B. Naik et al., “Deep neural network based anomaly detection in internet of things network traffic tracking for the applications of future smart cities,” Transactions on Emerging Telecommunications Technologies, pp. 1–26, 2020.
24. P. K. Yadav and A. Kumar, "Analysis of Machine Learning Model for Anomaly and Attack Detection in IoT Devices," 2022 4th International Conference on Inventive Research in Computing Applications (ICIRCA), Coimbatore, India, 2022, pp. 387-392, doi: 10.1109/ICIRCA54612.2022.9985703.
25. R. Kushwah and R. Garg, "Anomaly Detection in IOT Site Using CatBoost," 2023 3rd Asian Conference on Innovation in Technology (ASIANCON), Ravet IN, India, 2023, pp. 1-6, doi: 10.1109/ASIANCON58793.2023.10269881.
26. J. T P, P. K, A. Paul, R. R. Chandran and P. P. Menon, "A Hybrid Machine Learning Approach to Anomaly Detection in Industrial IoT," 2023 3rd International Conference on Advances in Computing, Communication, Embedded and Secure Systems (ACCESS), Kalady, Ernakulam, India, 2023, pp. 32-36, doi: 10.1109/ACCESS57397.2023.10199711.