Abstract
This paper presents a security framework that continuously authenticates smart homes users in order to make sure that only authorized ones are allowed to control their Internet of Things (IoT) devices while, at the same time, preventing them in case of performing abnormal and dangerous control actions. To do so, control commands under normal operation of both users and devices, are first implicitly traced to build a One Class Support Vector Machine (OCSVM) model as a baseline from which deviations (i.e., anomalous commands) should be detected and rejected, while normal observations (i.e., normal commands) should be considered as legitimate and allowed to be executed. Experiments conducted on our artificial datasets show the efficiency of such user behavior-based approach achieving at least 95.29% and 4.12% of True Positive (TP) and False Positive (FP) rates, respectively.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Crocker, P.: Smarter authentication makes mobile experiences more secure, user friendly. https://www.computerworld.com/article/3181710/mobile-wireless/smarter-authentication-makes-mobile-experiences-more-secure-user-friendly.html
Garcia-Font, V., Garrigues, C., Rifà -Pous, H.: A comparative study of anomaly detection techniques for smart city wireless sensor networks. Sens. J. 16, 868 (2016)
Haim, B., Menahem, E., Wolfsthal, Y., Meenan, C.: Visualizing insider threats: an effective interface for security analytics. In: 22nd ACM International Conference on Intelligent User Interfaces Companion, pp. 39–42 (2017)
Liao, Q., Li, H., Kang, S., Liu, C.: Application layer DDoS attack detection using cluster with label based on sparse vector decomposition and rhythm matching. Secur. Commun. Netw. J 8, 3111–3120 (2015)
Mathew, S., Petropoulos, M., Ngo, H.Q., Upadhyaya, S.: A data-centric approach to insider attack detection in database systems. In: 13th International Workshop on Recent Advances in Intrusion Detection, pp. 382–401, Berlin (2010)
Mazzawi, H., Dalal, G., Rozenblat, D., et al.: Anomaly detection in large databases using behavioral patterning. In: 33rd International Conference on Data Engineering, San Diego, pp. 1140–1149 (2017)
Najafabadi, M.M., Khoshgoftaar, T.M., Calvert, C., Kemp, C.: User behavior anomaly detection for application layer DDoS attacks. In: 18th International Conference on Information Reuse and Integration, San Diego, pp. 154–161 (2017)
Rath, A.T., Colin, J.N.: Strengthening access control in case of compromised accounts in smart home. In: 13th International Conference on Wireless and Mobile Computing, Networking and Communications, pp. 1–8, Rome (2017)
Ruan, X., Wu, Z., Wang, H., Jajodia, S.: Profiling online social behaviors for compromised account detection. Trans. Inf. Forensics Secur. J 11, 176–187 (2016)
Scholkopf, B., Platt, J., Taylor, J.S., et al.: Estimating the support of a high-dimensional distribution. Neural Comput. J 13, 1443–1471 (2001)
Viswanath, B., Bashir, M.A., Crovella, M., et al.: Towards detecting anomalous user behavior in online social networks. In: 23rd USENIX Security Symposium, San Diego, pp. 223–238 (2014)
Wang, C., Yang, B.: Composite Behavioral Modeling for Identity Theft Detection in Online Social Networks (2018). arXiv preprint arXiv:1801.06825
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Amraoui, N., Besrour, A., Ksantini, R., Zouari, B. (2020). Implicit and Continuous Authentication of Smart Home Users. In: Barolli, L., Takizawa, M., Xhafa, F., Enokido, T. (eds) Advanced Information Networking and Applications. AINA 2019. Advances in Intelligent Systems and Computing, vol 926. Springer, Cham. https://doi.org/10.1007/978-3-030-15032-7_103
Download citation
DOI: https://doi.org/10.1007/978-3-030-15032-7_103
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-15031-0
Online ISBN: 978-3-030-15032-7
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)