AI Chat Paper
Note: Please note that the following content is generated by AMiner AI. SciOpen does not take any responsibility related to this content.
{{lang === 'zh_CN' ? '文章概述' : 'Summary'}}
{{lang === 'en_US' ? '中' : 'Eng'}}
Chat more with AI
Powered by AMiner. Clicking this button will take you away from SciOpen.
Home Big Data Mining and Analytics Article
PDF (4.1 MB)
Cite
EndNote(RIS) BibTeX
Collect
Submit Manuscript AI Chat Paper
Show Outline
Outline
Show full outline
Hide outline
Outline
Show full outline
Hide outline
Open Access

EScope: Effective Event Validation for IoT Systems Based on State Correlation

School of Cyber Science and Technology, Beihang University, Beijing 100191, China
Department of Computer Science, Texas Christian University, Fort Worth, TX 76129, USA
Beihang Hangzhou Innovation Institute Yuhang, Hangzhou 310023, China
Show Author Information

Abstract

Typical Internet of Things (IoT) systems are event-driven platforms, in which smart sensing devices sense or subscribe to events (device state changes), and react according to the preconfigured trigger-action logic, as known as, automation rules. "Events" are essential elements to perform automatic control in an IoT system. However, events are not always trustworthy. Sensing fake event notifications injected by attackers (called event spoofing attack) can trigger sensitive actions through automation rules without involving authorized users. Existing solutions verify events via "event fingerprints" extracted by surrounding sensors. However, if a system has homogeneous sensors that have strong correlations among them, traditional threshold-based methods may cause information redundancy and noise amplification, consequently, decreasing the checking accuracy. Aiming at this, in this paper, we propose "EScope" , an effective event validation approach to check the authenticity of system events based on device state correlation. EScope selects informative and representative sensors using an Neural-Network-based (NN-based) sensor selection component and extracts a verification sensor set for event validation. We evaluate our approach using an existing dataset provided by Peeves. The experiment results demonstrate that EScope achieves an average 67% sensor amount reduction on 22 events compared with the existing work, and increases the event spoofing detection accuracy.

Keywords

Internet of Things (IoT)event spoofingevent fingerprintcorrelation analysis

References

[1]
M. Azrour, J. Mabrouki, A. Guezzaz, and Y. Farhaoui, New enhanced authentication protocol for internet of things, Big Data Mining and Analytics, vol. 4, no. 1, pp. 1–9, 2021.
Crossref Google Scholar
[2]
J. Mabrouki, M. Azrour, D. Dhiba, Y. Farhaoui, and S. El Hajjaji, IoT-based data logger for weather monitoring using arduino-based wireless sensor networks with remote graphical application and alerts, Big Data Mining and Analytics, vol. 4, no. 1, pp. 25–32, 2021.
Crossref Google Scholar
[3]
J. Mabrouki, M. Azrour, G. Fattah, D. Dhiba, and S. El Hajjaji, Intelligent monitoring system for biogas detection based on the internet of things: Mohammedia, morocco city landfill case, Big Data Mining and Analytics, vol. 4, no. 1, pp. 10–17, 2021.
Crossref Google Scholar
[4]
Samsung, One simple home system. A world of possibilities, https://www.smartthings.com, 2022.
[5]
HomeAssistant, Home assistant, https://www.homeassistant.io, 2022.
[6]
C. Aranzazu-Suescun and M. Cardei, Distributed algorithms for event reporting in mobile-sink WSNs for internet of things, Tsinghua Science and Technology, vol. 22, no. 4, pp. 413–426, 2017.
Crossref Google Scholar
[7]
C. Fu, Q. Zeng, and X. Du, HAWatcher: Semantics-aware anomaly detection for appified smart homes, in Proc. 30th USENIX Security Symp., Virtual Event, 2021, pp. 4223–4240.
Google Scholar
[8]
Samsung smart things, Mobile device presence update delay, https://community.smartthings.com/t/mobiledevice-presence-update-delay/98672, 2022.
Crossref
[9]
L. Russell, Wireless security monitoring versus a cellular jammer, https://www.home-security-systemsanswers.com/wireless-security-monitoring.html, 2022.
[10]
E. Fernandes, J. Jung, and A. Prakash, Security analysis of emerging smart home applications, in Proc. of the 2016 IEEE Symp. Security and Privacy (SP), San Jose, CA, USA, 2016, pp. 636–654.
Crossref Google Scholar
[11]
Z. Cai and Z. He, Trading private range counting over big IoT data, in Proc. of the 2019 IEEE 39th Int. Conf. Distributed Computing Systems (ICDCS), Dallas, TX, USA, 2019, pp. 144–153.
Crossref Google Scholar
[12]
E. Ronen, A. Shamir, A. O. Weingarten, and C. O’Flynn, IoT goes nuclear: Creating a ZigBee chain reaction, in Proc. of the 2017 IEEE Symp. Security and Privacy (SP), San Jose, CA, USA, 2017, pp. 195–212.
Crossref Google Scholar
[13]
S. Birnbach, S. Eberz, and I. Martinovic, Peeves: Physical event verification in smart homes, in Proc. 2019 ACM SIGSAC Conf. Computer and Communications Security, London, UK, 2019, pp. 1455–1467.
Crossref Google Scholar
[14]
A. Acar, H. Fereidooni, T. Abera, A. K. Sikder, M. Miettinen, H. Aksu, M. Conti, A. R. Sadeghi, and S. Uluagac, Peek-a-boo: I see your smart home activities, even encrypted!, in Proc. 13th ACM Conf. Security and Privacy in Wireless and Mobile Networks, Linz, Austria, 2020, pp. 207–218.
Crossref Google Scholar
[15]
X. Zheng and Z. Cai, Privacy-preserved data sharing towards multiple parties in industrial IoTs, IEEE J. Select. Areas Commun., vol. 38, no. 5, pp. 968–979, 2020.
Crossref Google Scholar
[16]
W. Zhang, Y. Meng, Y. Liu, X. Zhang, Y. Zhang, and H. Zhu, HoMonit: Monitoring smart home apps from encrypted traffic, in Proc. 2018 ACM SIGSAC Conf. Computer and Communications Security, Toronto, Canada, 2018, pp. 1074–1088.
Crossref Google Scholar
[17]
R. Trimananda, J. Varmarken, A. Markopoulou, and B. Demsky, Packet-level signatures for smart home devices, presented at the Network and Distributed Systems Security (NDSS) Symp., San Diego, CA, USA, 2020.
Crossref Google Scholar
[18]
G. Laput, Y. Zhang, and C. Harrison, Synthetic sensors: Towards general-purpose sensing, in Proc. 2017 CHI Conf. Human Factors in Computing Systems, Denver, CO, USA, 2017, pp. 3986–3999.
Crossref Google Scholar
[19]
Y. Liang, Z. Cai, J. Yu, Q. Han, and Y. Li, Deep learning based inference of private information using embedded sensors in smart devices, IEEE Netw., vol. 32, no. 4, pp. 8–14, 2018.
Crossref Google Scholar
[20]
R. Yasaei, F. Hernandez, and M. A. Al Faruque, IoT-CAD: Context-aware adaptive anomaly detection in IoT systems through sensor association, in Proc. of the 2020 IEEE/ACM Int. Conf. Computer Aided Design (ICCAD), San Diego, CA, USA, 2020, pp. 1–9.
Crossref Google Scholar
[21]
A. Guezzaz, Y. Asimi, M. Azrour, and A. Asimi, Mathematical validation of proposed machine learning classifier for heterogeneous traffic and anomaly detection, Big Data Mining and Analytics, vol. 4, no. 1, pp. 18–24, 2021.
Crossref Google Scholar
[22]
Z. Cai and X. Zheng, A private and efficient mechanism for data uploading in smart cyber-physical systems, IEEE Trans. Netw. Sci. Eng., vol. 7, no. 2, pp. 766–775, 2020.
Crossref Google Scholar
[23]
C. Debes, A. Merentitis, S. Sukhanov, M. Niessen, N. Frangiadakis, and A. Bauer, Monitoring activities of daily living in smart homes: Understanding human behavior, IEEE Signal Process. Mag., vol. 33, no. 2, pp. 81–94, 2016.
Crossref Google Scholar
[24]
K. Islam, W. Shen, and X. Wang, Security and privacy considerations for wireless sensor networks in smart home environments, in Proc. 2012 IEEE 16th Int. Conf. Computer Supported Cooperative Work in Design (CSCWD), Wuhan, China, 2012, pp. 626–633.
Crossref Google Scholar
[25]
X. Zheng, Z. Cai, and Y. Li, Data linkage in smart internet of things systems: A consideration from a privacy perspective, IEEE Commun. Mag., vol. 56, no. 9, pp. 55–61, 2018.
Crossref Google Scholar
[26]
V. Sivaraman, D. Chan, D. Earl, and R. Boreli, Smart-phones attacking smart-homes, in Proc. 9th ACM Conf. Security & Privacy in Wireless and Mobile Networks, Darmstadt, Germany, 2016, pp. 195–200.
Crossref Google Scholar
[27]
T. Gu, Z. Fang, A. Abhishek, H. Fu, P. Hu, and P. Mohapatra, IoTGaze: IoT security enforcement via wireless context analysis, in Proc. of the IEEE INFOCOM 2020-IEEE Conf. Computer Communications, Toronto, Canada, 2020, pp. 884–893.
Crossref Google Scholar
[28]
J. Wang, S. Hao, R. Wen, B. Zhang, L. Zhang, H. Hu, and R. Lu, IoT-praetor: Undesired behaviors detection for IoT devices, IEEE Int. Things J., vol. 8, no. 2, pp. 927–940, 2021.
Crossref Google Scholar
[29]
F. L. Coman, K. M. Malarski, M. N. Petersen, and S. Ruepp, Security issues in internet of things: Vulnerability analysis of LoRaWAN, Sigfox and NB-IoT, in Proc. of the 2019 Global IoT Summit (GIoTS), Aarhus, Denmark, 2019, pp. 1–6.
Crossref Google Scholar
[30]
R. Tibshirani, Regression shrinkage and selection via the lasso, J. Roy. Stat. Soc.: Ser. B (Methodol.), vol. 58, no. 1, pp. 267–288, 1996.
Crossref Google Scholar
[31]
Y. Li, C. Y. Chen, and W. W. Wasserman, Deep feature selection: Theory and application to identify enhancers and promoters, J. Comput. Biol., vol. 23, no. 5, pp. 322–336, 2016.
Crossref Google Scholar
[32]
B. Carpenter, Lazy sparse stochastic gradient descent for regularized multinomial logistic regression, Tech. Rep., Alias-i, Inc., New York, NY, USA, 2008, pp. 1–20.
Google Scholar
[33]
Y. Tsuruoka, J. Tsujii, and S. Ananiadou, Stochastic gradient descent training for L1-regularized log-linear models with cumulative penalty, in Proc. Joint Conf. 47th Annu. Meeting of the ACL and the 4th Int. Joint Conf. Natural Language Processing of the AFNLP, Suntec, Singapore, 2009, pp. 477–485.
Crossref Google Scholar
[34]
B. C. Ross, Mutual information between discrete and continuous datasets, PLoS ONE, vol. 9, no. 2, p. e87357, 2014.
Crossref Google Scholar
[35]
W. Gao, S. Kannan, S. Oh, and P. Viswanath, Estimating mutual information for discrete-continuous mixtures, arXiv preprint arXiv: 1709.06212, 2018.
Google Scholar
[36]
Facebook, Pytorch, https://pytorch.org, 2022.
[37]
Sklearn, scikit-learn: Machine learning in python, https://scikit-learn.org/stable, 2022.
Big Data Mining and Analytics
Volume 6 Issue 2,
June 2023
Pages 218-233
DOI: 10.26599/BDMA.2022.9020034
Cite this article:
Mao J, Xu X, Lin Q, et al. EScope: Effective Event Validation for IoT Systems Based on State Correlation. Big Data Mining and Analytics, 2023, 6(2): 218-233. https://doi.org/10.26599/BDMA.2022.9020034

813

Views

63

Downloads

2

Crossref

2

Web of Science

2

Scopus

0

CSCD

Altmetrics
Received: 28 July 2022
Revised: 23 September 2022
Accepted: 26 September 2022
Published: 26 January 2023
© The author(s) 2023.

The articles published in this open access journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).
Return