research-article

Precise Correlation Extraction for IoT Fault Detection With Concurrent Activities

Authors:

Hanjun KimAuthors Info & Claims

ACM Transactions on Embedded Computing Systems (TECS), Volume 20, Issue 5s

Article No.: 94, Pages 1 - 21

https://doi.org/10.1145/3477025

Published: 22 September 2021 Publication History

Abstract

In the Internet of Things (IoT) environment, detecting a faulty device is crucial to guarantee the reliable execution of IoT services. To detect a faulty device, existing schemes trace a series of events among IoT devices within a certain time window, extract correlations among them, and find a faulty device that violates the correlations. However, if a few users share the same IoT environment, since their concurrent activities make non-correlated devices react together in the same time window, the existing schemes fail to detect a faulty device without differentiating the concurrent activities. To correctly detect a faulty device in the multiple concurrent activities, this work proposes a new precise correlation extraction scheme, called PCoExtractor. Instead of using a time window, PCoExtractor continuously traces the events, removes unrelated device statuses that inconsistently react for the same activity, and constructs fine-grained correlations. Moreover, to increase the detection precision, this work newly defines a fine-grained correlation representation that reflects not only sensor values and functionalities of actuators but also their transitions and program states such as contexts. Compared to existing schemes, PCoExtractor detects and identifies 40.06% more faults for 4 IoT services with concurrent activities of 12 users while reducing 80.3% of detection and identification times.

References

[1]

AllJoyn. 2018. AllJoyn. Retrieved April 20, 2020 fromhttps://github.com/alljoyn/alljoyn.github.com/wiki.

[2]

Manos Antonakakis, Tim April, Michael Bailey, Matt Bernhard, Elie Bursztein, Jaime Cochran, Zakir Durumeric, J. Alex Halderman, Luca Invernizzi, Michalis Kallitsis, Deepak Kumar, Chaz Lever, Zane Ma, Joshua Mason, Damian Menscher, Chad Seaman, Nick Sullivan, Kurt Thomas, and Yi Zhou. 2017. Understanding the Mirai Botnet. In 26th USENIX Security Symposium (USENIX Security 17). USENIX Association, Vancouver, BC, 1093–1110.

Digital Library

[3]

Masoud Saeida Ardekani, Rayman Preet Singh, Nitin Agrawal, Douglas B. Terry, and Riza O. Suminto. 2017. Rivulet: A fault-tolerant platform for smart-home applications. In Proceedings of the 18th ACM/IFIP/USENIX Middleware Conference (Las Vegas, Nevada) (Middleware’17). ACM, New York, NY, USA, 41–54. https://doi.org/10.1145/3135974.3135988

Digital Library

[4]

G. Azkune and A. Almeida. 2018. A scalable hybrid activity recognition approach for intelligent environments. IEEE Access 6 (2018), 41745–41759. https://doi.org/10.1109/ACCESS.2018.2861004

[5]

Simon Birnbach, Simon Eberz, and Ivan Martinovic. 2019. Peeves: Physical event verification in smart homes. In Proceedings of the 2019 ACM SIGSAC Conference on Computer and Communications Security (London, United Kingdom) (CCS’19). Association for Computing Machinery, New York, NY, USA, 1455–1467. https://doi.org/10.1145/3319535.3354254

Digital Library

[6]

J. Choi, H. Jeoung, J. Kim, Y. Ko, W. Jung, H. Kim, and J. Kim. 2018. Detecting and identifying faulty IoT devices in smart home with context extraction. In 2018 48th Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN). 610–621. https://doi.org/10.1109/DSN.2018.00068

[7]

G. Civitarese, C. Bettini, T. Sztyler, D. Riboni, and H. Stuckenschmidt. 2018. NECTAR: Knowledge-based collaborative active learning for activity recognition. In 2018 IEEE International Conference on Pervasive Computing and Communications (PerCom). 1–10. https://doi.org/10.1109/PERCOM.2018.8444590

[8]

CommunitySmartapps 2021. SmartThings Smartapps from Community. Retrieved August 1, 2020 fromhttps://community.smartthings.com.

[9]

D. J. Cook, A. S. Crandall, B. L. Thomas, and N. C. Krishnan. 2013. CASAS: A smart home in a box. Computer 46, 7 (July 2013), 62–69. https://doi.org/10.1109/MC.2012.328

Digital Library

[10]

E. Fernandes, J. Jung, and A. Prakash. 2016. Security analysis of emerging smart home applications. In 2016 IEEE Symposium on Security and Privacy (SP). 636–654. https://doi.org/10.1109/SP.2016.44

[11]

N. Ghosh, S. Chandra, V. Sachidananda, and Y. Elovici. 2019. SoftAuthZ: A context-aware, behavior-based authorization framework for home IoT. IEEE Internet of Things Journal 6, 6 (Dec 2019), 10773–10785. https://doi.org/10.1109/JIOT.2019.2941767

[12]

Shuo Guo, Ziguo Zhong, and Tian He. 2009. FIND: Faulty node detection for wireless sensor networks. In Proceedings of the 7th ACM Conference on Embedded Networked Sensor Systems (Berkeley, California) (SenSys’09). ACM, New York, NY, USA, 253–266.

Digital Library

[13]

Jin Heo and Tarek Abdelzaher. 2009. Adaptguard: Guarding adaptive systems from instability. 77–86. https://doi.org/10.1145/1555228.1555256

Digital Library

[14]

Timothy W. Hnat, Vijay Srinivasan, Jiakang Lu, Tamim I. Sookoor, Raymond Dawson, John Stankovic, and Kamin Whitehouse. 2011. The hitchhiker’s guide to successful residential sensing deployments. In Proceedings of the 9th ACM Conference on Embedded Networked Sensor Systems (Seattle, Washington) (SenSys’11). Association for Computing Machinery, New York, NY, USA, 232–245.

Digital Library

[15]

Casen Hunger, Lluis Vilanova, Charalampos Papamanthou, Yoav Etsion, and Mohit Tiwari. 2018. DATS - Data containers for web applications. In Proceedings of the Twenty-Third International Conference on Architectural Support for Programming Languages and Operating Systems (Williamsburg, VA, USA) (ASPLOS’18). ACM, New York, NY, USA, 722–736. https://doi.org/10.1145/3173162.3173213

Digital Library

[16]

IFTTT 2021. IFTTT. Retrieved May 10, 2019 fromhttps://ifttt.com/.

[17]

IoTivity 2021. IoTivity. Retrieved May 10, 2019 fromhttps://iotivity.org.

[18]

Yunhan Jack Jia, Qi Alfred Chen, Shiqi Wang, Amir Rahmati, Earlence Fernandes, Z. Morley Mao, and Atul Prakash. 2017. ContexIoT: Towards providing contextual integrity to appified iot platforms. In 21st Network and Distributed Security Symposium.

[19]

Krasimira Kapitanova, Enamul Hoque, John A. Stankovic, Kamin Whitehouse, and Sang H. Son. 2012. Being SMART about failures: Assessing repairs in SMART homes. In Proceedings of the 2012 ACM Conference on Ubiquitous Computing (Pittsburgh, Pennsylvania) (UbiComp’12). ACM, New York, NY, USA, 51–60. https://doi.org/10.1145/2370216.2370225

Digital Library

[20]

Palanivel A. Kodeswaran, Ravi Kokku, Sayandeep Sen, and Mudhakar Srivatsa. 2016. Idea: A system for efficient failure management in smart IoT environments. In Proceedings of the 14th Annual International Conference on Mobile Systems, Applications, and Services (Singapore, Singapore) (MobiSys’16). ACM, New York, NY, USA, 43–56. https://doi.org/10.1145/2906388.2906406

Digital Library

[21]

Deepak Kumar, Kelly Shen, Benton Case, Deepali Garg, Galina Alperovich, Dmitry Kuznetsov, Rajarshi Gupta, and Zakir Durumeric. 2019. All things considered: An analysis of IoT devices on home networks. In 28th USENIX Security Symposium (USENIX Security 19). USENIX Association, Santa Clara, CA, 1169–1185.

Digital Library

[22]

Chris Lattner and Vikram Adve. 2004. LLVM: A compilation framework for lifelong program analysis & transformation. In Proceedings of the International Symposium on Code Generation and Optimization: Feedback-directed and Runtime Optimization (Palo Alto, California) (CGO’04). IEEE Computer Society, Washington, DC, USA, 75–86. http://dl.acm.org/citation.cfm?id=977395.977673.

Digital Library

[23]

J. Liono, F. D. Salim, N. van Berkel, V. Kostakos, and A. K. Qin. 2019. Improving experience sampling with multi-view user-driven annotation prediction. In 2019 IEEE International Conference on Pervasive Computing and Communications (PerCom). 1–11. https://doi.org/10.1109/PERCOM.2019.8767394

[24]

M. Miettinen, S. Marchal, I. Hafeez, N. Asokan, A. Sadeghi, and S. Tarkoma. 2017. IoT SENTINEL: Automated Device-type identification for security enforcement in IoT. In 2017 IEEE 37th International Conference on Distributed Computing Systems (ICDCS). 2177–2184. https://doi.org/10.1109/ICDCS.2017.283

[25]

S. Munir and J. A. Stankovic. 2014. FailureSense: Detecting sensor failure using electrical appliances in the home. In 2014 IEEE 11th International Conference on Mobile Ad Hoc and Sensor Systems. 73–81. https://doi.org/10.1109/MASS.2014.16

Digital Library

[26]

T. D. Nguyen, S. Marchal, M. Miettinen, H. Fereidooni, N. Asokan, and A. Sadeghi. 2019. DÏoT: A federated self-learning anomaly detection system for IoT. In 2019 IEEE 39th International Conference on Distributed Computing Systems (ICDCS). 756–767. https://doi.org/10.1109/ICDCS.2019.00080

[27]

M. Norris, B. Celik, P. Venkatesh, S. Zhao, P. McDaniel, A. Sivasubramaniam, and G. Tan. 2020. IoTRepair: Systematically addressing device faults in commodity IoT. In 2020 IEEE/ACM Fifth International Conference on Internet-of-Things Design and Implementation (IoTDI). 142–148.

[28]

openHAB 2021. openHAB. Retrieved August 1, 2020 fromhttps://www.openhab.org.

[29]

PublicSmartapps 2021. SmartThings Public Smartapps. Retrieved August 1, 2020 fromhttps://github.com/SmartThingsCommunity/SmartThingsPublic.

[30]

Daniele Riboni, Timo Sztyler, Gabriele Civitarese, and Heiner Stuckenschmidt. 2016. Unsupervised recognition of interleaved activities of daily living through ontological and probabilistic reasoning. In Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing (Heidelberg, Germany) (UbiComp’16). Association for Computing Machinery, New York, NY, USA, 1–12. https://doi.org/10.1145/2971648.2971691

Digital Library

[31]

Roei Schuster, Vitaly Shmatikov, and Eran Tromer. 2018. Situational access control in the internet of things. In Proceedings of the 2018 ACM SIGSAC Conference on Computer and Communications Security (Toronto, Canada) (CCS’18). ACM, New York, NY, USA, 1056–1073. https://doi.org/10.1145/3243734.3243817

Digital Library

[32]

Chenguang Shen, Rayman Preet Singh, Amar Phanishayee, Aman Kansal, and Ratul Mahajan. 2016. Beam: Ending monolithic applications for connected devices. In 2016 USENIX Annual Technical Conference (USENIX ATC 16). USENIX Association, Denver, CO, 143–157. https://www.usenix.org/conference/atc16/technical-sessions/presentation/shen.

Digital Library

[33]

Amit Kumar Sikder, Hidayet Aksu, and A. Selcuk Uluagac. 2017. 6thSense: A Context-aware sensor-based attack detector for smart devices. In 26th USENIX Security Symposium (USENIX Security 17). USENIX Association, Vancouver, BC, 397–414. https://www.usenix.org/conference/usenixsecurity17/technical-sessions/presentation/sikder.

Digital Library

[34]

Amit Kumar Sikder, Leonardo Babun, Hidayet Aksu, and A. Selcuk Uluagac. 2019. Aegis: A context-aware security framework for smart home systems. In Proceedings of the 35th Annual Computer Security Applications Conference (San Juan, Puerto Rico) (ACSAC’19). Association for Computing Machinery, New York, NY, USA, 28–41. https://doi.org/10.1145/3359789.3359840

Digital Library

[35]

SmartThings 2020. Samsung SmartThings. Retrieved May 10, 2019 fromhttp://www.smartthings.com.

[36]

Saleh Soltan, Prateek Mittal, and H. Vincent Poor. 2018. BlackIoT: IoT botnet of high wattage devices can disrupt the power grid. In 27th USENIX Security Symposium (USENIX Security 18). USENIX Association, Baltimore, MD, 15–32.

Digital Library

[37]

Shen Su, Yanbin Sun, Xiangsong Gao, Jing Qiu, and Zhihong Tian. 2019. A Correlation-change based feature selection method for IoT equipment anomaly detection. Applied Sciences 9, 3 (2019). https://doi.org/10.3390/app9030437

[38]

X. Su, P. Li, J. Riekki, X. Liu, J. Kiljander, J. Soininen, C. Prehofer, H. Flores, and Y. Li. 2018. Distribution of semantic reasoning on the Edge of internet of things. In 2018 IEEE International Conference on Pervasive Computing and Communications (PerCom). 1–9. https://doi.org/10.1109/PERCOM.2018.8444596

[39]

Tao Gu, Zhanqing Wu, Xianping Tao, H. K. Pung, and Jian Lu. 2009. epSICAR: An emerging patterns based approach to sequential, interleaved and concurrent activity recognition. In 2009 IEEE International Conference on Pervasive Computing and Communications. 1–9. https://doi.org/10.1109/PERCOM.2009.4912776

Digital Library

[40]

T. L. M. van Kasteren, G. Englebienne, and B. J. A. Kröse. 2011. Human Activity Recognition from Wireless Sensor Network Data: Benchmark and Software. Atlantis Press, Paris, 165–186. https://doi.org/10.2991/978-94-91216-05-3_8

[41]

Tao Wang, Wenbo Zhang, Jun Wei, and Hua Zhong. 2015. Fault detection for cloud computing systems with correlation analysis. In 2015 IFIP/IEEE International Symposium on Integrated Network Management (IM). 652–658. https://doi.org/10.1109/INM.2015.7140351

[42]

Xueqiang Wang, Yuqiong Sun, Susanta Nanda, and XiaoFeng Wang. 2019. Looking from the Mirror: Evaluating IoT Device security through mobile companion apps. In 28th USENIX Security Symposium (USENIX Security 19). USENIX Association, Santa Clara, CA, 1151–1167.

Digital Library

[43]

WSU CASAS Datasets 2020. WSU CASAS Datasets. Retrieved August 1, 2020 fromhttp://casas.wsu.edu/datasets/.

[44]

J. Ye. 2018. SLearn: Shared learning human activity labels across multiple datasets. In 2018 IEEE International Conference on Pervasive Computing and Communications (PerCom). 1–10. https://doi.org/10.1109/PERCOM.2018.8444594

[45]

J. Ye, G. Stevenson, and S. Dobson. 2015. Fault detection for binary sensors in smart home environments. In 2015 IEEE International Conference on Pervasive Computing and Communications (PerCom). 20–28. https://doi.org/10.1109/PERCOM.2015.7146505

[46]

J. Ye, G. Stevenson, and S. Dobson. 2015. Using temporal correlation and time series to detect missing activity-driven sensor events. In 2015 IEEE International Conference on Pervasive Computing and Communication Workshops (PerCom Workshops). 44–49. https://doi.org/10.1109/PERCOMW.2015.7133991

[47]

Juan Ye, Graeme Stevenson, and Simon Dobson. 2016. Detecting abnormal events on binary sensors in smart home environments. Pervasive and Mobile Computing 33 (2016), 32–49. https://doi.org/10.1016/j.pmcj.2016.06.012

[48]

Pushe Zhao, Masaru Kurihara, Junichi Tanaka, Tojiro Noda, Shigeyoshi Chikuma, and Tadashi Suzuki. 2017. Advanced correlation-based anomaly detection method for predictive maintenance. In 2017 IEEE International Conference on Prognostics and Health Management (ICPHM). 78–83. https://doi.org/10.1109/ICPHM.2017.7998309

[49]

S. Zhou, K. Lin, J. Na, C. Chuang, and C. Shih. 2015. Supporting service adaptation in fault tolerant internet of things. In 2015 IEEE 8th International Conference on Service-Oriented Computing and Applications (SOCA). 65–72. https://doi.org/10.1109/SOCA.2015.38

Digital Library

[50]

Wei Zhou, Yan Jia, Yao Yao, Lipeng Zhu, Le Guan, Yuhang Mao, Peng Liu, and Yuqing Zhang. 2019. Discovering and understanding the security hazards in the interactions between ioT devices, mobile apps, and clouds on smart home platforms. In 28th USENIX Security Symposium (USENIX Security 19). USENIX Association, Santa Clara, CA, 1133–1150.

Digital Library

Cited By

Liu MZhang LXu WJiang SKong F(2024)CPSim: Simulation Toolbox for Security Problems in Cyber-Physical SystemsACM Transactions on Design Automation of Electronic Systems10.1145/3674904Online publication date: 25-Jun-2024
https://dl.acm.org/doi/10.1145/3674904
Menard CLohstroh MBateni SChorlian MDeng ADonovan PFournier CLin SSuchert FTanneberger TKim HCastrillon JLee E(2023)High-performance Deterministic Concurrency Using Lingua FrancaACM Transactions on Architecture and Code Optimization10.1145/361768720:4(1-29)Online publication date: 26-Oct-2023
https://dl.acm.org/doi/10.1145/3617687
Lee EAkella RBateni SLin SLohstroh MMenard C(2023)Consistency vs. Availability in Distributed Cyber-Physical SystemsACM Transactions on Embedded Computing Systems10.1145/360911922:5s(1-24)Online publication date: 9-Sep-2023
https://dl.acm.org/doi/10.1145/3609119

Index Terms

Precise Correlation Extraction for IoT Fault Detection With Concurrent Activities
1. Hardware
  1. Emerging technologies
    1. Analysis and design of emerging devices and systems
      1. Emerging languages and compilers
2. Security and privacy
  1. Intrusion/anomaly detection and malware mitigation

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Published In

cover image ACM Transactions on Embedded Computing Systems

ACM Transactions on Embedded Computing Systems Volume 20, Issue 5s

Special Issue ESWEEK 2021, CASES 2021, CODES+ISSS 2021 and EMSOFT 2021

October 2021

1367 pages

ISSN:1539-9087

EISSN:1558-3465

DOI:10.1145/3481713

Editor:
Tulika Mitra
National University of Singapore, Singapore

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Copyright © 2021 Copyright held by the owner/author(s). Publication rights licensed to ACM.

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ACM Journals for the Design of Smart and Connected Systems

Publication History

Published: 22 September 2021

Accepted: 01 July 2021

Revised: 01 June 2021

Received: 01 April 2021

Published in TECS Volume 20, Issue 5s

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Institute of Information and Communication Technology Planning and Evaluation (IITP)
Ministry of Science and ICT
Samsung Electronics

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Cited By

Liu MZhang LXu WJiang SKong F(2024)CPSim: Simulation Toolbox for Security Problems in Cyber-Physical SystemsACM Transactions on Design Automation of Electronic Systems10.1145/3674904Online publication date: 25-Jun-2024
https://dl.acm.org/doi/10.1145/3674904
Menard CLohstroh MBateni SChorlian MDeng ADonovan PFournier CLin SSuchert FTanneberger TKim HCastrillon JLee E(2023)High-performance Deterministic Concurrency Using Lingua FrancaACM Transactions on Architecture and Code Optimization10.1145/361768720:4(1-29)Online publication date: 26-Oct-2023
https://dl.acm.org/doi/10.1145/3617687
Lee EAkella RBateni SLin SLohstroh MMenard C(2023)Consistency vs. Availability in Distributed Cyber-Physical SystemsACM Transactions on Embedded Computing Systems10.1145/360911922:5s(1-24)Online publication date: 9-Sep-2023
https://dl.acm.org/doi/10.1145/3609119

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