February  2018, 1(1): 63-76. doi: 10.3934/mfc.2018004

Application of learning algorithms in smart home IoT system security

School of Electronic and Information Engineering, Beihang University, Beijing, China

*Corresponding author: Jian Mao

Received  October 2017 Revised  December 2017 Published  February 2018

Fund Project: The first author is supported by the National Natural Science Foundation of China (No.61402029, 61379002, 61370190, 61672083) and the Funding Project of Shanghai Key Laboratory of Integrated Administration Technologies for Information Security (No. AGK201708).

With the rapid development of Internet of Things (IoT) technologies, smart home systems are getting more and more popular in our daily life. Besides providing convenient functionality and tangible benefits, smart home systems also expose users to security risks. To enhance the functionality and the security, machine learning algorithms play an important role in a smart home ecosystem, e.g., ensuring biotechnology-based authentication and authorization, anomalous detection, etc. On the other side, attackers also treat learning algorithms as a tool, as well as a target, to exploit the security vulnerabilities in smart home systems. In this paper, we unify the system architectures suggested by the mainstream service providers, e.g., Samsung, Google, Apple, etc. Based on our proposed overall smart home system model, we investigate the application of learning algorithms in smart home IoT system security. Our study includes two angles. First, we discussed the functionality and security enhancing methods based on learning mechanisms; second, we described the security threats exposed by employing learning techniques. We also explored the potential solutions that may address the aforementioned security problems.

Citation: Jian Mao, Qixiao Lin, Jingdong Bian. Application of learning algorithms in smart home IoT system security. Mathematical Foundations of Computing, 2018, 1 (1) : 63-76. doi: 10.3934/mfc.2018004
References:
[1]

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M. R. AlamM. B. I. Reaz and M. A. M. Ali, A review of smart homes-past, present, and future, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 42 (2012), 1190-1203. doi: 10.1109/TSMCC.2012.2189204. Google Scholar

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A. MahmoudU. RührmairM. Majzoobi and F. Koushanfar, Combined modeling and side channel attacks on strong pufs, IACR Cryptology ePrint Archive, 2013 (2013), p632. Google Scholar

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S. Majumdar, Y. Jarraya, M. Oqaily, A. Alimohammadifar, M. Pourzandi, L. Wang and M. Debbabi, Leaps: Learning-based proactive security auditing for clouds, in Proceedings of European Symposium on Research in Computer Security, Springer, 2017, 265-285. doi: 10.1007/978-3-319-66399-9_15. Google Scholar

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show all references

References:
[1]

Researchers exploit zigbee security flaws that compromise security of smart homes, http://www.networkworld.com/article/2969402/microsoft-subnet/researchers-exploitzigbee-securityflaws-that-compromise-security-of-smart-homes.html.Google Scholar

[2]

M. R. AlamM. B. I. Reaz and M. A. M. Ali, A review of smart homes-past, present, and future, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 42 (2012), 1190-1203. doi: 10.1109/TSMCC.2012.2189204. Google Scholar

[3]

I. Androutsopoulos, J. Koutsias, K. V. Chandrinos, G. Paliouras and C. D. Spyropoulos, An evaluation of naive bayesian anti-spam filtering, arXiv preprint cs/0006013.Google Scholar

[4]

Apple, Apple machine learning journal, https://machinelearning.apple.com/.Google Scholar

[5]

M. BackesM. DürmuthS. GerlingM. Pinkal and C. Sporleder, Acoustic side-channel attacks on printers., Proceedings of USENIX Security symposium, (2010), 307-322. Google Scholar

[6]

A. Bassi and G. Horn, Internet of things in 2020: A roadmap for the future, European Commission: Information Society and Media, 22 (2008), 97-114. Google Scholar

[7]

V. H. Bhide and S. Wagh, I-learning iot: An intelligent self learning system for home automation using IoT, in Proceedings of 2015 International Conference on Communications and Signal Processing (ICCSP), IEEE, 2015, 1763-1767. doi: 10.1109/ICCSP.2015.7322825. Google Scholar

[8]

Z. CaiZ. HeX. Guan and Y. Li, Collective data-sanitization for preventing sensitive information inference attacks in social networks, IEEE Transactions on Dependable and Secure Computing, PP (2017), 1-1. doi: 10.1109/TDSC.2016.2613521. Google Scholar

[9]

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[10]

J. ChoiD. Shin and D. Shin, Research and implementation of the context-aware middleware for controlling home appliances, IEEE Transactions on Consumer Electronics, 51 (2005), 301-306. Google Scholar

[11]

L. Deng and X. Li, Machine learning paradigms for speech recognition: An overview, IEEE Transactions on Audio, Speech, and Language Processing, 21 (2013), 1060-1089. doi: 10.1109/TASL.2013.2244083. Google Scholar

[12]

E. Fernandes, J. Jung and A. Prakash, Security analysis of emerging smart home applications, in Proceedings of 2016 IEEE Symposium on Security and Privacy (SP), IEEE, 2016, 636-654.Google Scholar

[13]

E. Fernandes, A. Rahmati, K. Eykholt and A. Prakash, Internet of things security research: A rehash of old ideas or new intellectual challenges?, arXiv preprint, arXiv: 1705.08522.Google Scholar

[14]

P. Fogla and W. Lee, Evading network anomaly detection systems: Formal reasoning and practical techniques, in Proceedings of the 13th ACM conference on Computer and communications security, ACM, 2006, 59-68. doi: 10.1145/1180405.1180414. Google Scholar

[15]

Gartner, Gartner says 6. 4 billion connected "things" will be in use in 2016, up 30 percent from 2015, http://www.gartner.com/newsroom/id/3165317.Google Scholar

[16]

A. Greenberg, Apple's 'differential privacy' is about collecting your data-but not your data, https://www.wired.com/2016/06/apples-differential-privacy-collecting-data/.Google Scholar

[17]

K. He, X. Zhang, S. Ren and J. Sun, Deep residual learning for image recognition, in Proceedings of the IEEE conference on computer vision and pattern recognition, 2016, 770-778. doi: 10.1109/CVPR.2016.90. Google Scholar

[18]

A. Hesseldahl, A hackers-eye view of the internet of things, http://recode.net/2015/04/07/a-hackers-eye-view-of-the-internet-of-things/.Google Scholar

[19]

G. Ho, D. Leung, P. Mishra, A. Hosseini, D. Song and D. Wagner, Smart locks: Lessons for securing commodity internet of things devices, in Proceedings of the 11th ACM on Asia Conference on Computer and Communications Security, ACM, 2016, 461-472. doi: 10.1145/2897845.2897886. Google Scholar

[20]

G. HospodarB. GierlichsE. De MulderI. Verbauwhede and J. Vandewalle, Machine learning in side-channel analysis: A first study, Journal of Cryptographic Engineering, 1 (2011), 293-302. doi: 10.1007/s13389-011-0023-x. Google Scholar

[21]

D. Istrate, M. Vacher, E. Castelli and C. -P. Nguyen, Sound processing for health smart home, in Proceedings of International Conference on Smart homes and health Informatics, 2004, 41-48.Google Scholar

[22]

Y. J. Jia, Q. A. Chen, S. Wang, A. Rahmati, E. Fernandes, Z. M. Mao, A. Prakash and S. J. Unviersity, Contexiot: Towards providing contextual integrity to appified iot platforms, in Proceedings of the 21st Network and Distributed System Security Symposium (NDSS'17), 2017. doi: 10.14722/ndss.2017.23051. Google Scholar

[23]

E. B. Karbab, M. Debbabi, A. Derhab and D. Mouheb, Cypider: Building community-based cyber-defense infrastructure for android malware detection, in Proceedings of the 32nd Annual Conference on Computer Security Applications, ACM, 2016, 348-362. doi: 10.1145/2991079.2991124. Google Scholar

[24]

G. KortuemF. KawsarV. Sundramoorthy and D. Fitton, Smart objects as building blocks for the internet of things, IEEE Internet Computing, 14 (2010), 44-51. doi: 10.1109/MIC.2009.143. Google Scholar

[25]

L. Lerman, G. Bontempi and O. Markowitch, Side channel attack: An approach based on machine learning, Center for Advanced Security Research Darmstadt, 29-41.Google Scholar

[26]

Y. LiangZ. CaiQ. Han and Y. Li, Location privacy leakage through sensory data, Security and Communication Networks, 2017 (2017), 12pp. doi: 10.1155/2017/7576307. Google Scholar

[27]

D. LimJ. W. LeeB. GassendG. E. SuhM. Van Dijk and S. Devadas, Extracting secret keys from integrated circuits, IEEE Transactions on Very Large Scale Integration (VLSI) Systems, 13 (2005), 1200-1205. Google Scholar

[28]

C. Liu, Y. Cao, Y. Luo, G. Chen, V. Vokkarane and Y. Ma, Deepfood: Deep learning-based food image recognition for computer-aided dietary assessment, in Proceedings of International Conference on Smart Homes and Health Telematics, Springer, 2016, 37-48. doi: 10.1007/978-3-319-39601-9_4. Google Scholar

[29]

C. Livadas, R. Walsh, D. Lapsley and W. T. Strayer, Usilng machine learning technliques to identify botnet traffic, in Proceedings of 2006 31st IEEE Conference on Local Computer Networks, IEEE, 2006, 967-974. doi: 10.1109/LCN.2006.322210. Google Scholar

[30]

R. Lutolf, Smart home concept and the integration of energy meters into a home based system, in Proceedings of Seventh International Conference on Metering Apparatus and Tariffs for Electricity Supply, IET, 1992, 277-278.Google Scholar

[31]

A. MahmoudU. RührmairM. Majzoobi and F. Koushanfar, Combined modeling and side channel attacks on strong pufs, IACR Cryptology ePrint Archive, 2013 (2013), p632. Google Scholar

[32]

S. Majumdar, Y. Jarraya, M. Oqaily, A. Alimohammadifar, M. Pourzandi, L. Wang and M. Debbabi, Leaps: Learning-based proactive security auditing for clouds, in Proceedings of European Symposium on Research in Computer Security, Springer, 2017, 265-285. doi: 10.1007/978-3-319-66399-9_15. Google Scholar

[33]

R. McCoppin and M. Rizki, Deep learning for image classification, in SPIE Defense+ Security, International Society for Optics and Photonics, 2014, 90790T-90790T.Google Scholar

[34]

E. C. McLaughlin, Alexa, what other devices are listening to me?, http://edition.cnn.com/2017/01/12/tech/voice-technology-internet-of-things-privacy/index.html.Google Scholar

[35]

T. Oluwafemi, T. Kohno, S. Gupta and S. Patel, Experimental security analyses of nonnetworked compact fluorescent lamps: A case study of home automation security, in Proceedings of LASER, 2013, 13-24.Google Scholar

[36]

L. Pang, I. Tchoudovski, A. Bolz, M. Braecklein, K. Egorouchkina and W. Kellermann, Real time heart ischemia detection in the smart home care system, in Proceedings of 27th Annual International Conference of the Engineering in Medicine and Biology Society (IEEE-EMBS 2005), IEEE, 2006, 3703-3706. doi: 10.1109/IEMBS.2005.1617286. Google Scholar

[37]

N. Papernot, P. McDaniel, A. Sinha and M. Wellman, Towards the science of security and privacy in machine learning, arXiv preprint, arXiv: 1611.03814.Google Scholar

[38]

M. Parkhi, A. Vedaldi and A. Zisserman, Deep face recognition, in Proceedings of British machine vision conference (BMVC), 1 (2015), 6pp. doi: 10.5244/C.29.41. Google Scholar

[39]

A. Rahmati, E. Fernandes, K. Eykholt, X. Chen and A. Prakash, Heimdall: A privacyrespecting implicit preference collection framework, in Proceedings of the 15th Annual International Conference on Mobile Systems, Applications, and Services, ACM, 2017, 453-463. doi: 10.1145/3081333.3081334. Google Scholar

[40]

B. Rashidi, C. Fung, A. Nguyen and T. Vu, Android permission recommendation using transitive bayesian inference model, in Proceedings of European Symposium on Research in Computer Security, Springer, 2016, 477-497. doi: 10.1007/978-3-319-45744-4_24. Google Scholar

[41]

S. Roy, J. DeLoach, Y. Li, N. Herndon, D. Caragea, X. Ou, V. P. Ranganath, H. Li and N. Guevara, Experimental study with real-world data for android app security analysis using machine learning, in Proceedings of the 31st Annual Computer Security Applications Conference, ACM, 2015, 81-90. doi: 10.1145/2818000.2818038. Google Scholar

[42]

U. Rüshrmair, F. Sehnke, J. Sölter, G. Dror, S. Devadas and J. Schmidhuber, Modeling attacks on physical unclonable functions, in Proceedings of the 17th ACM conference on Computer and communications security, ACM, 2010, 237-249.Google Scholar

[43]

U. RührmairJ. SölterF. SehnkeX. XuA. MahmoudV. StoyanovaG. DrorJ. SchmidhuberW. Burleson and S. Devadas, Puf modeling attacks on simulated and silicon data, IEEE Transactions on Information Forensics and Security, 8 (2013), 1876-1891. Google Scholar

[44]

M. S. Shahriar and M. S. Rahman, Urban sensing and smart home energy optimisations: A machine learning approach, in Proceedings of the 2015 International Workshop on Internet of Things towards Applications, ACM, 2015, 19-22.Google Scholar

[45]

F. Siegemund, A context-aware communication platform for smart objects, Lecture notes in computer science, (2004), 69-86. doi: 10.1007/978-3-540-24646-6_5. Google Scholar

[46]

K. Simonyan and A. Zisserman, Very deep convolutional networks for large-scale image recognition, arXiv: 1409.1556.Google Scholar

[47]

R. Sommer and V. Paxson, Outside the closed world: On using machine learning for network intrusion detection, in Proceedings of 2010 IEEE Symposium on Security and Privacy (SP), IEEE, 2010, 305-316. doi: 10.1109/SP.2010.25. Google Scholar

[48]

I. Strategy and P. Unit, Itu internet reports 2005: The internet of things, Geneva: International Telecommunication Union (ITU).Google Scholar

[49]

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Figure 1.  Unified Smart Home System Architecture
Figure 2.  Learning-based Attack Vectors in Smart Home Systems
Table 1.  A brief summary of learning application in smart home
Layer Application Description Func. Sec. References
Control Layer Image/Speech Recognition Identifying specific images to meet user requirements; Verifying users identities through face/voice [11], [28]
Incident Recognition Using real-time data to predict sudden events on health issues [36], [60]
Energy saving Predicting energy consumption;
Managing energy utilisation
[44], [50]
User preference Providing home services for users based on predicted user preference [10], [39]
Anomalous Detection Detecting abnormal behaviors;
Defending DDoS attack;
Device failure detection
[7], [29]
Processing Layer Malware Detection Detecting malicious software;
Providing recommended solutions
[23], [32], [40]
Layer Application Description Func. Sec. References
Control Layer Image/Speech Recognition Identifying specific images to meet user requirements; Verifying users identities through face/voice [11], [28]
Incident Recognition Using real-time data to predict sudden events on health issues [36], [60]
Energy saving Predicting energy consumption;
Managing energy utilisation
[44], [50]
User preference Providing home services for users based on predicted user preference [10], [39]
Anomalous Detection Detecting abnormal behaviors;
Defending DDoS attack;
Device failure detection
[7], [29]
Processing Layer Malware Detection Detecting malicious software;
Providing recommended solutions
[23], [32], [40]
Table 2.  A taxonomy of learning-related attack in smart home
Angles Description Attack Vectors References
Exploiting vulnerabilities of Learning Automatic vehicle interference Tampering with the image transmitted to the automatic vehicle image recognition algorithm [37]
Controlling voice control system Designing an ultrasound that contain voice control commands, but humans could not hear [55]
Intrusion detection systems evasion Disguising traffic pattern of the malicious data [14]
Using Learning-Based Techniques Attack cryptographic algorithm Learning-based analysis of power traces to find secret key information [20], [25]
PUF attack Learning-based modeling methods; Combining side-channel information with machine learning modeling techniques [27], [31],
[42], [43]
Stealing information from cache Building cache pattern classifier to extract information [58]
Recovering printed text Analyzing voice of printer via machine learning [5]
Angles Description Attack Vectors References
Exploiting vulnerabilities of Learning Automatic vehicle interference Tampering with the image transmitted to the automatic vehicle image recognition algorithm [37]
Controlling voice control system Designing an ultrasound that contain voice control commands, but humans could not hear [55]
Intrusion detection systems evasion Disguising traffic pattern of the malicious data [14]
Using Learning-Based Techniques Attack cryptographic algorithm Learning-based analysis of power traces to find secret key information [20], [25]
PUF attack Learning-based modeling methods; Combining side-channel information with machine learning modeling techniques [27], [31],
[42], [43]
Stealing information from cache Building cache pattern classifier to extract information [58]
Recovering printed text Analyzing voice of printer via machine learning [5]
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