Advertisement

Secure and Safe IIoT Systems via Machine and Deep Learning Approaches

  • Aris S. LalosEmail author
  • Athanasios P. Kalogeras
  • Christos Koulamas
  • Christos Tselios
  • Christos Alexakos
  • Dimitrios Serpanos
Chapter

Abstract

This chapter reviews security and engineering system safety challenges for Internet of Things (IoT) applications in industrial environments. On the one hand, security concerns arise from the expanding attack surface of long-running technical systems due to the increasing connectivity on all levels of the industrial automation pyramid. On the other hand, safety concerns magnify the consequences of traditional security attacks. Based on the thorough analysis of potential security and safety issues of IoT systems, the chapter surveys machine learning and deep learning (ML/DL) methods that can be applied to counter the security and safety threats that emerge in this context. In particular, the chapter explores how ML/DL methods can be leveraged in the engineering phase for designing more secure and safe IoT-enabled long-running technical systems. However, the peculiarities of IoT environments (e.g., resource-constrained devices with limited memory, energy, and computational capabilities) still represent a barrier to the adoption of these methods. Thus, this chapter also discusses the limitations of ML/DL methods for IoT security and how they might be overcome in future work by pursuing the suggested research directions.

Keywords

Machine learning Deep learning Security threats in IoT 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Notes

Acknowledgements

We acknowledge support of this work by the project “I3T—Innovative Application of Industrial Internet of Things (IIoT) in Smart Environments” (MIS 5002434) which is implemented under the “Action for the Strategic Development on the Research and Technological Sector,” funded by the Operational Programme “Competitiveness, Entrepreneurship and Innovation” (NSRF 2014–2020) and co-financed by Greece and the European Union (European Regional Development Fund).

The views and opinions expressed are those of the authors and do not necessary reflect the official position of Citrix Systems Inc.

References

  1. Aazam, M., Khan, I., Alsaffar, A. A., & Huh, E. (2014). Cloud of things: Integrating internet of things and cloud computing and the issues involved. In Proceedings of 2014 11th International Bhurban Conference on Applied Sciences Technology (IBCAST) Islamabad, Pakistan, 14th–18th January, 2014 (pp. 414–419).  https://doi.org/10.1109/IBCAST.2014.6778179.
  2. Abeshu, A., & Chilamkurti, N. (2018). Deep learning: The frontier for distributed attack detection in fog-to-things computing. IEEE Communications Magazine, 56(2), 169–175. ISSN 0163-6804.  https://doi.org/10.1109/MCOM.2018.1700332.CrossRefGoogle Scholar
  3. Adolphs, P., Cabot, J., & Wimmer, M. (2016). Structure of the Administration Shell: Continuation of the Development of the Reference Model for the Industrie 4.0 Component. Platform Industrie 4.0. https://www.plattform-i40.de/I40/Redaktion/EN/Downloads/Publikation/structure-of-the-administration-shell.pdf.
  4. Alharbi, S., Rodriguez, P., Maharaja, R., Iyer, P., Subaschandrabose, N., & Ye, Z. (2017). Secure the internet of things with challenge response authentication in fog computing. In 2017 IEEE 36th International Performance Computing and Communications Conference (IPCCC) (pp. 1–2).  https://doi.org/10.1109/PCCC.2017.8280489.
  5. Aminanto, M. E., Choi, R., Tanuwidjaja, H. C., Yoo, P. D., & Kim, K. (2018). Deep abstraction and weighted feature selection for wi-fi impersonation detection. IEEE Transactions on Information Forensics and Security, 13(3), 621–636. ISSN 1556-6013.  https://doi.org/10.1109/TIFS.2017.2762828.CrossRefGoogle Scholar
  6. Athey, S., & Imbens, G. (2015). Machine learning methods for estimating heterogeneous causal effects.zbMATHGoogle Scholar
  7. Attenberg, J., Ipeirotis, P., & Provost, F. (2015). Beat the machine: Challenging humans to find a predictive model’s “unknown unknowns”. Journal of Data and Information Quality, 6(1), 1:1–1:17. ISSN 1936-1955. https://doi.org/10.1145/2700832.CrossRefGoogle Scholar
  8. Atzori, L., Iera, A., & Morabito, G. (2010). The internet of things: A survey. Computer Networks, 54(15), 2787–2805. ISSN 1389-1286. https://doi.org/10.1016/j.comnet.2010.05.010.CrossRefGoogle Scholar
  9. Caruana, R., Lou, Y., Gehrke, J., Koch, P., Sturm, M., & Elhadad, N. (2015). Intelligible models for healthcare: Predicting pneumonia risk and hospital 30-day readmission. In Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD ’15 (pp. 1721–1730), New York, NY: ACM. ISBN 978-1-4503-3664-2. https://doi.org/10.1145/2783258.2788613.CrossRefGoogle Scholar
  10. Chen, K., Zhang, S., Li, Z., Zhang, Y., Deng, Q., Ray, S., et al. (2018). Internet-of-things security and vulnerabilities: Taxonomy, challenges, and practice. Journal of Hardware and Systems Security, 2(2), 97–110. ISSN 2509-3428. https://doi.org/10.1007/s41635-017-0029-7.CrossRefGoogle Scholar
  11. Conn, A. (2015). The AI wars: The battle of the human minds to keep artificial intelligence safe. Needham: Industrial Internet Consortium. http://futureoflife.org/2015/12/17/the-ai-wars-the-battle-of-the-human-minds-to-keep-artificial-intelligence-safe.Google Scholar
  12. Doshi, R., Apthorpe, N., & Feamster, N. (2018). Machine learning ddos detection for consumer internet of things devices. In 2018 IEEE Security and Privacy Workshops (SPW) (pp. 29–35).  https://doi.org/10.1109/SPW.2018.00013.
  13. Du, W., & Zhan, Z. (2002). Building decision tree classifier on private data. In Proceedings of the IEEE International Conference on Privacy, Security and Data Mining, CRPIT ’14 (Vol. 14, pp. 1–8), Darlinghurst: Australian Computer Society, ISBN 0-909-92592-5. http://dl.acm.org/citation.cfm?id=850782.850784.
  14. ENISA Report. (2017). Baseline Security Recommendations for IoT. https://www.enisa.europa.eu/publications/baseline-security-recommendations-for-iot.Google Scholar
  15. ENISA Report. (2018a). Good Practices for Security of Internet of Things, https://www.enisa.europa.eu/publications/good-practices-for-security-of-iot.Google Scholar
  16. ENISA Report. (2018b) Hardware Threat Landscape and Good Practice Guide, https://www.enisa.europa.eu/publications/hardware-threat-landscape.
  17. ENISA Report. (2018c). Ad-hoc and sensor networking for M2M Communications, https://www.enisa.europa.eu/publications/m2m-communications-threat-landscape.Google Scholar
  18. Evans, D. (2011). The internet of things—how the next evolution of the internet is changing everything. White Paper. San Jose: CISCO.Google Scholar
  19. Fiore, U., Palmieri, F., Castiglione, A., & De Santis, A. (2013). Network anomaly detection with the restricted boltzmann machine. Neurocomputing, 122, 13–23. ISSN 0925-2312. https://doi.org/10.1016/j.neucom.2012.11.050.CrossRefGoogle Scholar
  20. Freitas, A. A. (2014). Comprehensible classification models: A position paper. SIGKDD Explorations Newsletter, 15(1), 1–10. ISSN 1931-0145. https://doi.org/10.1145/2594473.2594475.CrossRefGoogle Scholar
  21. Gangsar, P., & Tiwari, R. (2017). Comparative investigation of vibration and current monitoring for prediction of mechanical and electrical faults in induction motor based on multiclass-support vector machine algorithms. Mechanical Systems and Signal Processing, 94, 464–481. ISSN 0888-3270. https://doi.org/10.1016/j.ymssp.2017.03.016.CrossRefGoogle Scholar
  22. Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep learning. Cambridge: The MIT Press. ISBN 0262035618, 9780262035613.zbMATHGoogle Scholar
  23. Hiromoto, R. E., Haney, M., & Vakanski, A. (2017). A secure architecture for iot with supply chain risk management. In 2017 9th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS) (Vol. 1, pp. 431–435).  https://doi.org/10.1109/IDAACS.2017.8095118.
  24. Humayed, A., Lin, J., Li, F., & Luo, B. (2017). Cyber-physical systems security—a survey. IEEE Internet of Things Journal, 4 (6), 1802–1831. ISSN 2327-4662.  https://doi.org/10.1109/JIOT.2017.2703172.CrossRefGoogle Scholar
  25. IIC. Industrial Internet of Things Volume G4: Security Framework (2016). https://www.iiconsortium.org/IISF.htm.
  26. ITY-T. Overview of Internet of Things (2012)Google Scholar
  27. Jazdi, N. (2014). Cyber physical systems in the context of industry 4.0. In 2014 IEEE International Conference on Automation, Quality and Testing, Robotics (pp. 1–4).  https://doi.org/10.1109/AQTR.2014.6857843.
  28. Jordan, M. I., & Mitchell, T. M. (2015). Machine learning: Trends, perspectives, and prospects. Science, 349(6245), 255–260. ISSN 0036-8075.  https://doi.org/10.1126/science.aaa8415.MathSciNetCrossRefGoogle Scholar
  29. Kapoor, S., Mojsilovic, A., Strattner, J. N., & Varshney, K. R. (2015). From open data ecosystems to systems of innovation: A journey to realize the promise of open data. In Proceedings of the Data for Good Exchange Conference, New York, NY, USA.Google Scholar
  30. Khan, R., Khan, S. U., Zaheer, R., & Khan, S. (2012). Future internet: The internet of things architecture, possible applications and key challenges. In 2012 10th International Conference on Frontiers of Information Technology (pp. 257–260).  https://doi.org/10.1109/FIT.2012.53.
  31. Kim, G., Lee, S., & Kim, S. (2014). A novel hybrid intrusion detection method integrating anomaly detection with misuse detection. Expert Systems with Applications, 41(4), 1690–1700. ISSN 0957-4174. https://doi.org/10.1016/j.eswa.2013.08.066.MathSciNetCrossRefGoogle Scholar
  32. Lerman, L., Bontempi, G., & Markowitch, O. (2015). A machine learning approach against a masked AES. Journal of Cryptographic Engineering, 5(2), 123–139. ISSN 2190-8516. https://doi.org/10.1007/s13389-014-0089-3.CrossRefGoogle Scholar
  33. Lin, S.-W., Crawford, M., Miller, B., Durand, J., & Bleakley, G. (2017a). The industrial internet of things volume G1: reference architecture. Needham: Industrial Internet Consortium. https://www.iiconsortium.org/IIC_PUB_G1_V1.80_2017-01-31.pdf.Google Scholar
  34. Lin, S.-W., Murphy, B., Clauer, E., Loewen, U., & Bleakley, G. (2017b). Architecture alignment and interoperability. Industrial Internet Consortium and Plattform Industrie 4.0 Joint Whitepaper. http://www.iiconsortium.org/pdf/JTG2_Whitepaper_final_20171205.pdf.
  35. Maghrebi, H., Portigliatti, T., & Prouff, E. (2016). Breaking cryptographic implementations using deep learning techniques. In IACR Cryptology ePrint Archive.Google Scholar
  36. Mainetti, L., Patrono, L., & Vilei, A. (2011). Evolution of wireless sensor networks towards the internet of things: A survey. In SoftCOM 2011, 19th International Conference on Software, Telecommunications and Computer Networks (pp. 1–6).Google Scholar
  37. Maller, N., & Hansson, S. O. (2008). Principles of engineering safety: Risk and uncertainty reduction. Reliability Engineering & System Safety, 93(6), 798–805. ISSN 0951-8320. https://doi.org/10.1016/j.ress.2007.03.031.CrossRefGoogle Scholar
  38. Mashal, I., Alsaryrah, O., Chung, T.-Y., Yang, C.-Z., Kuo, W.-H., & Agrawal, D. P. (2015). Choices for interaction with things on internet and underlying issues. Ad Hoc Networks, 28, 68–90. ISSN 1570-8705. https://doi.org/10.1016/j.adhoc.2014.12.006.CrossRefGoogle Scholar
  39. McLaughlin, N., Martinez del Rincon, J., Kang, B., Yerima, S., Miller, P., Sezer, S., Safaei, Y., Trickel, E., Zhao, Z., Doupé, A., & Joon Ahn, G. (2017). Deep android malware detection. In Proceedings of the Seventh ACM on Conference on Data and Application Security and Privacy, CODASPY ’17 (pp. 301–308). New York, NY: ACM. ISBN 978-1-4503-4523-1. https://doi.org/10.1145/3029806.3029823.CrossRefGoogle Scholar
  40. Meidan, Y., Bohadana, M., Shabtai, A., Ochoa, M., Tippenhauer, N. O., Guarnizo, J. D., & Elovici, Y. (2017). Detection of unauthorized iot devices using machine learning techniques. CoRR, abs/1709.04647. http://arxiv.org/abs/1709.04647.
  41. Mena, D. M., Papapanagiotou, I., & Yang, B. (2018). Internet of things: Survey on security. Information Security Journal: A Global Perspective, 27(3), 162–182. https://doi.org/10.1080/19393555.2018.1458258.Google Scholar
  42. Miorandi, D., Sicari, S., Pellegrini, F. D., & Chlamtac, I. (2012). Internet of things: Vision, applications and research challenges. Ad Hoc Networks, 10(7), 1497–1516. ISSN 1570-8705. https://doi.org/10.1016/j.adhoc.2012.02.016.CrossRefGoogle Scholar
  43. Ng, A. Y., & Jordan, M. I. (2001). On discriminative vs. generative classifiers: A comparison of logistic regression and naive bayes. In Proceedings of the 14th International Conference on Neural Information Processing Systems: Natural and Synthetic, NIPS’01 (pp. 841–848). Cambridge, MA: MIT Press. http://dl.acm.org/citation.cfm?id=2980539.2980648.Google Scholar
  44. OWASP. The free and open software security community (2018). http://www.owasp.org/index.php/OWASP_Internet_of_Things_Project.
  45. Ozay, M., Esnaola, I., Yarman Vural, F. T., Kulkarni, S. R., & Poor, H. V. (2016). Machine learning methods for attack detection in the smart grid. IEEE Transactions on Neural Networks and Learning Systems, 27(8), 1773–1786. ISSN 2162-237X.  https://doi.org/10.1109/TNNLS.2015.2404803.MathSciNetCrossRefGoogle Scholar
  46. Pan, S., Morris, T., & Adhikari, U. (2015). Developing a hybrid intrusion detection system using data mining for power systems. IEEE Transactions on Smart Grid, 6(6), 3104–3113. ISSN 1949-3053.  https://doi.org/10.1109/TSG.2015.2409775.CrossRefGoogle Scholar
  47. Pascanu, R., Mikolov, T., & Bengio, Y. (2013). On the difficulty of training recurrent neural networks. In Proceedings of the 30th International Conference on International Conference on Machine Learning, ICML’13 (Vol. 28, pp. 1310–1318). JMLR.org. http://dl.acm.org/citation.cfm?id=3042817.3043083.
  48. Quinlan, J. R. (1986). Induction of decision trees. Machine Learning, 1(1), 81–106. ISSN 0885-6125. https://doi.org/10.1023/A:1022643204877.Google Scholar
  49. Rudin, C. (2014). Algorithms for interpretable machine learning. In Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD ’14 (pp. 1519–1519). ACM: New York, NY. ISBN 978-1-4503-2956-9. https://doi.org/10.1145/2623330.2630823.CrossRefGoogle Scholar
  50. Schmidt, R., Möhring, M., Härting, R.-C., Reichstein, C., Neumaier, P., & Jozinović, P. (2015). Industry 4.0 – potentials for creating smart products: Empirical research results. In W. Abramowicz (Ed.), Business information systems (pp. 16–27). Cham: Springer. ISBN 978-3-319-19027-3.CrossRefGoogle Scholar
  51. Sethi, P., & Sarangi, S. R. (2017). Internet of things: Architectures, protocols, and applications. Journal of Electrical and Computer Engineering, 2017, 9324035:1–9324035:25.Google Scholar
  52. Sharp, M., Ak, R., & Hedberg, T. (2018). A survey of the advancing use and development of machine learning in smart manufacturing. Journal of Manufacturing Systems, 48, 170–179. ISSN 0278-6125. https://doi.org/10.1016/j.jmsy.2018.02.004. Special Issue on Smart Manufacturing.CrossRefGoogle Scholar
  53. Shaw, E. (2015). Improving service and communication with open data. Data Smart City solutions. https://datasmart.ash.harvard.edu/news/article/improving-service-and-communication-with-open-data-702.
  54. Su, M.-Y. (2011). Real-time anomaly detection systems for denial-of-service attacks by weighted k-nearest-neighbor classifiers. Expert Systems with Applications, 38(4), 3492–3498. ISSN 0957-4174. https://doi.org/10.1016/j.eswa.2010.08.137.CrossRefGoogle Scholar
  55. Syarif, A. R., & Gata, W. (2017). Intrusion detection system using hybrid binary pso and k-nearest neighborhood algorithm. In 2017 11th International Conference on Information Communication Technology and System (ICTS) (pp. 181–186).  https://doi.org/10.1109/ICTS.2017.8265667.
  56. Torres, P., Catania, C., Garcia, S., & Garino, C. G. (2016). An analysis of recurrent neural networks for botnet detection behavior. In 2016 IEEE Biennial Congress of Argentina (ARGENCON) (pp. 1–6).  https://doi.org/10.1109/ARGENCON.2016.7585247.
  57. Varshney, K. R., Prenger, R. J., Marlatt, T. L., Chen, B. Y., & Hanley, W. G. (2013). Practical ensemble classification error bounds for different operating points. IEEE Transactions on Knowledge and Data Engineering, 25(11), 2590–2601. ISSN 1041-4347.  https://doi.org/10.1109/TKDE.2012.219.CrossRefGoogle Scholar
  58. Welling, M. (2015). Are ml and statistics complementary. IMS-ISBA Meeting on Data Science in the Next 50 Years.Google Scholar
  59. Weyer, S., Schmitt, M., Ohmer, M., & Gorecky, D. (2015). Towards industry 4.0 – standardization as the crucial challenge for highly modular, multi-vendor production systems. IFAC-PapersOnLine, 48(3), 579–584. ISSN 2405-8963. https://doi.org/10.1016/j.ifacol.2015.06.143. 15th IFAC Symposium onInformation Control Problems inManufacturing.CrossRefGoogle Scholar
  60. Wu, M., Lu, T.-J., Ling, F.-Y., Sun, J., & Du, H.-Y. (2010). Research on the architecture of internet of things. In 2010 3rd International Conference on Advanced Computer Theory and Engineering(ICACTE) (Vol. 5, pp. V5–484–V5–487).  https://doi.org/10.1109/ICACTE.2010.5579493.
  61. Xanthopoulos, A. S., Kiatipis, A., Koulouriotis, D. E., & Stieger, S. (2018). Reinforcement learning-based and parametric production-maintenance control policies for a deteriorating manufacturing system. IEEE Access, 6, 576–588. ISSN 2169-3536.  https://doi.org/10.1109/ACCESS.2017.2771827.CrossRefGoogle Scholar
  62. Xie, M., Huang, M., Bai, Y., & Hu, Z. (2017). The anonymization protection algorithm based on fuzzy clustering for the ego of data in the internet of things. Journal of Electrical and Computer Engineering, Hindawi, 1 (1), 1–10. Article ID 2970673.Google Scholar
  63. Xu, L. D., He, W., & Li, S. (2014) Internet of things in industries: A survey. IEEE Transactions on Industrial Informatics, 10(4), 2233–2243. ISSN 1551-3203.  https://doi.org/10.1109/TII.2014.2300753.CrossRefGoogle Scholar
  64. Yang, K., Ren, J., Zhu, Y., & Zhang, W. (2018). Active learning for wireless iot intrusion detection. IEEE Wireless Communications, 25(6), 19–25. ISSN 1536-1284.  https://doi.org/10.1109/MWC.2017.1800079.CrossRefGoogle Scholar
  65. Ye, Y., Li, T., Adjeroh, D., & Iyengar, S. S. (2017). A survey on malware detection using data mining techniques. ACM Computing Surveys, 50(3), 41:1–41:40. ISSN 0360-0300. https://doi.org/10.1145/3073559.CrossRefGoogle Scholar
  66. Zenati, H., Foo, C. S., Lecouat, B., Manek, G., & Chandrasekhar, V. R. (2018) Efficient GAN-based anomaly detection. CoRR, abs/1802.06222. http://arxiv.org/abs/1802.06222.

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Aris S. Lalos
    • 1
    Email author
  • Athanasios P. Kalogeras
    • 1
  • Christos Koulamas
    • 1
  • Christos Tselios
    • 2
  • Christos Alexakos
    • 1
  • Dimitrios Serpanos
    • 1
  1. 1.Industrial Systems InstituteATHENA Research CenterPatrasGreece
  2. 2.Citrix SystemsPatrasGreece

Personalised recommendations