Enhancing Security in IoT Instruments Using Artificial Intelligence

  • Chapter
  • First Online:
IoT and Cloud Computing for Societal Good

Part of the book series: EAI/Springer Innovations in Communication and Computing ((EAISICC))

Abstract

IoT is the amalgamation of sensor and actuators monitoring to accomplish a task. These instruments with different capabilities communicate over a common platform. IoT with AI aids the machines to learn and incorporate the methodology in many samples. The previous contribution uses artificial intelligence embedded in the instrument to assist and certify the structure. Computer-based intelligence is a geographic area of imitation cognition in which PC procedure is engaged to pick up, in actuality, framework and inference. As learning occurs, the capacities interior of the system become continuously sharp, and the program ends up acceptable for settling on taught decisions. In the framework, two of the most acclaimed strategies are fake system frameworks (ANNs) and inherited computations. ANNs imitate the nerve cell and synapses in the psyche to trade collection for map**, realizing, besides, essential leadership. They are in use inside IoT arrangement to screen the domain of IoT instruments and to settle on instructed conclusion. The authors have suggested the use of ANN to get acquainted with the sound state of a system and related contraptions. The proposal aims to increase reliability by 4.17% and security by 9.5%.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 149.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 199.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free ship** worldwide - see info
Hardcover Book
USD 199.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free ship** worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. N. Ambika, Chapter 7: Methodical IoT-based information system in healthcare, in Smart Medical collection Sensing and IoT Systems Design in Healthcare, ed. by C. Chakraborthy, (IGI Global, Bangalore, 2020), pp. 155–177

    Google Scholar 

  2. F.A. Othman, M. Hashem, I.A.T. Alotaibi, F. Alaba, Cyberspace of Things security: A survey. J. Netw. Comput. Appl., 10–28 (2017)

    Google Scholar 

  3. N. Ambika, G.T. Raju, ECAWSN: eliminating compromised node with the help of auxiliary nodes in wireless sensor network. Int. J. Secur. Netw. 9(2), 78–84 (April 2014)

    Article  Google Scholar 

  4. N. Ambika, Energy-perceptive authentication in virtual private networks using GPS collection, in Security, Privacy and Trust in the IoT Environment, (Springer, Cham, 2019), pp. 25–38

    Chapter  Google Scholar 

  5. R. Ganesh Babu, P. Karthika, V. Aravinda Rajan, Secure IoT systems using raspberry Pi machine learning artificial intelligence, in International Conference on Computer Networks and Inventive Communication Technologies, (Coimbatore, 2019), pp. 797–805

    Google Scholar 

  6. A.K. Jain, K. Nandakumar, A. Nagar, Biometric template security. EURASIP J. Adv. Signal Process. 113, 1–17 (January 2008)

    Google Scholar 

  7. J. Zhou, Y. Wang, K. Ota, M. Dong, AAIoT: Accelerating artificial intelligence in IoT systems. IEEE Wireless Commun. Lett. 8(3), 825–828 (January 2019)

    Article  Google Scholar 

  8. S.K. Singh, S. Rathore, J.H. Park, Blockiotintelligence: A blockchain-enabled intelligent IoT architecture with artificial intelligence. Futur. Gener. Comput. Syst., 1–23 (2019)

    Google Scholar 

  9. O. Vermesan et al., cyberspace of robotic things: converging sensing/actuating, hypoconnectivity, artificial intelligence and IoT Platforms, in Cognitive Hyperconnected Digital Transformation: cyberspace of Things Intelligence Evolution, (River Publishers, Gistrup, 2017), pp. 97–155

    Google Scholar 

  10. Z. Allam, Z.A. Dhunny, On big collection, artificial intelligence and smart cities. Cities 89, 80–91 (2019)

    Article  Google Scholar 

  11. S. Soomro, M.H. Miraz, A. Prasanth, M. Abdullah, Artificial intelligence enabled IoT: traffic congestion reduction in smart cities, in IET 2018 Smart Cities Symposium (SCS ‘18), (University of Bahrain, Bahrain, 2018), pp. 81–86

    Google Scholar 

  12. F. Farivar, M.S. Haghighi, A. Jolfaei, M. Alazab, Artificial Intelligence for Detection, Estimation, and Compensation of Malicious Attacks in Nonlinear Cyber-Physical Systems and Industrial IoT. IEEE Trans. Ind. Inf. 16(4), 2716–2725 (2019)

    Article  Google Scholar 

  13. D. Choi, K. Lee, An artificial intelligence approach to financial fraud detection under IoT environment: A survey and implementation. Secur. Commun. Netw., 1–16 (2018)

    Google Scholar 

  14. H. Jiang, Mobile fire evacuation system for large public buildings based on artificial Intelligence and IoT. IEEE Access 7, 64101–64109 (May 2019)

    Article  Google Scholar 

  15. W. Lee et al., Automatic agent generation for IoT-based smart house simulator. Neurocomputing 209, 14–24 (October 2016)

    Article  Google Scholar 

  16. L. Liu, B. Zhou, Z. Zou, S.C. Yeh, L. Zheng, A smart unstaffed retail shop based on artificial intelligence and IoT, in IEEE 23rd International workshop on computer aided modeling and design of communication links and networks (CAMAD), (Barcelona, 2018), pp. 1–4

    Google Scholar 

  17. A.H. Sodhro, S. Pirbhulal, V.H.C. de Albuquerque, Artificial intelligence-driven mechanism for border computing-based industrial applications. IEEE Trans. Ind. Inf. 15(7), 4235–4243 (March 2019)

    Article  Google Scholar 

  18. A. Rego, A. Canovas, J.M. Jiménez, J. Lloret, An intelligent system for video surveillance in IoT environments. IEEE Access 6, 31580–31598 (June 2018)

    Article  Google Scholar 

  19. M. Muslih, D. Supardi, E. Multipi, Y.M. Nyaman, A. Rismawan, Develo** smart workspace based IOT with artificial intelligence using telegram chatbot, in 2018 International Conference on Computing, Engineering, and Design (ICCED), (Bangkok, 2018), pp. 230–234

    Google Scholar 

  20. E. Alreshidi, Smart sustainable agriculture (SSA) solution underpinned by cyberspace of things (IoT) and artificial intelligence (AI). Int. J. Adv. Comput. Sci. Appl. 10(5), 93–102 (May 2019)

    Google Scholar 

  21. J. Lloret, J. Tomas, A. Canovas, L. Parra, An Integrated IoT Architecture for Smart Metering. IEEE Commun. Mag. 54(12), 50–57 (December 2016)

    Article  Google Scholar 

  22. F. Bu, X. Wang, A smart agriculture IoT system based on deep reinforcement learning. Futur. Gener. Comput. Syst. 99, 500–507 (October 2019)

    Article  Google Scholar 

  23. G.K. Shyam, S.S. Manvi, P. Bharti, Smart waste management using cyberspace-of-Things (IoT), in 2nd international conference on computing and communications technologies (ICCCT), (Chennai, 2017), pp. 199–203

    Google Scholar 

  24. A. Somov et al., Pervasive agriculture: IoT-enabled greenhouse for plant growth control. IEEE Pervasive Computing 17(4), 65–75 (October-December 2018)

    Article  Google Scholar 

  25. I. García-Magariño, R. Muttukrishnan, J. Lloret, Human-centric AI for trustworthy IoT systems with explainable multilayer perceptrons. IEEE Access 7, 125562–125574 (August 2019)

    Article  Google Scholar 

  26. R. Tolosana, M. Gomez-Barrero, C. Busch, J. Ortega-Garcia, Biometric presentation attack detection: Beyond the visible spectrum. IEEE Trans. Inf. Forensics Secur. 15, 1261–1275 (August 2019)

    Article  Google Scholar 

  27. D. Gafurov, E. Snekkenes, T.E. Buvarp, Robustness of biometric gait authentication against impersonation attack, in OTM Confederated International Conferences “On the Move to Meaningful cyberspace Systems”, (Montpellier, 2006), pp. 479–488

    Google Scholar 

  28. A. George et al., Biometric face presentation attack detection with multi-channel convolutional neural network. IEEE Trans. Inf. Forensics Secur 15, 42–55 (May 2019)

    Article  Google Scholar 

  29. M.A. Ferrer, M. Diaz, C. Carmona-Duarte, R. Plamondon, A biometric attack case based on signature synthesis, in International Carnahan Conference on Security Technology (ICCST), (Montreal, 2018), pp. 1–6

    Google Scholar 

  30. C. Rathgeb, A. Uhl, Statistical attack against iris-biometric fuzzy commitment schemes, in CVPR 2011 WORKSHOPS, (Colorado Springs, 2011), pp. 23–30

    Google Scholar 

  31. E.M. Nowara, A. Sabharwal, A. Veeraraghavan, Ppgsecure: Biometric presentation attack detection using photopletysmograms, in 12th IEEE International Conference on Automatic Face & Gesture Recognition, (Washington, DC, 2017), pp. 56–62

    Google Scholar 

  32. M. Une, A. Otsuka, H. Imai, Wolf attack probability: A new security measure in biometric authentication systems, in International Conference on Biometrics, (Baltimore, 2007), pp. 396–406

    Google Scholar 

  33. O.V. Komogortsev, A. Karpov, C.D. Holland, Attack of mechanical replicas: Liveness detection with eye movements. IEEE Trans. Inf. Forensics Secur. 10(4), 716–725 (February 2015)

    Article  Google Scholar 

  34. M. Pal, G. Saha, On robustness of speech based biometric systems against voice conversion attack. Appl. Soft Comput. 30, 214–228 (May 2015)

    Article  Google Scholar 

  35. A.P.S. Bhogal, D. Söllinger, P. Trung, A. Uhl, Non-reference image quality assessment for biometric presentation attack detection, in 5th International Workshop on Biometrics and Forensics (IWBF), (Coventry, 2017), pp. 1–6

    Google Scholar 

  36. W.J. Scheirer, T.E. Boult, Cracking fuzzy vaults and biometric encryption, in Biometrics Symposium, (Baltimore, 2007), pp. 1–6

    Google Scholar 

  37. S. Ghouzali et al., Trace attack against biometric mobile applications. Mob. Inf. Syst., 1–15 (April 2016)

    Google Scholar 

  38. Q. Gui, W. Yang, Z. **, M.V. Ruiz-Blondet, S. Laszlo, A residual feature-based replay attack detection approach for brainprint biometric systems, in IEEE International Workshop on Information Forensics and Security (WIFS), (Abu Dhabi, 2016), pp. 1–6

    Google Scholar 

  39. K.B. Raja, R. Raghavendra, C. Busch, Color adaptive quantized patterns for presentation attack detection in ocular biometric systems, in 9th International Conference on Security of Information and Networks, (Newark, 2016), pp. 9–15

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2022 Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Ambika, N. (2022). Enhancing Security in IoT Instruments Using Artificial Intelligence. In: Verma, J.K., Saxena, D., González-Prida, V. (eds) IoT and Cloud Computing for Societal Good. EAI/Springer Innovations in Communication and Computing. Springer, Cham. https://doi.org/10.1007/978-3-030-73885-3_16

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-73885-3_16

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-73884-6

  • Online ISBN: 978-3-030-73885-3

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Navigation