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Security framework for smart cyber infrastructure

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Abstract

The rapid deployment of the Internet of Things (IoT) devices have led to the development of innovative information services, unavailable a few years ago. To provide these services, IoT devices connect and communicate using networks like Bluetooth, Wi-Fi, and Ethernet. This full-stack connection of the IoT devices has introduced a grand security challenge. This paper presents an IoT security framework to protect smart infrastructures from cyber attacks. This IoT security framework is applied to Bluetooth protocol and IoT sensors networks. For the Bluetooth protocol, the intrusion detection system (IDS) uses n-grams to extract temporal and spatial features of Bluetooth communication. The Bluetooth IDS has a precision of 99.6% and a recall of 99.6% using classification technique like Ripper algorithm and Decision Tree (C4.5). We also used AdaBoost, support vector machine (SVM), Naive Bayes, and Bagging algorithm for intrusion detection. The Sensor IDS uses discrete wavelet transform (DWT) to extract spatial and temporal features characteristics of the observed signal. Using the detailed coefficients of Biorthogonal DWT, Daubechies DWT, Coiflets DWT, Discrete Meyer DWT, Reverse Biorthogonal DWT, Symlets DWT, we present the results for detecting attacks with One-Class SVM, Local Outlier Factor, and Elliptic Envelope. The attacks used in our evaluation include Denial of Service Attacks, Impersonation Attacks, Random Signal Attacks, and Replay Attacks on temperature sensors. The One-Class SVM performed the best when compared with the results of other machine learning techniques.

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References

  1. Langner, R.: Stuxnet: dissecting a cyberwarfare weapon. IEEE Secur. Privacy 9(3), 49–51 (2011). https://doi.org/10.1109/MSP.2011.67

    Article  Google Scholar 

  2. Satam, P., Hariri, S.: WIDS: an anomaly based intrusion detection system for Wi-Fi (IEEE 80.211) Protocol. IEEE Trans. Netw. Serv. Manage. 18(1), 1077–1091 (2021). https://doi.org/10.1109/TNSM.2020.3036138

    Article  Google Scholar 

  3. Alipour, H., Al-Nashif, Y.B., Satam, P., Hariri, S.: Wireless anomaly detection based on IEEE 80.211 behavior analysis. IEEE Trans. Inf. Forensics Secur. 10(10), 2158–2170 (2015). https://doi.org/10.1109/TIFS.2015.2433898

    Article  Google Scholar 

  4. Al-Nashif, Y., Kumar, A.A., Hariri, S., Qu, G., Luo, Y., Szidarovsky, F.: Multi-level intrusion detection system (ML-IDS). In: 5th International Conference on Autonomic Computing, ICAC 2008, pp. 131–140 (2008). https://doi.org/10.1109/ICAC.2008.25

  5. Satam, P., Alipour, H., Al-Nashif, Y., Hariri, S.: Anomaly behavior analysis of DNS protocol. J. Internet Serv. Inf. Secur. JISIS 5(4), 85–97 (2015)

    Google Scholar 

  6. Satam, P., Kelly, D., Hariri, S.: Anomaly behavior analysis of website vulnerability and security. In: Proceedings of IEEE/ACS International Conference on Computer Systems and Applications, AICCSA, vol. 0 (2016). https://doi.org/10.1109/AICCSA.2016.7945697

  7. Satam, P., Satam, S., Hariri, S., Alshawi, A.: Anomaly behavior analysis of IoT protocols. Model. Des. Secur. Internet Things (2020). https://doi.org/10.1002/9781119593386.ch13

    Article  Google Scholar 

  8. Pacheco, J., Hariri, S.: IoT security framework for smart cyber infrastructures. In: Proceedings—IEEE 1st International Workshops on Foundations and Applications of Self-Systems, FAS-W 2016, pp. 242–247 (2016). https://doi.org/10.1109/FAS-W.2016.58

  9. Buckman, A.H., Mayfield, M., Beck, S.B.M.: What is a smart building? Smart Sustain. Built Environ. 3(2), 92–109 (2014). https://doi.org/10.1108/SASBE-01-2014-0003

    Article  Google Scholar 

  10. Wang, Z., Wang, L., Dounis, A.I., Yang, R.: Multi-agent control system with information fusion based comfort model for smart buildings. Appl. Energy 99, 247–254 (2012). https://doi.org/10.1016/j.apenergy.2012.05.020

    Article  Google Scholar 

  11. Sadiku, M.N.O., Musa, S.M.: Cloud computing: opportunities and challenges. IEEE Potential 33, 34–36 (2014)

    Article  Google Scholar 

  12. Yi, S., Li, C., Li, Q.: A survey of fog computing: Concepts, applications and issues. In: Proceedings of the International Symposium on Mobile Ad Hoc Networking and Computing (MobiHoc) 2015 June, pp. 37–42 (2015). https://doi.org/10.1145/2757384.2757397

  13. Jabbar, M.A., Aluvalu, R.: Intrusion detection system for the internet of things: a review. IET Conf. Publ. (2018). https://doi.org/10.1049/cp.2018.1419

    Article  Google Scholar 

  14. Berthier, R., Sanders, W.H.: Specification-based intrusion detection for advanced metering infrastructures. In: Proceedings of IEEE Pacific Rim International Symposium on Dependable Computing, PRDC, pp. 184–193 (2011). https://doi.org/10.1109/PRDC.2011.30

  15. Olufowobi, H., Young, C., Zambreno, J., Bloom, G.: SAIDuCANT: specification-based automotive intrusion detection using controller area network (CAN) timing. IEEE Trans. Veh. Technol. 69(2), 1484–1494 (2020). https://doi.org/10.1109/TVT.2019.2961344_rfseq1

    Article  Google Scholar 

  16. Hajisalem, V., Babaie, S.: A hybrid intrusion detection system based on ABC-AFS algorithm for misuse and anomaly detection. Comput. Netw. 136, 37–50 (2018). https://doi.org/10.1016/j.comnet.2018.02.028

    Article  Google Scholar 

  17. Satam, P., Satam, S., Hariri, S.: Bluetooth Intrusion Detection System (BIDS). Proceedings of IEEE/ACS International Conference on Computer Systems and Applications, AICCSA , 2018 November (2019). https://doi.org/10.1109/AICCSA.2018.8612809

  18. Jin, J., Gubbi, J., Marusic, S., Palaniswami, M.: An information framework for creating a smart city through internet of things. IEEE Internet Things J. 1(2), 112–121 (2014). https://doi.org/10.1109/JIOT.2013.2296516

    Article  Google Scholar 

  19. Ferreira, H.G.C., Dias Canedo, E., De Sousa, R.T.: IoT architecture to enable intercommunication through REST API and UPnP using IP, ZigBee and arduino. In: International Conference on Wireless and Mobile Computing, Networking and Communications, pp. 53–60 (2013). https://doi.org/10.1109/WiMOB.2013.6673340

  20. Karagiannis, V., Chatzimisios, P., Vazquez-Gallego, F., Alonso-Zarate, J.: A survey on application layer protocols for the Internet of Things. Trans. IoT Cloud Comput. 3(1), 11–17 (2015)

    Google Scholar 

  21. Manadhata, P.K., Wing, J.M.: An attack surface metric. IEEE Trans. Softw. Eng. 37(3), 371–386 (2011). https://doi.org/10.1109/TSE.2010.60

    Article  Google Scholar 

  22. Soliman, M., Abiodun, T., Hamouda, T., Zhou, J., Lung, C.H.: Smart home: integrating internet of things with web services and cloud computing. Proc. Int. Conf. Cloud Comput. Technol. Sci. CloudCom 2, 317–320 (2013). https://doi.org/10.1109/CloudCom.2013.155

    Article  Google Scholar 

  23. Sweldens, W.: The lifting scheme: a custom-design construction of biorthogonal wavelets. Appl. Comput. Harmon. Anal. 3(2), 186–200 (1996). https://doi.org/10.1006/acha.1996.0015

    Article  MathSciNet  MATH  Google Scholar 

  24. Wahid, K.A., Dimitrov, V.S., Jullien, G.A., Badawy, W.: An analysis of Daubechies discrete wavelet transform based on algebraic integer encoding scheme. In: Proceedings—3rd International Workshop on Digital and Computational Video, DCV 2002, pp. 27–34 (2002). https://doi.org/10.1109/DCV.2002.1218740

  25. Graps, A.: An introduction to wavelets. IEEE Comput. Sci. Eng. 2(2), 50–61 (1995). https://doi.org/10.1109/99.388960

    Article  Google Scholar 

  26. Xu, L., Zhang, D., Wang, K.: Wavelet-based cascaded adaptive filter for removing baseline drift in pulse waveforms. IEEE Trans. Biomed. Eng. 52(11), 1973–1975 (2005). https://doi.org/10.1109/TBME.2005.856296

    Article  Google Scholar 

  27. Szewczyk, R., Grabowski, K., Napieralska, M., Sankowski, W., Zubert, M., Napieralski, A.: A reliable iris recognition algorithm based on reverse biorthogonal wavelet transform. Pattern Recognit. Lett. 33(8), 1019–1026 (2012). https://doi.org/10.1016/j.patrec.2011.08.018

    Article  Google Scholar 

  28. Phinyomark, A., Limsakul, C., Phukpattaranont, P.: An optimal wavelet function based on wavelet denoising for multifunction myoelectric control, pp. 1098–1101 (2009). https://doi.org/10.1109/ecticon.2009.5137236

  29. Davis, J., Goadrich, M.: The relationship between precision-recall and roc curves. In: ICML ’06: Proceedings of the 23rd international conference on Machine Learning, pp. 233–240 (2006). https://doi.org/10.1145/1143844.1143874

  30. Satam, S., Satam, P., Hariri, S.: Multi-level Bluetooth Intrusion Detection System. In: Proceedings of IEEE/ACS International Conference on Computer Systems and Applications, AICCSA 1–8 November 2020 (2020). https://doi.org/10.1109/AICCSA50499.2020.9316514

  31. Brown, P., Cocke, J., Pietra, S.D., Pietra, V.D., Jelinek, F., Mercer, R., Roossin, P.: A statistical approach to French/English translation. Biol. Artif. Intell. Syst. 16(2), 547–561 (1988). https://doi.org/10.1007/978-94-009-3117-6_27

    Article  Google Scholar 

  32. Satam, S.: Bluetooth anomaly based intrusion detection system by Shalaka Satam A Thesis submitted to the Faculty of the Department of Electrical and Computer Engineering in Partial Fulfillment of the Requirements For the Degree of Master of Science In the Graduate C (2017)

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Acknowledgements

This work is partly supported by the Air Force Office of Scientific Research (AFOSR) Dynamic Data-Driven Application Systems (DDDAS) award number FA9550-18-1- 0427, National Science Foundation (NSF) research projects NSF-1624668 and NSF-1849113, National Institute of Standards and Technology (NIST) 70NANB18H263 and Department of Energy/National Nuclear Security Administration under Award Number(s) DE-NA0003946.

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  1. Department of Electrical and Computer Engineering, The University of Arizona, Tucson, AZ, 85719, USA

    Shalaka Satam, Pratik Satam & Salim Hariri

  2. Department of Industrial Engineering, Universidad de Sonora, Hermosillo, 83000, Sonora, Mexico

    Jesus Pacheco

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  1. Shalaka Satam
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Satam, S., Satam, P., Pacheco, J. et al. Security framework for smart cyber infrastructure. Cluster Comput 25, 2767–2778 (2022). https://doi.org/10.1007/s10586-021-03482-2

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