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Functional Prototype for Intrusion Detection System Oriented to Intelligent IoT Models

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Ambient Intelligence – Software and Applications –,10th International Symposium on Ambient Intelligence (ISAmI 2019)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1006 ))

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Abstract

The importance of IoT (internet of things) systems, that allow things to be connected to the Internet and increase their functionalities, is becoming increasingly more relevant. The number of connected devices is growing exponentially. The special features of these devices, and the protocols used in IoT systems, make them more vulnerable to intrusion attacks. New needs arise in terms of network security. To improve the security of an IoT system without affecting the performance of the systems, an IDS (Intrusion Detection Systems) is proposed to detect anomalies in the IoT environment. In order to do so, machine learning techniques as well as the dataset used and the classification method must be taken into account. Our research focuses on the development of an IDS prototype that takes the network frames of an IoT environment using the MQTT protocol, a dataset with a compilation of attacks in a system that uses the protocol, and tests a classification model in a real time environment.

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References

  1. Barakat, S.M.: Internet of Things: Ecosystem and Applications. J. Curr. Res. Sci. 4(1), 32–34 (2017)

    Google Scholar 

  2. Mohn, E.: Internet of Things. Salem Press Encyclopedia of Science (2018)

    Google Scholar 

  3. Lueth, K.L.: The top 20 Internet of Things companies right now, iot-analytics.com, (2015). https://iot-analytics.com/20-internet-of-things-companies/. Accessed 11 Jan 2019

  4. Chernyshev, M., Baig, Z., Bello, O., Zeadally, S.: Internet of things (IoT): Research, simulators, and testbeds. IEEE Internet Things J. 5(3), 1637–1647 (2018)

    Article  Google Scholar 

  5. Granjal, J., Monteiro, E., Sa Silva, J.: Security for the Internet of Things: a survey of existing protocols and open research issues. IEEE Commun. Surv. Tutorials 17(3), 1294–1312 (2015)

    Article  Google Scholar 

  6. Neshenko, N., Bou-harb, E., Crichigno, J., Kaddoum, G., Ghani, N.: Demystifying IoT security: an exhaustive survey on IoT vulnerabilities and a first empirical look on internet-scale IoT exploitations, pp. 1–30, April 2019

    Google Scholar 

  7. Ahmad, M., Younis, T., Habib, M.A., Ashraf, R., Ahmed, S.H.: A review of current security issues in Internet of Things. In: Jan, M.A., Khan, F., Alam, M. (eds.) Recent Trends and Advances in Wireless and IoT-enabled Networks, pp. 11–23. Springer, Cham (2019)

    Chapter  Google Scholar 

  8. Kolias, C., Kambourakis, G., Stavrou, A., Voas, J.: DDoS in the IoT: mirai and other botnets. Comput. (Long. Beach. Calif) 50(7), 80–84 (2017)

    Google Scholar 

  9. Garcia-Teodoro, P., Diaz-Verdejo, J., Maciá-Fernández, G., Vázquez, E.: Anomaly-based network intrusion detection: Techniques, systems and challenges. Comput. Secur. 28(1), 18–28 (2009)

    Article  Google Scholar 

  10. Al-Mandhari, I.S., Guan, L., Edirisinghe, E.A.: Investigating the effective use of machine learning algorithms in network intruder detection systems. In: Advances in Information and Communication Networks, pp. 145–161 (2019)

    Google Scholar 

  11. Sethi, P., Sarangi, S.R.: Internet of Things : Architectures, Protocols, and Applications, vol. 2017 (2017)

    Article  Google Scholar 

  12. Tavallaee, M., Bagheri, E., Lu, W., Ghorbani, A.A.: A detailed analysis of the KDD CUP 99 data set. In: IEEE Symposium on Computational Intelligence in Security and Defense Application CISDA 2009, no. Cisda, pp. 1–6 (2009)

    Google Scholar 

  13. Aggarwal, P., Sharma, S.K.: Analysis of KDD dataset attributes - class wise for intrusion detection. Procedia Comput. Sci. 57, 842–851 (2015)

    Article  Google Scholar 

  14. Moustafa, N., Slay, J.: UNSW-NB15: a comprehensive data set for network intrusion detection systems (UNSW-NB15 network data set). In: Proceedings of 2015 Military Communications and Information Systems Conference MilCIS 2015, pp. 1–6 (2015)

    Google Scholar 

  15. Kolias, C., Kambourakis, G., Stavrou, A., Gritzalis, S.: Intrusion detection in 802.11 networks: empirical evaluation of threats and a public dataset. IEEE Commun. Surv. Tutorials 18(1), 184–208 (2016)

    Article  Google Scholar 

  16. Sun, S., Ye, Z., Yan, L., Su, J., Wang, R.: Wrapper feature selection based on lightning attachment procedure optimization and support vector machine for intrusion detection. In: Proceedings 2018 IEEE 4th International Symposium Wireless Systems Within International Conferences Intelligent Data Acquisition Advanced Computing Systems, pp. 41–46 (2018)

    Google Scholar 

  17. Araújo, N., De Oliveira, R., Ferreira, E., Shinoda, A.A., Bhargava, B.: Identifying important characteristics in the KDD99 intrusion detection dataset by feature selection using a hybrid approach. In: 2010 17th International Conference on Telecommunications, ICT 2010, pp. 552–558 (2010)

    Google Scholar 

  18. Aminanto, M.E., Choi, R., Tanuwidjaja, H.C., Yoo, P.D., Kim, K.: Deep abstraction and weighted feature selection for Wi-Fi impersonation detection. IEEE Trans. Inf. Forensics Secur. 13(3), 621–636 (2017)

    Article  Google Scholar 

  19. Aminanto, M.E., Tanuwidjaja, H.C., Yoo, P.D., Kim, K.: Wi-Fi intrusion detection using weighted-feature selection for neural networks classifier. In: 2017 International Workshop on Big Data and Information Security, pp. 99–104 (2017)

    Google Scholar 

  20. Tsai, C.F., Hsu, Y.F., Lin, C.Y., Lin, W.Y.: Intrusion detection by machine learning: a review. Expert Syst. Appl. 36(10), 11994–12000 (2009)

    Article  Google Scholar 

  21. Moustafa, N., Hu, J., Slay, J.: A holistic review of network anomaly detection systems: a comprehensive survey. J. Netw. Comput. Appl. 128, 33–55 (2019)

    Article  Google Scholar 

  22. Pajouh, H.H., Javidan, R., Khayami, R., Ali, D., Choo, K.-K.R.: A two-layer dimension reduction and two-tier classification model for anomaly-based intrusion detection in IoT backbone networks. IEEE Trans. Emerg. Top. Comput. 6750(c), 1 (2016)

    Google Scholar 

  23. Alrashdi, I., Alqazzaz, A., Aloufi, E., Alharthi, R., Zohdy, M., Ming, H.: AD-IoT: anomaly detection of IoT cyberattacks in smart city using machine learning. In: 2019 IEEE 9th Annual Computing and Communication Workshop and Conference, pp. 0305–0310 (2019)

    Google Scholar 

  24. Moustafa, N., Turnbull, B., Choo, K.K.R.: An ensemble intrusion detection technique based on proposed statistical flow features for protecting network traffic of internet of things. IEEE Internet Things J. PP(c), 1 (2018)

    Google Scholar 

  25. Benkhelifa, E., Welsh, T., Hamouda, W.: A critical review of practices and challenges in intrusion detection systems for IoT: toward universal and resilient systems. IEEE Commun. Surv. Tutorials 20(4), 3496–3509 (2018)

    Article  Google Scholar 

  26. da Costa, K.A.P., Papa, J.P., Lisboa, C.O., Munoz, R., de Albuquerque, V.H.C.: Internet of Things: a survey on machine learning-based intrusion detection approaches. Comput. Netw. 151, 147–157 (2019)

    Article  Google Scholar 

  27. Alaiz-Moreton, H., Aveleira-Mata, J., Ondicol-Garcia, J., Muñoz-Castañeda, A.L., García, I., Benavides, C.: Multiclass classification procedure for detecting attacks on MQTT-IoT protocol. Complexity 2019, 1–11 (2019)

    Article  Google Scholar 

  28. openwrt.org. https://openwrt.org/. Accessed 15 Jan 2019

  29. iptables-mod-tee. https://openwrt.org/packages/pkgdata/iptables-mod-tee. Accessed 05 Feb 2019

  30. flask. http://flask.pocoo.org/. Accessed 06 Feb 2019

  31. wireshark.org. https://www.wireshark.org/docs/man-pages/tshark.html. Accessed 05 Feb 2019

  32. vuejs.org. https://vuejs.org/. Accessed 06 Feb 2019

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Author information

Authors and Affiliations

  1. Research Institute of Applied Sciences in Cybersecurity (RIASC) MIC, Universidad de León, 24071, León, Spain

    Jose Aveleira-Mata

  2. Department of Electrical Engineering and Systems and Automatic, Universidad de León, León, Spain

    Hector Alaiz-Moreton

Authors
  1. Jose Aveleira-Mata
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  2. Hector Alaiz-Moreton
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Corresponding author

Correspondence to Jose Aveleira-Mata .

Editor information

Editors and Affiliations

  1. Departamento de Informática, Universidade do Minho, Braga, Portugal

    Paulo Novais

  2. Department of Communications, Polytechnic University of Valencia, Valencia, Valencia, Spain

    Jaime Lloret

  3. IoT European Digital Innovation Hub, University of Salamanca, Salamanca, Salamanca, Spain

    Pablo Chamoso

  4. Departamento de Informática, Universidade do Minho, Braga, Portugal

    Davide Carneiro

  5. Escuela de Ingenieros Industriales de Albacete, Departamento de Sistemas Informáticos, Universidad de Castilla-La Mancha, Albacete, Spain

    Elena Navarro

  6. Faculty of Robotics and Design Engineering, Osaka Institute of Technology, Osaka, Japan

    Sigeru Omatu

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Aveleira-Mata, J., Alaiz-Moreton, H. (2020). Functional Prototype for Intrusion Detection System Oriented to Intelligent IoT Models. In: Novais, P., Lloret, J., Chamoso, P., Carneiro, D., Navarro, E., Omatu, S. (eds) Ambient Intelligence – Software and Applications –,10th International Symposium on Ambient Intelligence. ISAmI 2019. Advances in Intelligent Systems and Computing, vol 1006 . Springer, Cham. https://doi.org/10.1007/978-3-030-24097-4_22

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