Abstract
IoT end-user devices are attractive and sometime easy targets for attackers, because they are often vulnerable in different aspects. Cyberattacks, started from those devices, can easily disrupt the availability of services offered by major internet companies. People that commonly get access to them across the world may experience abrupt interruption of services they use. In that context, this paper describes an embedded prototype to classify intrusions, affecting TCP packets. The proposed solution adopts an Artificial Neural Network (ANN) executed on resource-constrained and low-cost embedded micro controllers. The prototype operates without the need of remote intelligence assist. The adoption of an on-the-edge artificial intelligence architecture brings advantages such as responsiveness, promptness and low power consumption. The embedded intelligence is trained by using the well-known KDD Cup 1999 dataset, properly balanced on 5 types of labelled intrusions patterns. A pre-trained ANN classifies features extracted from TCP packets. The results achieved in this paper refer to the application running on the low cost widely available Nucleo STM32 micro controller boards from STMicroelectronics, featuring a F3 chip running at 72 MHz and a F4 chip running at 84 MHz with small embedded RAM and Flash memory.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Zhang, Z., Cho, M.C.Y., Wang, C., Hsu, C., Chen, C., Shieh, S.: IoT security: ongoing challenges and research opportunities. In: 2014 IEEE 7th International Conference on Service-Oriented Computing and Applications, Matsue, pp. 230–234 (2014)
Sadeghi, A., Wachsmann, C., Waidner, M.: Security and privacy challenges in industrial Internet of Things. In: 2015 52nd ACM/EDAC/IEEE Design Automation Conference (DAC), San Francisco, CA, pp. 1–6 (2015)
Kolias, C., Kambourakis, G., Stavrou, A., Voas, J.: DDoS in the IoT: mirai and other botnets. Computer 50(7), 80–84 (2017)
Buttigieg, R., Farrugia, M., Meli, C.: Security issues in controller area networks in automobiles. In: 2017 18th International Conference on Sciences and Techniques of Automatic Control and Computer Engineering (STA), Monastir, pp. 93–98 (2017)
Miller, C., Valasek, C.: Remote exploitation of an unaltered passenger vehicle. Black Hat USA 2015, 91 (2015)
Mukherjee, B., Heberlein, L.T., Levitt, K.N.: Network intrusion detection. In: IEEE Network, vol. 8, no. 3, pp. 26–41, May–June 1994
STM32Cube.AI. https://www.st.com/en/embedded-software/STM32Cube.AI.html
Young, C., Zambreno, J., Olufowobi, H., Bloom, G.: Survey of automotive controller area network intrusion detection systems. In: IEEE Design & Test. IEEE (2019)
Kang, M.-J., Kang, J.-W.: Intrusion detection system using deep neural network for in-vehicle network security. PLoS ONE 11(6), e0155781 (2016)
Shone, N., Ngoc, T.N., Phai, V.D., Shi, Q.: A deep learning approach to network intrusion detection. IEEE Trans. Emerg. Top. Comput. Intell. 2(1), 41–50 (2018)
Kim, J., Kim, J., Thu, H.L.T., Kim, H.: Long short term memory recurrent neural network classifier for intrusion detection. In: 2016 International Conference on Platform Technology and Service (PlatCon), Jeju, pp. 1–5 (2016)
Faker, O., Dogdu, E.: Intrusion detection using big data and deep learning techniques. In: Proceedings of the 2019 ACM Southeast Conference, pp. 86–93. ACM, April 2019
Raza, S., Wallgren, L., Voigt, T.: SVELTE: Real-time intrusion detection in the Internet of Things. Ad Hoc Netw. 11(8), 2661–2674 (2013)
Lai, L., Suda, N., Chandra, V.: CMSIS-NN: Efficient Neural Network Kernels for Arm Cortex-M CPUs (2018). arXiv preprint arXiv:1801.06601
ARM NN SDK. https://developer.arm.com/products/processors/machine-learning/arm-nn
Renesas e-AI solution. https://www.renesas.com/en-in/solutions/key-technology/e-ai.html
Tensorflow Micro. https://www.tensorflow.org/lite/microcontrollers/overview
Cup, K.D.D.: Dataset (1999). http://kdd.ics.uci.edu/databases/kddcup99/kddcup99
Sharafaldin, I., Lashkari, A.H., Ghorbani, A.A.: Toward generating a new intrusion detection dataset and intrusion traffic characterization. In: 4th International Conference on Information Systems Security and Privacy (ICISSP), Portugal, January 2018
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Varenne, R., Delorme, J.M., Plebani, E., Pau, D., Tomaselli, V. (2019). Intelligent Recognition of TCP Intrusions for Embedded Micro-controllers. In: Cristani, M., Prati, A., Lanz, O., Messelodi, S., Sebe, N. (eds) New Trends in Image Analysis and Processing – ICIAP 2019. ICIAP 2019. Lecture Notes in Computer Science(), vol 11808. Springer, Cham. https://doi.org/10.1007/978-3-030-30754-7_36
Download citation
DOI: https://doi.org/10.1007/978-3-030-30754-7_36
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-30753-0
Online ISBN: 978-3-030-30754-7
eBook Packages: Computer ScienceComputer Science (R0)
