Abstract
Securing Internet of Things networks from cyber security attacks is essential for preventing data loss and safeguarding backbone networks. The resource-constrained nature of the sensor nodes used in the IoT makes them vulnerable to various attacks. Hence, it is important to monitor network traffic information to accurately and promptly identify threats. In this paper, using a machine learning-based framework for learning and detecting such attacks in an IoT network from the network data is proposed. Further, a real IoT network consisting of Raspberry Pi sensor nodes and ZigBee communication modules is built for implementing two cyber attacks. The network traffic information for normal and attack scenarios is collected to evaluate the attack detection performance of learning-based models. We performed a comparison analysis with deep learning and traditional machine learning models. Our evaluation reveals that the proposed features and the machine learning framework can detect attacks with high accuracy from the network traffic information. In particular, the triplet network-based deep learning framework showed promising results in efficiently detecting the attacks from the traffic information with merely a small set of training samples.
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Parmisano, A., Garcia, S., M.J.E.: Stratosphere laboratory - a labeled dataset with malicious and benign IoT network traffic. (2020).https://www.stratosphereips.org/datasets-iot23
Ashraf, I., et al.: A survey on cyber security threats in IoT-enabled maritime industry. IEEE Trans. Intell. Transp. Syst. 1–14 (2022)
Breiman, L.: Random forests. Mach. Learn. 45(1), 5–32 (2001)
Canedo, J., Skjellum, A.: Using machine learning to secure IoT systems. In: Proceeding of the 14th Conference on Privacy, Security and Trust, pp. 219–222. IEEE (2016)
Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y.: Xgboost: extreme gradient boosting. R package version 0.4-2, pp. 1–4 (2015)
Chowdhury, M., Ray, B., Chowdhury, S., Rajasegarar, S.: A novel insider attack and machine learning based detection for the internet of things. ACM Trans. IoT 2(4), 1–23 (2021)
Cortes, C., Vapnik, V.: Support-vector networks. Mach. Lear. 20(3), 273–297 (1995)
DigiXBee: Python library (2022). https://xbplib.readthedocs.io/en/latest/
DigiXBee: Zigbee modules (s2c) (2022). http://www.digi.com/resources/documentation/digidocs/pdfs/90001500.pdf
Erfani, S.M., Rajasegarar, S., Karunasekera, S., Leckie, C.: High-dimensional and large-scale anomaly detection using a linear one-class SVM with deep learning. Pattern Recogn. 58, 121–134 (2016)
Ergen, T., Kozat, S.S.: Unsupervised anomaly detection with LSTM neural networks. IEEE Trans. Neural Net. Learn. Syst. 31(8), 3127–3141 (2019)
MacQueen, J.: Classification and analysis of multivariate observations. In: 5th Berkeley Symposium Mathamatical Statistics Probability, pp. 281–297 (1967)
Markets: IoT solutions & markets (2020). https://www.marketsandmarkets.com/Market-Reports/iot-solutions-and-services-market-120466720.html
Piracha, Waqas Ahmad, Chowdhury, Morshed, Ray, Biplob, Rajasegarar, Sutharshan, Doss, Robin: Insider attacks on Zigbee based IoT networks by exploiting AT commands. In: Shankar Sriram, V.. S.., Subramaniyaswamy, V.., Sasikaladevi, N.., Zhang, Leo, Batten, Lynn, Li, Gang (eds.) ATIS 2019. CCIS, vol. 1116, pp. 77–91. Springer, Singapore (2019). https://doi.org/10.1007/978-981-15-0871-4_6
SchölkopfÜ, B., Williamson, R.C., SmolaÜ, A., Shawe-Taylory, J.: SV estimation of a distribution’s support. Adv. Neural Inf. Process. Syst 41, 582–588 (2000)
Schroff, F., Kalenichenko, D., Philbin, J.: Facenet: a unified embedding for face recognition and clustering. In: CVPR, pp. 815–823 (2015)
Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. In: NIPS, pp. 3104–3112. NIPS’14, MIT Press, USA (2014)
Vaccari, I., Cambiaso, E., Aiello, M.: Remotely exploiting at command attacks on zigbee networks. Secur. Commun. Netw. 1–9 (2017)
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Kanthuru, V.A. et al. (2022). Cyber Attack Detection in IoT Networks with Small Samples: Implementation And Analysis. In: Chen, W., Yao, L., Cai, T., Pan, S., Shen, T., Li, X. (eds) Advanced Data Mining and Applications. ADMA 2022. Lecture Notes in Computer Science(), vol 13725. Springer, Cham. https://doi.org/10.1007/978-3-031-22064-7_10
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DOI: https://doi.org/10.1007/978-3-031-22064-7_10
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