Abstract
The Internet-of-Things (IoT) relies on the TCP protocol to transport data from a source to a destination. Making it vulnerable to DDoS using the TCP SYN attack on Cyber-Physical Systems (CPS). Thus, with a potential propagation to the different servers located in both fog and the cloud infrastructures of the CPS. This study compares the effectiveness of supervised, unsupervised, semi-supervised machine learning algorithms, as well as statistical models for detecting DDoS attacks in CPS-IoT.
The models considered are broadly grouped into three: (i) ML-based detection - Logistic Regression, K-Means, and Artificial Neural Networks with two variants based on traffic slicing. We also investigated the effectiveness of semi-supervised hybrid learning models, which used unsupervised K-Means to label the data, then fed the output to a supervised learning model for attack detection. (ii) Statistic-based detection - Exponentially Weighted Moving Average and Linear Discriminant Analysis. (Iii) Prediction ‘algorithms - LGR, Kernel Ridge Regression and Support Vector Regression. Results of simulations showed that the hybrid model was able to achieve 100% accuracy with near zero false positives for all the ML models, while traffic slicing traffic helped improved detection time; the statistical models performed comparatively poorly, while the prediction models were able to achieve over 94% attack prediction accuracy.
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References
Ajayi, O.O., Bagula, A.B., Maluleke, H.C., Odun-Ayo, I.A.: Transport inequalities and the adoption of intelligent transportation systems in Africa: a research landscape. Sustainability 13(22), 12891 (2021)
Bagula, A., Mandava, M., Bagula, H.: A framework for healthcare support in the rural and low income areas of the developing world. J. Netw. Comput. Appl. 120, 17–29 (2018)
Ismail, A., Bagula, B.A., Tuyishimire, E.: Internet-of-Things in motion: a UAV coalition model for remote sensing in smart cities. Sensors 18(7), 2184 (2018)
Ma, K., Bagula, A., Nyirenda, C., Ajayi, O.: An IoT-based Fog computing model. Sensors 19(12), 2783 (2019)
Zennaro, M., Bagula, A.: Design of a flexible and robust gateway to collect sensor data in intermittent power environments. Int. J. Sens. Netw. 8(3–4), 172–181 (2010)
Bagula, A.B.: Hybrid traffic engineering: the least path interference algorithm. In: Proceedings of the SAICT 2004, ACM International Conference Proceedings Series, pp. 89–96 (2004). ISBN: 1-58113-982-9
Ahmad, R., Alsmadi, I.: Machine learning approaches to IoT security: a systematic literature review. Int. Things 14, 100365 (2021)
AMQP: CloudAMQP. https://www.cloudamqp.com/docs/amqp.html
Pardo-Castellote, G.: Omg data-distribution service: architectural overview. In: Proceedings of IEEE Military Communications Conference (MILCOM), pp. 200–206 (2003)
Anonymous "MQTT FAQ." https://mqtt.org/faq/
Millard, P., Saint-Andre, P., Meijer, R.: "No title," XEP-0060: Publish-Subscribe, XMPP Standards Foundation
Bagula, A., Ajayi, O., Maluleke, H.: Cyber physical systems dependability using CPS-IOT monitoring. Sensors 21(8), 2761 (2021)
Garber, L.: Denial-of-service attacks rip the Internet. Computer 33(04), 12–17 (2000)
Zargar, S.T., Joshi, J., Tipper, D.: A survey of defense mechanisms against distributed denial of service (DDoS) flooding attacks. IEEE Commun. Surv. Tutorials 15(4), 2046–2069 (2013)
Khan, F.I., Hameed, S.: Understanding security requirements and challenges in internet of things (IoTs): a review. arXiv preprint arXiv:1808.10529
Singh, K., Singh, P., Kumar, K.: Application layer HTTP-GET flood DDoS attacks: research landscape and challenges. Comput. Secur. 65, 344–372 (2017)
Hosseini, S., Azizi, M.: The hybrid technique for DDoS detection with supervised learning algorithms. Comput. Netw. 158, 35–45 (2019)
Wang, M., Lu, Y., Qin, J.: A dynamic MLP-based DDoS attack detection method using feature selection and feedback. Comput. Secur. 88, 101645 (2020)
Chaudhary, P., Gupta, B.B.: Ddos detection framework in resource constrained internet of things domain. In: Proceedings of IEEE Global Conference on Consumer Electronics (GCCE), pp. 675–678 (2019)
Wehbi, K., Hong, L., Al-salah, T., Bhutta, A.A.: A survey on machine learning based detection on DDoS attacks for IoT systems. In: Proceedings of the IEEE Southeastcon, pp. 1–6 (2019)
Polat, H., Polat, O., Cetin, A.: Detecting DDoS attacks in software-defined networks through feature selection methods and machine learning models. MDPI Sustain. 12(3), 1035 (2020)
Lichman, M.: DARPA intrusion detection evaluation dataset. DARPA Intrusion Detection Evaluation Dataset—MIT Lincoln Laboratory (2000)
Machaka, P., Bagula, A.: Statistical properties and modelling of DDoS attacks. In: Vinh, P.C., Rakib, A. (eds.) Context-Aware Systems and Applications, and Nature of Computation and Communication. ICCASA ICTCC 2020 2020. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol. 343. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-67101-3_4
Morissette, L., Chartier, S.: The k-means clustering technique: general considerations and implementation in Mathematica. Tutorials Quant. Methods Psychol. 9(1), 15–24 (2013)
Roberts, S.W.: Control chart tests based on geometric moving averages. Technometrics 1(3), 239–250 (1959)
Theodoridis, S.: Classification: a tour of the classics. In: Theodoridis, S., Ed. Machine Learning, pp. 275–325. Academic Press, London (2015)
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Machaka, P., Ajayi, O., Kahenga, F., Bagula, A., Kyamakya, K. (2023). Modelling DDoS Attacks in IoT Networks Using Machine Learning. In: Masinde, M., Bagula, A. (eds) Emerging Technologies for Developing Countries. AFRICATEK 2022. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 503. Springer, Cham. https://doi.org/10.1007/978-3-031-35883-8_11
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