Abstract
The current stage of development of cybersecurity has led to the need to create new and improve old methods of data analysis. The paper proposes a novel approach to early intrusion detection based on the analysis of the spectral plane of the signal and the detail coefficients obtained by wavelet transform. The approach makes it possible to consider data not only in the frequency domain, but also in the time domain, which greatly simplifies the localization of anomalies. Wavelet analysis can effectively extract information from a signal and is suitable for anomaly detection, while energy spectrum analysis allows you to determine the physical nature of this signal and implement its suppression or filtering. The approach represents the signal at different frequency values. Different wavelets have several decomposition levels, and each level has a different center frequency. The energy spectrum of the signal was reconstructed from the wavelet coefficients. For a given energy spectrum, the energy cumulate at high, medium and low frequencies was calculated. Experimental results have shown that this approach is well suited for detecting anomalies in network traffic and can be applied to detect new attacks.
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References
Kotenko, I., Saenko, I., Lauta, O.: Analytical modeling and assessment of cyber resilience on the base of stochastic networks conversion. In: 10th International Workshop on Resilient Networks Design and Modeling, Longyearbyen, Norway, pp. 1–8 (2018)
Vokorokos, L., Kleinova, A., Latka, O.: Network security on the intrusion detection system level. In: 2006 International Conference on Intelligent Engineering Systems, London, UK, pp. 270–275 (2006)
Du, Z., Ma, L., Li, H., Li, Q., Sun, G., Liu, Z.: Network traffic anomaly detection based on wavelet analysis. In: 16th International Conference on Software Engineering Research, Management and Applications, Kunming, China, pp. 94–101 (2018)
Liu, W., Duan, H., Wang, P., Wu, J., Yang, L.: Wavelet-based analysis of network security databases. In: Proceedings of the International Conference on Communication Technology, Beijing, China, vol. 1, pp. 372–377 (2003)
Sutha, S., Kamaraj, N.: Combined wavelet transform and ANN for power system security analysis. In: TENCON 2008 - 2008 IEEE Region 10 Conference, Hyderabad, India, pp. 1–6 (2008)
Misiti, M., Misiti, Y., Oppenheim, G., Poggi, J.: Wavelets and Their Applications. John Wiley & Sons, Ltd, London (2007)
Langi, A.Z.R., Pitara, S.W., Kuspriyanto: Stock prices trends analysis using wavelet transform. In: International Conference on Cloud Computing and Social Networking, Bandung, Indonesia, pp. 1–4 (2012)
Niu, D., Diao, L., Zang, Z., Che, H., Zhang, T., Chen, X.: A machine-learning approach combining wavelet packet denoising with catboost for weather forecasting. Atmosphere 12, 1618 (2021)
Dremin, I.M., Furletov, V.I., Ivanov, O.V., Nechitailo, V.A., Terziev, V.G.: Precursors of stall and surge processes in gas turbines revealed by wavelet analysis. Control. Eng. Pract. 10, 599–604 (2002)
Dhana Lakshmi, K.V., Ram, S.S.T., Subbrahmanyam, J., Kumar G.R.: Significance of wavelet and IOT techniques in micro-grid based power system protection. In: International Symposium on Sustainable Energy, Signal Processing and Cyber Security, Gunupur Odisha, India, pp. 1–5 (2020)
Salagean, M.: Real network traffic anomaly detection based on analytical discrete wavelet transform. In: 12th International Conference on Optimization of Electrical and Electronic Equipment, Brasov, Romania, pp. 926–931 (2010)
Zhang, F., Dong, X., Yang, B., Zhou, Y., Ren, K.: A systematic evaluation of wavelet-based attack framework on random delay countermeasures. IEEE Trans. Inf. Forensics Secur. 15, 1407–1422 (2019)
Petrik, B., Dubrovin, V., Nelasa, H., Tverdokhlib, Y.: Network Intrusion monitoring system wavelet analysis traffic. In: International Conference on Problems of Infocommunications, Science and Technology, Kharkiv, Ukraine, pp. 61–66 (2020)
Klein, R.W., Temple, M.A., Mendenhall, M.J.: Application of wavelet-based RF fingerprinting to enhance wireless network security. J. Commun. Networks 11, 544–555 (2009)
Bozdal, M., Samie, M., Jennions, I.K.: WINDS: a wavelet-based intrusion detection system for Controller Area Network (CAN). IEEE Access 9, 58621–58633 (2021)
Jibao, L., Huiqiang, W., Xiaowu, L., Ying, L.: A quantitative prediction method of network security situation based on wavelet neural network. In: The First International Symposium on Data, Privacy, and e-Commerce, Chengdu, China, pp. 197–202 (2007)
Hu, J., Zhang, Y., Zou, C., Liu, J.: Intrusion prediction algorithm based on modified wavelet neural network. In: 4th International Conference on Information Communication and Signal Processing, Shanghai, Chine, pp. 632–636 (2021)
Rafiei, M., Niknam, T., Khooban, M.: Probabilistic forecasting of hourly electricity price by generalization of ELM for usage in improved wavelet neural network. IEEE Trans. Industr. Inf. 13, 71–79 (2017)
Xiaoli, Z., Xiangjun, Z., Li, L., Choi, S., Yuanyuan, W.: Fault location using wavelet energy spectrum analysis of traveling waves. In: International Power Engineering Conference, Singapore, pp. 1126–1130 (2007)
Salwani, M., Jasmy, Y.: Relative wavelet energy as a tool to select suitable wavelet for artifact removal in EEG. In: 1st International Conference on Computers, Communications, and Signal Processing with Special Track on Biomedical Engineering, Kuala Lumpur, Malaysia, pp. 282–287 (2005)
Acknowledgements
The research was supported by the grant of the Russian Science Foundation No. 23-11-20024, https://rscf.ru/en/project/23-11-20024/, and Saint-Petersburg Science Foundation.
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Saenko, I., Bortniker, P., Lauta, O., Zhdanova, I., Vasiliev, N. (2023). An Approach to Early Computer Network Intrusion Detection Based on the Wavelet Transform Energy Spectra Analysis. In: Kovalev, S., Kotenko, I., Sukhanov, A. (eds) Proceedings of the Seventh International Scientific Conference “Intelligent Information Technologies for Industry” (IITI’23). IITI 2023. Lecture Notes in Networks and Systems, vol 777. Springer, Cham. https://doi.org/10.1007/978-3-031-43792-2_7
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