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Automatic Control and Computer Sciences

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01-12-2023

Early Detection of Network Attacks Based on Weight-Insensitive Neural Networks

Authors: D. S. Lavrova, O. A. Izotova

Published in: Automatic Control and Computer Sciences | Issue 8/2023

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Abstract

In this paper, we describe an approach for the early detection of network attacks using weight-insensitive neural networks (or weight agnostic neural networks (WANNs). The selection of the type of neural networks is determined by the specifics of their architecture, which provides high data-processing speed and performance, which is significant when solving the problem of the early detection of attacks. The experimental studies demonstrate the effectiveness of the proposed approach, which is based on a combination of multiple regression for selecting features of the training set and WANNs. The accuracy of attack recognition is comparable to the best results in this field with a significant gain in time.

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Zegzhda, D., Lavrova, D., Pavlenko, E., and Shtyrkina, A., Cyber attack prevention based on evolutionary cybernetics approach, Symmetry, 2020, vol. 12, no. 11, p. 1931. https://doi.org/10.3390/sym12111931ADSCrossRef

Kalinin, M., Zegzhda, D., and Zavadskii, E., Protection of energy network infrastructures applying a dynamic topology virtualization, Energies, 2022, vol. 15, no. 11, p. 4123. https://doi.org/10.3390/en15114123CrossRef

Poltavtseva, Maria.A., Kharitonova, A.N., and Lavrova, D.S., Specifics of communication organization and attacks on medical cyber-physical devices, 2021 Int. Conf. Engineering and Telecommunication (En&T), Dolgoprudny, Moscow oblast, 2022, IEEE, 2022, pp. 1–4. https://doi.org/10.1109/ent50460.2021.9681727

Krundyshev, V. and Kalinin, M., Prevention of cyber attacks in smart manufacturing applying modern neural network methods, IOP Conf. Ser.: Mater. Sci. Eng., 2020, vol. 940, no. 1, p. 012011. https://doi.org/10.1088/1757-899x/940/1/012011

Topical cyber threats: Results of 2020, 2021. https://www.ptsecurity.com/ru-ru/research/analytics/cybersecurity-threatscape-2020/. Cited July 17, 2023.

Topical cyber threats: Results of 2021, 2022. https://www.ptsecurity.com/ru-ru/research/analytics/cybersecurity-threatscape-2021/. Cited July 17, 2023.

Krundyshev, V. and Kalinin, M., Hybrid neural network framework for detection of cyber attacks at smart infrastructures, Proc. 12th Int. Conf. on Security of Information and Networks, Association for Computing Machinery, 2019, p. 3357623. https://doi.org/10.1145/3357613.3357623

Kalinin, M.O., Zubkov, E.A., Suprun, A.F., and Pechenkin, A.I., Prevention of attacks on dynamic routing in self-organizing adhoc networks using swarm intelligence, Autom. Control Comput. Sci., 2018, vol. 52, no. 8, pp. 977–983. https://doi.org/10.3103/s0146411618080163CrossRef

Krundyshev, V., Kalinin, M., and Zegzhda, P., Artificial swarm algorithm for VANET protection against routing attacks, 2018 IEEE Industrial Cyber-Physical Systems (ICPS), IEEE, 2018, pp. 795–800. https://doi.org/10.1109/icphys.2018.8390808CrossRef

10.

Belenko, V., Krundyshev, V., and Kalinin, M., Synthetic datasets generation for intrusion detection in VANET, Proc. 11th Int. Conf. on Security of Information and Networks, Cardiff, UK, 2018, New York: Association for Computing Machinery, 2018, p. 9. https://doi.org/10.1145/3264437.3264479

11.

Zegzhda, D.P., Kalinin, M.O., Krundyshev, V.M., Lavrova, D.S., Moskvin, D.A., and Pavlenko, E.Yu., Application of bioinformatics algorithms for polymorphic cyberattacks detection, Inf. Avtom., 2021, vol. 20, no. 4, pp. 820–844. https://doi.org/10.15622/ia.20.4.3CrossRef

12.

Stepanov, M.D., Pavlenko, E.Yu., and Lavrova, D.S., Detecting network attacks on software configured networks using the isolating forest algorithm, Autom. Control Comput. Sci., 2021, vol. 55, no. 8, pp. 1039–1050. https://doi.org/10.3103/s0146411621080307CrossRef

13.

Khraisat, A., Gondal, I., Vamplew, P., and Kamruzzaman, J., Survey of intrusion detection systems: Techniques, datasets and challenges, Cybersecurity, 2019, vol. 2, no. 1, p. 20. https://doi.org/10.1186/s42400-019-0038-7CrossRef

14.

Moon, D., Im, H., Kim, I., and Park, J.H., DTB-IDS: An intrusion detection system based on decision tree using behavior analysis for preventing APT attacks, J. Supercomput., 2017, vol. 73, no. 7, pp. 2881–2895. https://doi.org/10.1007/s11227-015-1604-8CrossRef

15.

Rai, K., Devi, M.S., and Guleria, A., Decision tree based algorithm for intrusion detection, Int. J. Adv. Networking Appl., 2016, vol. 7, no. 4, pp. 2828–2834.

16.

Ingre, B., Yadav, A., and Soni, A.K., Decision tree based intrusion detection system for NSL-KDD dataset, Information and Communication Technology for Intelligent Systems (ICTIS 2017)-Volume 2, Smart Innovation, Systems and Technologies, vol. 84, Cham: Springer, 2017, pp. 207–218. https://doi.org/10.1007/978-3-319-63645-0_23CrossRef

17.

Kumar, M., Hanumanthappa, M., and Kumar, T.V.S., Intrusion detection system using decision tree algorithm, 2012 IEEE 14th Int. Conf. on Communication Technology, Chengdu, China, 2012, IEEE, 2012, pp. 629–634. https://doi.org/10.1109/icct.2012.6511281

18.

Goyal, A. and Kumar, Ch., GA-NIDS: A genetic algorithm based network intrusion detection system, Northwestern University, 2008, vol. 178, no. 15, pp. 3024–3042.

19.

Li, W., Hybrid neural network intrusion detection system using genetic algorithm, 2010 Int. Conf. on Multimedia Technology, Ningbo, China, 2010, IEEE, 2004, pp. 1–8. https://doi.org/10.1109/icmult.2010.5631462

20.

Kshirsagar, V.P., Tidke, S.M., and Vishnu, S.S., Intrusion detection system using genetic algorithm and data mining: An overview, Int. J. Comput. Sci. Inf., 2012, vol. 2, no. 2, pp. 118–122. https://doi.org/10.47893/ijcsi.2012.1076CrossRef

21.

Bhattacharjee, P.S., Md Fujail, A.K., and Begum, S.A., A comparison of intrusion detection by k-means and fuzzy C-means clustering algorithm over the NSL-KDD dataset, 2017 IEEE Int. Conf. on Computational Intelligence and Computing Research (ICCIC), Coimbatore, India, 2017, IEEE, 2017, pp. 235–246. https://doi.org/10.1109/iccic.2017.8524401

22.

Hoque, M.S., Mukit, Md.A., and Bikas, A.N., An implementation of intrusion detection system using genetic algorithm, 2012. https://doi.org/10.48550/arXiv.1204.1336

23.

Vinayakumar, R., Soman, K.P., and Poornachandran, P., Evaluation of recurrent neural network and its variants for intrusion detection system (IDS), Int. J. Inf. Syst. Model. Des., 2017, vol. 8, no. 3, pp. 43–63. https://doi.org/10.4018/ijismd.2017070103CrossRef

24.

Ádám, N., Madoš, B., Baláž, A., and Pavlik, T., Artificial neural network based IDS, IEEE 15th Int. Symp. on Applied Machine Intelligence and Informatics (SAMI), Herl’any, Slovakia, 2017, IEEE, 2017, pp. 159–164. https://doi.org/10.1109/SAMI.2017.7880294

25.

Vorobeva, Yu.N., Kataseva, D.V., Katasev, A.S., and Kirpichnikov, A.P., Neural network model of detecting DDOS-attacks, Vestn. Tekhnologicheskogo Univ., vol. 21, no. 2, pp. 94–98.

26.

Yin, C., Zhu, Yu., Fei, J., and He, X., A deep learning approach for intrusion detection using recurrent neural networks, IEEE Access, 2017, vol. 5, pp. 21954–21961. https://doi.org/10.1109/access.2017.2762418CrossRef

27.

Wang, W., Zhu, M., Zeng, X., Ye, X., and Sheng, Yi., Malware traffic classification using convolutional neural network for representation learning, Int. Conf. on Information Networking (ICOIN), Da Nang, Vietnam, 2017, IEEE, 2017, pp. 712–717. https://doi.org/10.1109/ICOIN.2017.7899588

28.

Subba, B., Biswas, S., and Karmakar, S., A neural network based system for intrusion detection and attack classification, 2016 Twenty Second National Conf. on Communication (NCC), Guwahati, India, 2016, IEEE, 2016, pp. 1–6. https://doi.org/10.1109/ncc.2016.7561088

29.

Haider, S., Akhunzada, A., Mustafa, I., Patel, T.B., Fernandez, A., Choo, K.-K.R., and Iqbal, J., A deep CNN ensemble framework for efficient DDoS attack detection in software defined networks, IEEE Access, 2020, vol. 8, pp. 53972–53983. https://doi.org/10.1109/access.2020.2976908CrossRef

30.

Tekerek, A., A novel architecture for web-based attack detection using convolutional neural network, Comput. Secur., 2021, vol. 100, p. 102096. https://doi.org/10.1016/j.cose.2020.102096CrossRef

31.

Zhang, X., Chen, J., Zhou, Yu., Han, L., and Lin, J., A multiple-layer representation learning model for network-based attack detection, IEEE Access, 2019, vol. 7, pp. 91992–92008. https://doi.org/10.1109/access.2019.2927465CrossRef

32.

Pascanu, R., Stokes, J.W., Sanossian, H., Marinescu, M., and Thomas, A., Malware classification with recurrent networks, 2015 IEEE Int. Conf. on Acoustics, Speech and Signal Processing (ICASSP), South Brisbane, Australia, 2015, IEEE, 2015, pp. 1916–1920. https://doi.org/10.1109/icassp.2015.7178304

33.

Staudemeyer, R.C., Applying long short-term memory recurrent neural networks to intrusion detection, S. Afr. Comput. J., 2015, vol. 56, pp. 136–154. https://doi.org/10.18489/sacj.v56i1.248CrossRef

34.

Kim, J., Kim, J., Thi Thu, H.L., and Kim, H., Long short term memory recurrent neural network classifier for intrusion detection, 2016 Int. Conf. on Platform Technology and Service (PlatCon), Jeju, Korea (South), 2016, IEEE, 2016, pp. 1–5. https://doi.org/10.1109/platcon.2016.7456805

35.

Shettar, P., Kachavimath, A.V., Mulla, M.M., G, N.D., and Hanchinmani, G., Intrusion detection system using MLP and chaotic neural networks, 2021 Int. Conf. on Computer Communication and Informatics (ICCCI), Coimbatore, India, 2021, IEEE, 2021, pp. 1–4. https://doi.org/10.1109/iccci50826.2021.9457024

36.

Mustafaev, A.G., Neural network system for detectin computer attacks based on network traffic analysis, Vopr. Bezopasnosti, 2016, no. 2, pp. 1–7. https://doi.org/10.7256/2409-7543.2016.2.18834

37.

Demertzis, K., Tsiknas, K., Takezis, D., Skianis, C., and Iliadis, L., Darknet traffic big-data analysis and network management for real-time automating of the malicious intent detection process by a weight agnostic neural networks framework, Electronics, 2021, vol. 10, no. 7, p. 781. https://doi.org/10.3390/electronics10070781CrossRef

38.

William, P., Choubey, S., Choubey, A., and Verma, A., Darknet traffic analysis and network management for malicious intent detection by neural network frameworks, Using Computational Intelligence for the Dark Web and Illicit Behavior Detection, IGI Global, 2022, pp. 1–19. https://doi.org/10.4018/978-1-6684-6444-1.ch001CrossRef

39.

Zhong, S., Liu, D., Lin, L., Zhao, M., Fu, X., and Guo, F., A novel anomaly detection method for gas turbines using weight agnostic neural network search, 2020 Asia-Pacific Int. Symp. on Advanced Reliability and Maintenance Modeling (APARM), Vancouver, Canada, 2020, IEEE, 2020, pp. 1–6. https://doi.org/10.1109/aparm49247.2020.9209368

40.

Sharafaldin, I., Lashkari, A.H., and Ghorbani, A.A., Toward generating a new intrusion detection dataset and intrusion traffic characterization, 4th Int. Conf. on Information Systems Security and Privacy (ICISSP), 2018, pp. 108–116. https://doi.org/10.5220/0006639801080116

Title: Early Detection of Network Attacks Based on Weight-Insensitive Neural Networks
Authors: D. S. Lavrova
O. A. Izotova
Publication date: 01-12-2023
Publisher: Pleiades Publishing
Published in: Automatic Control and Computer Sciences / Issue 8/2023
Print ISSN: 0146-4116
Electronic ISSN: 1558-108X
DOI: https://doi.org/10.3103/S014641162308014X

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