Maxim Kalinin
ABSTRACT
The paper reviews actual tasks of intrusion prevention for the reconfigurable networks, the smart networks, that arrange a self-organizing adhoc cyberspace of the Internet of Things (IoT), VANET, FANET, WSN. The smart networks are characterized with heterogeneity, structural complexity, and dynamic topology that are causing the new security threats. For them, the conventional methods of intrusion detection take a poor effect. Our study sets a comprehensive approach that aggregates the artificial intelligence (AI) based protection erecting a multimodal intrusion prevention approach. It combines the advanced artificial neural networks for effective classification of the security intrusions on the big datasets, the bioinformatics algorithms for the similarity matching of the operational sequences, and the swarm intelligence for the higher trustiness of the interhost communications. Our solution has shown the higher effectiveness than a traditional technique of the intensive data processing.
- 2019. Penetration testing of corporate information systems: statistics and findings. Retrieved February 20, 2020 from www.ptsecurity.com/ww-en/analytics/corp-vulnerabilities-2019Google Scholar
- Halimaa A. and Sundarakantham K.2019. Machine learning based intrusion detection system. In Proceedings of the International Conference on Trends in Electronics and Informatics. 916–920. https://doi.org/10.1109/ICOEI.2019.8862784Google Scholar
- Grosan C., Abraham A., and Chis M.2006. Swarm intelligence in data mining. Studies in Computational Intelligence(2006). https://doi.org/10.1007/978-3-540-34956-3_1Google Scholar
- Yin C., Zhu Y., Fei J., and He X.2017. A deep learning approach for intrusion detection Using Recurrent Neural Networks. IEEE Access 5(2017), 21954–21961. https://doi.org/10.1109/ACCESS.2017.2762418Google ScholarDigital Library
- Lavrova D., Zegzhda D., and Yarmak A.2019. Using GRU neural network for cyber-attack detection in automated process control systems. In IEEE International Black Sea Conference on Communications and Networking. Article 8812818. https://doi.org/10.1109/BlackSeaCom.2019.8812818Google Scholar
- Zegzhda D., Lavrova D., and Khushkeev A.2019. Detection of information security breaches in distributed control systems based on values prediction of multidimensional time series. In Proceedings of 2019 IEEE International Conference on Industrial Cyber Physical Systems. 780–784. https://doi.org/10.1109/ICPHYS.2019.8780304Google ScholarCross Ref
- Lakshminarayana D.H., Philips J., , and Tabrizi N.2019. A survey of intrusion detection techniques. In Proc. of 18th IEEE International Conference on Machine Learning and Applications. 1122–1129.Google Scholar
- Zegzhda D.P. and Pavlenko E.Y.2018. Digital manufacturing security indicators. Automatic Control and Computer Sciences 52, 8 (2018), 1150–1159. https://doi.org/10.3103/S0146411618080333Google ScholarCross Ref
- Ivanov D.V. and Moskvin D. A.2019. Application of fractal methods to ensure the cyber-resilience of self-organizing networks. Nonlinear Phenomena in Complex Systems 22, 4 (2019), 336–341. https://doi.org/10.33581/1561-4085-2019-22-4-336-341Google ScholarCross Ref
- Kabir E., Hu J., Wang H., and Zhuo G.2016. A novel statistical technique for intrusion detection systems. Future Generation Computer Systems 79 (2016), 303–318. https://doi.org/10.1016/j.future.2017.01.029Google ScholarCross Ref
- Pavlenko E. and Zegzhda D.2018. Architecture of homeostatic security control for digital manufacture systems based on software-defined networks. In SHS Web of Conferences, Vol. 44. Article 00067. https://doi.org/10.1051/shsconf/20184400067Google Scholar
- Malyshev E.V., Moskvin D.A., and Zegzhda D.P.2019. Application of an artificial neural network for detection of attacks in VANETs. Automatic Control and Computer Sciences 53, 8 (2019), 889–894. https://doi.org/10.3103/S0146411619080194Google Scholar
- Tseng F.H., Der Chou L., and Chao H.C.2011. A survey of black hole attacks in wireless mobile ad hoc networks. Human-centric Computing and Information Sciences 1, 1 (2011), 1–16.Google ScholarCross Ref
- Schmidhuber J.2015. Deep learning in neural networks: An overview. Neural Networks 61(2015), 85–117. https://doi.org/10.1016/j.neunet.2014.09.003Google ScholarDigital Library
- Kambourakis G. Kolias C. and Maragoudakis M.2011. Swarm intelligence in intrusion detection: A survey. Computers and Security 30, 8 (2011), 625–642. https://doi.org/10.1016/j.cose.2011.08.009Google ScholarDigital Library
- Caterini L. and Chang D. E.2018. Recurrent neural networks. Springer Briefs in Computer Science, Article 9783319753034 (2018), 59-79 pages.Google Scholar
- Ahmed M., Naser Mahmood A., and Hu J.2016. A survey of network anomaly detection techniques. Journal of Network and Computer Applications 60 (2016), 19–31. https://doi.org/10.1016/j.jnca.2015.11.016Google ScholarDigital Library
- Khan M.A. and Salah K.2018. IoT security: Review, blockchain solutions, and open challenges. Future Generation Computer Systems 82 (2018), 395–411.Google ScholarCross Ref
- Poltavtseva M.A., Zegzhda D.P., and Pavlenko E.Y.2019. High-performance NIDS architecture for enterprise networking. In IEEE International Black Sea Conference on Communications and Networking. Article 8812808. https://doi.org/10.1109/BlackSeaCom.2019.8812808Google Scholar
- Faker O. and Dogdu E.2019. Intrusion detection using big data and deep learning techniques. In Proceedings of the 2019 ACM Southeast Conference. 86–93. https://doi.org/10.1145/3299815.3314439Google Scholar
- Mott R.2005. Smith-Waterman algorithm. Encyclopedia of Life Sciences(2005).Google Scholar
- Rojas R. and Rojas R.1996. Perceptron learning. Neural Networks (1996), 77–98.Google Scholar
- Demidov R.A., Pechenkin A.I., Zegzhda P.D., and Kalinin M.O.2018. Application model of modern artificial neural network methods for the analysis of information systems security. Automatic Control and Computer Sciences 52, 8 (2018), 965–970. https://doi.org/10.3103/S0146411618080072Google ScholarCross Ref
- Demidov R.A., Zegzhda P.D., and Kalinin M.O.2018. Threat analysis of cyber security in wireless adhoc networks using hybrid neural network model. Automatic Control and Computer Sciences 52, 8 (2018), 971–976. https://doi.org/10.3103/S0146411618080084Google ScholarCross Ref
- Hochreiter S. and Schmidhuber J.1997. Long Short-Term Memory. Neural Computation 9, 8 (1997), 1735–1780. https://doi.org/10.1162/neco.1997.9.8.1735Google ScholarDigital Library
- Jatti S.A.V. and Kishor Sontif V.J.K.2019. Intrusion detection systems. International Journal of Recent Technology and Engineering 8, 2(2019), 3976–3983. Issue 11.Google ScholarCross Ref
- Platonov V.V. and Semenov P.O.2015. Using data-mining methods to detect network attacks. Automatic Control and Computer Sciences 49, 8 (2015), 766–769. https://doi.org/10.3103/S0146411615080131Google ScholarCross Ref
- Lee W., Stolfo S.J., and Mok K.W.2000. Adaptive intrusion detection: A data mining approach. Artificial Intelligence Review 14, 6 (2000), 533–567.Google ScholarDigital Library
Index Terms
- AI-based Security for the Smart Networks
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