Skip to main content
Erschienen in: Automatic Control and Computer Sciences 8/2020

01.12.2020

Development of the Intrusion Detection System for the Internet of Things Based on a Sequence Alignment Algorithm

verfasst von: M. O. Kalinin, V. M. Krundyshev, B. G. Sinyapkin

Erschienen in: Automatic Control and Computer Sciences | Ausgabe 8/2020

Einloggen, um Zugang zu erhalten

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config
loading …

Abstract

This work presents the method for traffic analysis based on the Needleman–Wunsch global algorithm of sequence alignment. A prototype of the intrusion detection system for the Internet of Things has been developed. Results of the experiments show the proposed approach is promising.
Literatur
1.
Zurück zum Zitat Maayan, G., The IoT Rundown For 2020: Stats, Risks, and Solutions, 2020. https://securitytoday.com/Articles/2020/01/13/The-IoT-Rundown-for-2020.aspx?Page=1. Accessed April 25, 2020. Maayan, G., The IoT Rundown For 2020: Stats, Risks, and Solutions, 2020. https://​securitytoday.​com/​Articles/​2020/​01/​13/​The-IoT-Rundown-for-2020.​aspx?​Page=​1.​ Accessed April 25, 2020.
2.
Zurück zum Zitat IoT under fire: Kaspersky detects more than 100 million attacks on smart devices in H1 2019, 2019. https://www.kaspersky.com/about/press-releases/2019_iot-under-fire-kaspersky-detects-more-than-100-million-attacks-on-smart-devices-in-h1-2019. Accessed April 25, 2020. IoT under fire: Kaspersky detects more than 100 million attacks on smart devices in H1 2019, 2019. https://​www.​kaspersky.​com/​about/​press-releases/​2019_​iot-under-fire-kaspersky-detects-more-than-100-million-attacks-on-smart-devices-in-h1-2019.​ Accessed April 25, 2020.
3.
Zurück zum Zitat Lavrova, D., Pechenkin, A., and Gluhov, V., Applying correlation analysis methods to control flow violation detection in the Internet of Things, Autom. Control Comput. Sci., 2015, vol. 49, no. 8, pp. 735–740.CrossRef Lavrova, D., Pechenkin, A., and Gluhov, V., Applying correlation analysis methods to control flow violation detection in the Internet of Things, Autom. Control Comput. Sci., 2015, vol. 49, no. 8, pp. 735–740.CrossRef
4.
Zurück zum Zitat Zegzhda, D., Lavrova, D., and Poltavtseva, M., Multifractal security analysis of cyberphysical systems, Nonlinear Phenom. Complex Syst. (Dordrecht, Neth.), 2019, vol. 22, no. 2, pp. 196–204. Zegzhda, D., Lavrova, D., and Poltavtseva, M., Multifractal security analysis of cyberphysical systems, Nonlinear Phenom. Complex Syst. (Dordrecht, Neth.), 2019, vol. 22, no. 2, pp. 196–204.
5.
Zurück zum Zitat Platonov, V.V. and Semenov, P.O., An adaptive model of a distributed intrusion detection system, Autom. Control Comput. Sci., 2017, vol. 51, no. 8, pp. 894–898CrossRef Platonov, V.V. and Semenov, P.O., An adaptive model of a distributed intrusion detection system, Autom. Control Comput. Sci., 2017, vol. 51, no. 8, pp. 894–898CrossRef
6.
Zurück zum Zitat Ovasapyan, T.D., Moskvin, D.A., and Kalinin, M.O., Using neural networks to detect internal intruders in VANETs, Autom. Control Comput. Sci., 2018, vol. 52, no. 8, pp. 954–958.CrossRef Ovasapyan, T.D., Moskvin, D.A., and Kalinin, M.O., Using neural networks to detect internal intruders in VANETs, Autom. Control Comput. Sci., 2018, vol. 52, no. 8, pp. 954–958.CrossRef
8.
Zurück zum Zitat Zegzhda, D., Lavrova, D., and Khushkeev, A., Detection of information security breaches in distributed control systems based on values prediction of multidimensional time series, 2019 IEEE International Conference on Industrial Cyber Physical Systems, ICPS 2019, 2019, pp. 780–784. Zegzhda, D., Lavrova, D., and Khushkeev, A., Detection of information security breaches in distributed control systems based on values prediction of multidimensional time series, 2019 IEEE International Conference on Industrial Cyber Physical Systems, ICPS 2019, 2019, pp. 780–784.
10.
Zurück zum Zitat Platonov, V.V. and Semenov, P.O., Using data-mining methods to detect network attacks, Autom. Control Comput. Sci., 2015, vol. 49, no. 8, pp. 766–769.CrossRef Platonov, V.V. and Semenov, P.O., Using data-mining methods to detect network attacks, Autom. Control Comput. Sci., 2015, vol. 49, no. 8, pp. 766–769.CrossRef
11.
Zurück zum Zitat Markov, Y.A., Kalinin, M.O., and Zegzhda, D.P., A technique of abnormal behavior detection with genetic sequences alignment algorithms, International Conference on Enterprise Information Systems and Web Technologies 2010, 2010, pp. 104–110. Markov, Y.A., Kalinin, M.O., and Zegzhda, D.P., A technique of abnormal behavior detection with genetic sequences alignment algorithms, International Conference on Enterprise Information Systems and Web Technologies 2010, 2010, pp. 104–110.
12.
Zurück zum Zitat Markov, Y.A. and Kalinin, M.O., Intellectual intrusion detection with sequences alignment methods, Lect. Notes Comput. Sci., 2010, vol. 6258, pp. 217–228.CrossRef Markov, Y.A. and Kalinin, M.O., Intellectual intrusion detection with sequences alignment methods, Lect. Notes Comput. Sci., 2010, vol. 6258, pp. 217–228.CrossRef
13.
Zurück zum Zitat Coull, S., Branch, J., Szymański, B., and Breimer, E., Intrusion detection: A bioinformatics approach, Proc. 19th Ann. Comput. Secur. Appl. Conf., Washington, 2003. Coull, S., Branch, J., Szymański, B., and Breimer, E., Intrusion detection: A bioinformatics approach, Proc. 19th Ann. Comput. Secur. Appl. Conf., Washington, 2003.
15.
Zurück zum Zitat Malyshev, E.V., Moskvin, D.A., and Zegzhda, D.P., Application of an artificial neural network for detection of attacks in VANETs, Autom. Control Comput. Sci., 2019, vol. 53, no. 8, pp. 889–894.CrossRef Malyshev, E.V., Moskvin, D.A., and Zegzhda, D.P., Application of an artificial neural network for detection of attacks in VANETs, Autom. Control Comput. Sci., 2019, vol. 53, no. 8, pp. 889–894.CrossRef
16.
Zurück zum Zitat Lavrova, D., Zegzhda, D., and Yarmak, A., Predicting cyber attacks on industrial systems using the Kalman filter, 3rd World Conference on Smart Trends in Systems, Security and Sustainability, WorldS4 2019, 2019, pp. 317–321. Lavrova, D., Zegzhda, D., and Yarmak, A., Predicting cyber attacks on industrial systems using the Kalman filter, 3rd World Conference on Smart Trends in Systems, Security and Sustainability, WorldS4 2019, 2019, pp. 317–321.
17.
Zurück zum Zitat Lavrova, D.S., Alekseev, I.V., and Shtyrkina, A.A., Security analysis based on controlling dependences of network traffic parameters by wavelet transformation, Autom. Control Comput. Sci., 2018, vol. 52, no. 8, pp. 931–935.CrossRef Lavrova, D.S., Alekseev, I.V., and Shtyrkina, A.A., Security analysis based on controlling dependences of network traffic parameters by wavelet transformation, Autom. Control Comput. Sci., 2018, vol. 52, no. 8, pp. 931–935.CrossRef
18.
Zurück zum Zitat Lavrova, D.S. and Vasil’ev, Y.S., An ontological model of the domain of applications for the Internet of Things in analyzing information security, Autom. Control Comput. Sci., 2017, vol. 51, no. 8, pp. 817–823.CrossRef Lavrova, D.S. and Vasil’ev, Y.S., An ontological model of the domain of applications for the Internet of Things in analyzing information security, Autom. Control Comput. Sci., 2017, vol. 51, no. 8, pp. 817–823.CrossRef
19.
Zurück zum Zitat Trabelsi, Z. and Hamdy, R., Implementation of a DNA-based anomaly identification system utilizing associative string processor (ASP), ACS/IEEE International Conference on Computer Systems and Applications – AICCSA 2010, Hammamet, 2010, pp. 1–8. Trabelsi, Z. and Hamdy, R., Implementation of a DNA-based anomaly identification system utilizing associative string processor (ASP), ACS/IEEE International Conference on Computer Systems and Applications – AICCSA 2010, Hammamet, 2010, pp. 1–8.
20.
Zurück zum Zitat Chakraborty, A. and Bandyopadhyay, S., FOGSAA: Fast optimal global sequence alignment algorithm, Sci. Rep., 2013, no. 3, p. 1746. Chakraborty, A. and Bandyopadhyay, S., FOGSAA: Fast optimal global sequence alignment algorithm, Sci. Rep., 2013, no. 3, p. 1746.
21.
Zurück zum Zitat Galvez, S., Diaz, D., Hernandez, P., Esteban, F.J., Caballero, J.A., and Dorado, G., Next-generation bioinformatics: Using many-core processor architecture to develop a web service for sequence alignment, Bioinformatics, 2010, no. 26, pp. 683–686. Galvez, S., Diaz, D., Hernandez, P., Esteban, F.J., Caballero, J.A., and Dorado, G., Next-generation bioinformatics: Using many-core processor architecture to develop a web service for sequence alignment, Bioinformatics, 2010, no. 26, pp. 683–686.
22.
Zurück zum Zitat Al-Ibaisi, T., Abu-Dalhoum, A., Al-Rawi, M., Alfonseca, M., and Ortega, A., Network intrusion detection using genetic algorithm to find best DNA signature, WSEAS Trans. Syst., 2008, vol. 7, no. 7, pp. 589–599. Al-Ibaisi, T., Abu-Dalhoum, A., Al-Rawi, M., Alfonseca, M., and Ortega, A., Network intrusion detection using genetic algorithm to find best DNA signature, WSEAS Trans. Syst., 2008, vol. 7, no. 7, pp. 589–599.
23.
Zurück zum Zitat Kang, H., Ahn, D., et al., IoT network intrusion dataset, IEEE Dataport, 2019. https://ieee-dataport.org/open-access/iot-network-intrusion-dataset. Accessed April 25, 2020. Kang, H., Ahn, D., et al., IoT network intrusion dataset, IEEE Dataport, 2019. https://​ieee-dataport.​org/​open-access/​iot-network-intrusion-dataset.​ Accessed April 25, 2020.
24.
Zurück zum Zitat Parmisano, A., Garcia, S., and Erquiaga, M., Stratosphere Laboratory. A labeled dataset with malicious and benign IoT network traffic. https://www.stratosphereips.org/datasets-iot23. Accessed April 25, 2020. Parmisano, A., Garcia, S., and Erquiaga, M., Stratosphere Laboratory. A labeled dataset with malicious and benign IoT network traffic. https://​www.​stratosphereips.​org/​datasets-iot23.​ Accessed April 25, 2020.
25.
Zurück zum Zitat Koroniotis, N., Moustafa, N., Sitnikova, E., and Turnbull, B., Towards the Development of Realistic Botnet Dataset in the Internet of Things for Network Forensic Analytics: Bot-IoT Dataset, 2018. https://arxiv.org/ abs/1811.00701. Koroniotis, N., Moustafa, N., Sitnikova, E., and Turnbull, B., Towards the Development of Realistic Botnet Dataset in the Internet of Things for Network Forensic Analytics: Bot-IoT Dataset, 2018. https://​arxiv.​org/​ abs/1811.00701.
Metadaten
Titel
Development of the Intrusion Detection System for the Internet of Things Based on a Sequence Alignment Algorithm
verfasst von
M. O. Kalinin
V. M. Krundyshev
B. G. Sinyapkin
Publikationsdatum
01.12.2020
Verlag
Pleiades Publishing
Erschienen in
Automatic Control and Computer Sciences / Ausgabe 8/2020
Print ISSN: 0146-4116
Elektronische ISSN: 1558-108X
DOI
https://doi.org/10.3103/S0146411620080155

Weitere Artikel der Ausgabe 8/2020

Automatic Control and Computer Sciences 8/2020 Zur Ausgabe