Abstract
Functional integrity and sustainability of cyber systems, coherency, and connectivity of the flexible infrastructures based on the Internet of Things (IoT) concept arise in the cyber security as a stability manifestation. There is a set of polymorphic attacks that have mutations (local differences and time gaps) in the sequences of the operational acts forming an IoT-specific class of the intrusions such as a forced power consumption and a forced topology change. Our research proposes the nature-inspired technologies, namely a genetic sequence alignment computing and a sequence similarity calculation, instead of massive equity checking of multiple packets and signatures traditionally used for the system protection. The sequence alignment computing is used by nature to compare and copy the DNA and nucleotide chains keeping the stability of the bioinformatic structures, which are rather similar to sequences of the security events. The methods based on the Needleman-Wunsch, Needleman-Wunsch with the position weight matrix (PWM), Smith-Waterman, and Mauve techniques have been implemented and experimentally studied with a BoT-IoT dataset on the IoT Raspberry Pi4 platform. The experiments have shown the best effectiveness obtained with the Needleman-Wunsch algorithm with the PWM: accuracy—0.98%, precision—0.99%, and recall—0.98% achieved at detection of the polymorphic intrusions.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
E. Aleksandrova, Methods of group authentication for low-resource vehicle and flying self-organizing networks. Automat. Control Comput. Sci. 51(8), 947–958 (2017)
S. Benaicha, L. Saoudi, S. Guermeche, O. Lounis, Intrusion Detection System Using Genetic Algorithm. in 2014 Science and Information Conference (London, 2014), pp. 564–568
H. Carrillo, D. Lipman, The multiple sequence alignment problem in biology. SIAM J. Appl. Mathe. 48(5), 1073–1082 (1988)
X. Chen et al., Learning position weight matrices from sequence and expression data. Comput. Syst. Bioinfo. 6, 249–260 (2007)
S. Coull, J. Branch, B. Szymański, E. Breimer, Intrusion Detection: A Bioinformatics Approach. in 19th Annual Computer Security Applications Conference (Las Vegas, NV, USA, 2003), pp. 24–33
A. Dakhnovich, D. Moskvin, D. Zeghzda, An approach for providing industrial control system sustainability in the age of digital transformation. IOP Conf. Series Mater. Sci. Eng. 497, 1–10 (2019)
A. Darling et al., Mauve: multiple alignment of conserved genomic sequence with rearrangements. Genome Res. 14, 1394–1403 (2004)
P. Fogla, M. Sharif, R. Perdisci, O. Kolesnikov, W. Lee, Polymorphic Blending Attacks. in USENIX (2006), pp. 241–256.
M. Hoque, M. Mukit, M. Bikas, An implementation of intrusion detection system using genetic algorithm. Int. J. Netw. Sec. Appl. 4(2), 109–120 (2012)
Kaspersky Lab Report, IoT under fire: Kaspersky detects more than 100 million attacks on smart devices in H1 2019 (2019) URL: https://www.kaspersky.com/about/press-releases/2019_iot-under-fire-kaspersky-detects-more-than-100-million-attacks-on-smart-devices-in-h1-2019
Kaspersky Lab, DDoS attacks in Q1 2020. URL: https://securelist.ru/ddos-attacks-in-q1-2020/95949/
N. Koroniotis, N. Moustafa, E. Sitnikova, B. Turnbull, 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
D. Lavrova, M. Poltavtseva, A. Shtyrkina, Security Analysis of Cyber-Physical Systems Network Infrastructure. in 2018 IEEE Industrial Cyber-Physical Systems (ICPS) (St. Petersburg, 2008), pp. 818–823.
E. Malyshev, D. Moskvin, D. Zegzhda, Application of an artificial neural network for detection of attacks in VANETs. Automat. Control Comput. Sci. 53(8), 889–894 (2019)
M. Mellal, A. Salhi, Parallel-Series System Optimization by Weighting Sum Methods and Nature-Inspired Computing, in Applied Nature-Inspired Computing: Algorithms and Case Studies. ed. by N. Dey, A. Ashour, S. Bhattacharyya (Springer Tracts in Nature-Inspired Computing, Springer, Singapore, 2020), pp. 231–251
S. Needleman, C. Wunsch, A general method applicable to the search for similarities in the amino acid sequence of two proteins. J. Mol. Biol. 48(3), 443–453 (1970)
E. Pavlenko, D. Zegzhda, M. Poltavtseva, Ensuring the Sustainability of Cyberphysical Systems Based on Dynamic Reconfiguration. in 2019 IEEE International Conference on Industrial Cyber Physical Systems (ICPS) (Taipei, Taiwan, 2019), pp. 785–789
A. Procopiou, N. Komninos, Bio/Nature-inspired Algorithms in A.I. for Malicious Activity Detection. in Nature-Inspired Cyber Security and Resiliency: Fundamentals, techniques and applications, ed. by E.-S. El-Alfy, M. Elroweissy, E.W. Fulp, W. Mazurczyk (2019)
A. Shenfield, D. Day, A. Ayesh, Intelligent intrusion detection systems using artificial neural networks. ICT Express 4(2), 95–99 (2018)
J. Sangeetha et al., Analyzing and Predicting the QoS of Traffic in WiMAX Network Using Gene Expression Programming. in Advancements in Applied Metaheuristic Computing, ed. by N. Dey (IGI Global, 2018), pp. 34–70
A. Saxena, R. Kumar, Chaotic Variants of Grasshopper Optimization Algorithm and Their Application to Protein Structure Prediction, in Applied Nature-Inspired Computing: Algorithms and Case Studies. ed. by N. Dey, A. Ashour, S. Bhattacharyya (Springer Tracts in Nature-Inspired Computing, Springer, Singapore, 2020), pp. 151–175
T. Smith et al., Identification of common molecular subsequences. J. Mol. Biol. 147(1), 195–197 (1981)
Z. Trabelsi, R. Hamdy, Implementation of a Dna-Based Anomaly Identification System Utilizing Associative String Processor (ASP). in ACS/IEEE International Conference on Computer Systems and Applications—AICCSA (Hammamet, 2010) pp. 1–8
L. Volkert, D. Stoffer, A Comparison Of Sequence Alignment Algorithms for Measuring Secondary Structure Similarity. in 2004 Symposium on Computational Intelligence in Bioinformatics and Computational Biology (La Jolla, CA, USA, 2004), pp. 182–189
Y. Wang, H. Wu, Y. Cai, A benchmark study of sequence alignment methods for protein clustering. BMC Bioinfo. 19, 529 (2018)
D. Zegzhda, D. Lavrova, E. Pavlenko, Management of a dynamic infrastructure of complex systems under conditions of directed cyber attacks. J. Comput. Syst. Sci. Int. 59(3), 358–370 (2020)
P. Zegzhda, M. Poltavtseva, A. Pechenkin et al., A use case analysis of heterogeneous semistructured objects in information security problems. Autom. Control Comput. Sci. 52(8), 918–930 (2018)
Acknowledgments
The research is financially supported by LG Electronics Inc.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this chapter
Cite this chapter
Cho, H., Lim, S., Kalinin, M., Krundyshev, V., Belenko, V., Chernenko, V. (2021). Genetic Sequence Alignment Computing for Ensuring Cyber Security of the IoT Systems. In: Joshi, A., Nagar, A.K., Marín-Raventós, G. (eds) Sustainable Intelligent Systems. Advances in Sustainability Science and Technology. Springer, Singapore. https://doi.org/10.1007/978-981-33-4901-8_14
Download citation
DOI: https://doi.org/10.1007/978-981-33-4901-8_14
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-33-4900-1
Online ISBN: 978-981-33-4901-8
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)