Abstract
Data Security is a protective privacy measure which is used for prevention of unauthorized access in various domains such as computers network, web application, database etc. Bio-inspired computing is a branch of Machine learning deals with the biological properties of living organism. These techniques are used in the fields of data security and feature extraction in combination with different IoT architecture for secure data transition and maintenance. This chapter discusses some of the biologically inspired computational algorithms like Ant Colony Optimization, Artificial Bee Colony, Genetic bee colony, and Firefly which are used in solving real world problem in the field of IoT for providing data security. Finally the chapter concludes by stating the effectiveness of these algorithms on the bases of the case studies and thus provides a wider perspective towards Bio-inspired techniques and their use for data security in IoT.
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References
Alam, B., Doja, M. N., Alam, M., & Malhotra, S. (2013, September). Security issues analysis for cloud computing. International Journal of Computer Science and Information Security, 11(9), 117–125.
Alshamlan, H. M., Badr, G. H., & Alohali, Y. A. (2015). Genetic Bee Colony (GBC) algorithm: A new gene selection method for microarray cancer classification. Computational Biology and Chemistry, 56, 49–60.
Banati, H., & Bajaj, M. (2013). Performance analysis of firefly algorithm for data clustering. International Journal of Swarm Intelligence, 1(1), 19–35.
Banu, R., Ahammed, G. A., & Fathima, N. (2016). A review on biologically inspired approaches to security for Internet of Things (IoT). In 2016 International Conference on Electrical, Electronics, and Optimization Techniques (ICEEOT). IEEE.
Beckers, R., Deneubourg, J. L., & Goss, S. (1992). Trails and U-turns in the selection of the shortest path by the ant Lasius niger. Journal of Theoretical Biology, 159, 397–415.
Bello, O., & Zeadally, S. (2014). Intelligent device-to-device communication in the Internet of Things. IEEE Systems Journal, 10(3), 1172–1182.
Birattari, M., Pellegrini, P., & Dorigo, M. (2007). On the invariance of ant colony optimization. IEEE Transactions on Evolutionary Computation, 11(6), 732–742. https://doi.org/10.1109/TEVC.2007.892762.
Bolondi, M., & Bondanza, M. (1993). Parallelizzazione di un algoritmo per la risoluzione del problema del commesso viaggiatore. Masters thesis, Politecnico di Milano, Italy. Croes, G.A.
Butler, K., Kuligowski, E., Furman, S., & Peacock, R. (2017). Perspectives of occupants with mobility impairments on evacuation methods for use during fire emergencies. Fire Safety Journal, 91, 955–963.
Dorigo, M., & Birattari, M. (2011). Ant colony optimization. In C. Sammut & G. I. Webb (Eds.), Encyclopedia of machine learning. Boston: Springer.
Dorigo, M., & Di Caro, G. (1999). Ant colony optimization: A new meta-heuristic. IEEE. 2. 1477 Vol. 2. https://doi.org/10.1109/CEC.1999.782657.
Liu, X. (2017). Routing protocols based on ant colony optimization in wireless sensor networks: A survey. IEEE Access, 5, 26303–26317.
Najjar-Ghabel, S., Yousefi, S., & Farzinvash, L. (2018). Reliable data gathering in the Internet of Things using artificial bee colony. Turkish Journal of Electrical Engineering and Computer Sciences, 26(4), 1710–1723.
Okdem, S., Karaboga, D., & Ozturk, C. (2011, June). An application of wireless sensor network routing based on artificial bee colony algorithm. In 2011 IEEE Congress of Evolutionary Computation (CEC) (pp. 326–330). IEEE.
Olander, J., Ronchi, E., Lovreglio, R., & Nilsson, D. (2017). Dissuasive exit signage for building fire evacuation. Applied Ergonomics, 59, 84–93.
Perumal, T., Sulaiman, M. N., Datta, S. K., & Leong, C. Y. (2016). Rule-based conflict resolution framework for internet of things device management in smart home environment. In IEEE global conference on consumer electronics.
Sari, I. R. F. (2017, October). Bioinspired algorithms for Internet of Things network. In 2017 4th International Conference on Information Technology, Computer, and Electrical Engineering (ICITACEE). IEEE.
Suwetha, K., Vinothini, K., & Shankar, R. (2017). High performance in security system for image in IoT. International Journal of Engineering Development and Research, 501–507.
Xu, L. D., He, W., & Li, S. (2014). Internet of Things in industries: A survey. IEEE Transactions on Industrial Informatics.
Yang, X. S. (2008). Nature-inspired metaheuristic algorithms. Beckington: Luniver Press.
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Mani Sekhar, S.R., Siddesh, G.M., Tiwari, A., Anand, A. (2020). Bioinspired Techniques for Data Security in IoT. In: Alam, M., Shakil, K., Khan, S. (eds) Internet of Things (IoT). Springer, Cham. https://doi.org/10.1007/978-3-030-37468-6_9
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DOI: https://doi.org/10.1007/978-3-030-37468-6_9
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