Nature is the best creation of the earth which we see in our daily life. Nature gives us problem solving methods with how to accept the environmental changes. Researcher also look for the solving their problem inspired with nature. These methods help to solve the problem in artificial atmosphere. Current cyber security system inherent limitation such as absence of self awareness and self correcting methods, disability to diagnose configuration fault and conflict resolution due to multiparty management of security infrastructure. Nature inspired biological system has build-in attractive quality to adapt varying environment conditions, inherent flexibility to failures and damages, The system employs a hybrid approach that combines nature-inspired optimization methods with simulation modeling to construct and evaluate candidate architectures, and adapt to changing threat levels. This research paper addresses an important gap in the area of cyber security to generate optimal/near-optimal security decisions in real time which has not been explored for improving cyber security.
Cite this article:
Poonam Yadav. A taxonomy review of Nature Inspired Algorithms on cyber security for communication and networking. Int. J. Tech. 2020; 10(1):47-52. doi: 10.5958/2231-3915.2020.00009.7
Poonam Yadav. A taxonomy review of Nature Inspired Algorithms on cyber security for communication and networking. Int. J. Tech. 2020; 10(1):47-52. doi: 10.5958/2231-3915.2020.00009.7 Available on: https://ijtonline.com/AbstractView.aspx?PID=2020-10-1-9
1. Neal Wagner, Cem ¸ S. ¸ Sahin, Jaime Pena, and William W. Streilein, "A Nature-inspired Decision System for Secure Cyber Network Architecture", MIT Lincoln Laboratory Lexington, MA, USA.
2. Fielder, “Decision support approaches for cyber security investment,” Decision Support Systems, vol. 86, pp. 13–23, 2016.
3. N. Wagner et al., “Towards automated cyber decision support: A case study on network segmentation for security,” in IEEE Symposium on Computational Intelligence for Cyber Security, December 2016.
4. Muhammad Mostafa Amir Faisal, Muhammad Ariful Islam Chowdhury, ”Bio Inspired Cyber Security Architecture for Smart Grid”, 2016.
5. Shikha Mehta Parul Agarwal, "Nature-Inspired Algorithms: State-of-Art, Problem and Prospects," International Journal of Computer Applications, vol. 100, no. 14, August 2014.
6. D. Dasgupta, (2006) “Computational Intelligence in Cyber Security”, IEEE International Conference on Computational Intelligence for Homeland Security and Personal Safety (CIHSPS 2006), pp. 2–3.
7. S. Poonia, A. Bhardwaj, G. S. Dangayach, (2011) “Cyber Crime: Practices and Policies for Its Prevention”, The First International Conference on Interdisciplinary Research and Development, Special No. of the International Journal of the Computer, the Internet and Management, Vol. 19, No.SP1.
8. Back, T. 1996: Evolutionary algorithms in theory and practice. Oxford University Press.
9. J.H. Holland, Genetic algorithms and the optimal allocation of trials, SIAM J. Comput. 2 (2) (1973) 88–105
10. Koza, John R. 1992. Genetic Programming: On the Programming of Computers by Means of Natural Selection. Cambridge, MA: The MIT Press.
11. Beyer, H.G. and Schwefel, H.P. 2002: Evolution strategies. Natural Computing 1,3–52.
12. Mukesh Gupta and Akansha Jain S. N. Tazi, "A Survey on application of nature inspired algorithms," International Journal of Computer Science Engineering and Information Technology Research (IJCSEITR), vol. 4, no. 4, pp. 33-40, August 2014.
13. Bonabeau, E., Dorigo, M. and Theraulaz, G.1999:Swarm intelligence. Oxford University Press
14. Kennedy, J.; Eberhart, R. (1995). "Particle Swarm Optimization". Proceedings of IEEE International Conference on Neural Networks. IV. pp. 1942– 1948.
15. Dorigo, M., Maniezzo, V., and Colorni, A. (1996). Ant System: Optimization by a colony of cooperating agents. IEEE Transactions on Systems, Man, and Cybernetics – Part B, 26, 29–41.
16. D. Karaboga, B. Basturk, A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm, Journal of Global Optimization 39 (2007) 459–471
17. X. Li, Z. Shao, J. Qian,Anoptimizing method base on autonomous animates: fish- swarm algorithm, Systems Engineering Theory and Practice 22 (2002) 32–38.
18. Shah_Hosseini, H. Shahid Beheshti Univ., Tehran Problem solving by intelligent water drops IEEE. Congress on Evolutionary Computation, 2007. CEC 2007.
19. Binitha S, S Siva Sathya, "A Survey of Bio inspired Optimization Algorithms", International Journal of Soft Computing and Engineering (IJSCE) ISSN: 2231-2307, Volume-2, Issue-2, May 2012.
20. Jianjun Ni, LiuyingWu, Xinnan Fan and Simon X. Yang, "Review Article Bioinspired Intelligent Algorithm and Its Applications for Mobile Robot Control: A Survey" Hindawi Publishing Corporation Computational Intelligence and Neuroscience Volume 2016, Article ID 3810903, 16 pages
21. Selma Dilek, Hüseyin Çakır and Mustafa Aydın, "Applications of Artificial Intelligence Techniques to Combating Cyber Crimes: A Review", International Journal of Artificial Intelligence and Applications (IJAIA), Vol. 6, No. 1, January 2015.
22. N. Wattanapongsakorn, S. Srakaew, E. Wonghirunsombat, C. Sribavonmongkol, T. Junhom, P. Jongsubsook, C. Charnsripinyo, (2012) “A Practical Network-based Intrusion Detection and Prevention System”, IEEE 11th International Conference on Trust, Security and Privacy in Computing and Communications, pp. 209 – 214.
23. A. S. A. Aziz, M. A. Salama, A. Hassanien, S. E. Hanafi, (2012) “Artificial Immune System Inspired Intrusion Detection System Using Genetic Algorithm”, Informatica, Vol. 36, pp. 347 358.
24. F. Barani, (2014) "A hybrid approach for dynamic intrusion detection in ad hoc networks using genetic algorithm and artificial immune system," Iranian Conference on Intelligent Systems (ICIS), pp.1 6.
25. P. K. Harmer, P. D. Williams, G. H. Gunsch, G. B. Lamont, (2002) “An Artificial Immune System Architecture for Computer Security Applications”, IEEE transactions on evolutionary computation, Vol. 6, No. 3, pp. 252¬ 280.