Internet of Things (IoT) Based Bio-inspired Artificial Intelligent Technique to Combat Cybercrimes: A Review

  • M. Balasaraswathi
  • V. Sivasankaran
  • N. Akshaya
  • Radika Baskar
  • E. Suganya
Part of the EAI/Springer Innovations in Communication and Computing book series (EAISICC)


Nature has made a huge impact on human’s diurnal lives in finding solutions for complex problems. Majority of the people are receiving benefits by the advanced technologies. Without the aid of computers, it is hard to imitate the problem solving techniques being implemented by nature. Bio-mimicry is one such study which enables the researchers in solving human problems by emulating and mimicking nature. This emerging technology fetches its sources from bio-inspired engineering at various levels. BIC, short for bio-inspired computing or biologically inspired computation, found to be a broad term, which comprises different fields like computer science, mathematics and biology. Mix and match of various biological and computational processes is the reliable means for researchers in this regard and this leads to the convergence of two fields: computer science and biology. In recent past, the bio-inspired optimization algorithms are highly convincing to bring out best expositions for intricate snags by joining hands with machine learning. Along with BIC, swarm intelligence (SI) is also equally gaining importance. Bio-inspiring algorithms like ant colony algorithm, bat algorithm, cuckoo search, fire-fly algorithms, and particle swarm optimization possess extensive applications in almost all fields of science and engineering. Swarm intelligence is the collective behavior of decentralized, self-organized systems, though they are natural or artificial and it is based on meta-heuristic approach which generally pulls out an approximate optimal solution independent of the problems in less time. Nowadays, the information technology advancements induce felons to commit copious cybercrimes using cyberspace. More sophisticated cyber-defense systems are to be devised essentially, since (1) cyber-infrastructures are extremely open and prone to infringements and other intimidations and (2) human intervention and physical devices are not adequate to monitor and fortify the cyber-infrastructures. The ability to detect a wide variety of threats and adaptability to make quick real-time decisions, flexibility and robustness are the major expected qualities for these systems. Abundant BIC approaches incorporated with artificial intelligence (AI) using IOT plays a vital role in cybercrime detection and prevention. Let us have a cattle graze of few techniques of AI associated with bio-inspired computing and its futuristic scope over human community to combat cybercrimes and this review would pave the alleyway for forthcoming studies to cherry-pick algorithms based on fitment.


Cyber-security Internet of Things Artificial intelligence Bio-inspired techniques 


  1. 1.
    Dyson, F. (2007). Our biotech future. The New York Review of Books.Google Scholar
  2. 2.
    Haupt, R. L., & Haupt, S. E. (2004). Particle genetic algorithms (2nd ed.). New York. ISBN: 0-471-45565-2: Wiley.zbMATHGoogle Scholar
  3. 3.
    Gandomi, A. H., & Alavi, A. H. (2012). Krill Herd: A new bio-inspired optimization algorithm. Journal of Communications in Nonlinear Science and Numerical Simulation, 17(12), 4831–4845.MathSciNetCrossRefGoogle Scholar
  4. 4.
    Fister, I., Fister, I., Jr., Brest, J., & Žumer, V. (2012). Memetic artificial bee colony algorithm for large-scale global optimization. IEEE Congress on Evolutionary Computation, 1–8.Google Scholar
  5. 5.
    Yang, X. S. (2009). Firefly algorithms for multimode optimization. Journal of Stochastic Algorithms: Foundations and Applications, Springer, 169–178.Google Scholar
  6. 6.
    Binitha, S., & Siva Sathya, S. (2012, May). A survey of bio-inspired optimization algorithms. International Journal of Soft Computing and Engineering, 2(2), 137–151. ISSN: 2231-2307.Google Scholar
  7. 7.
    Mahale, R. A., & Chavan, S. D. (2012, December). A survey: Evolutionary and swarm based bio-inspired optimization algorithm. International Journal of Scientific and Research Publications, 2(12), 1–7.Google Scholar
  8. 8.
    Gandomi, A. H., & Alayi, A. H. (2012). Krill herd: A new bio-inspired optimization algorithm. Communications in Nonlinear Sciences and Numerical Simulation, 17(12), 4831–4845.MathSciNetCrossRefGoogle Scholar
  9. 9.
    Goldberg, D. E. (1989). Genetic algorithms in search, optimization, and machine learning. Reading: Addison-Wesley.zbMATHGoogle Scholar
  10. 10.
    Mishra, A., & Shukla, A. (2016). A new insight into the schema survival for genetic algorithms consisting of chromosomes having bits with different vulnerability for mutation. International Journal of Control Theory and Applications, 9(42), 39–44. ISSN: 0974-5572.Google Scholar
  11. 11.
    Kar, A. K. (2016). Bio inspired computing—A review of algorithms and scope of applications. Journal of Expert Systems with Applications, 59, 20–32.CrossRefGoogle Scholar
  12. 12.
    Fister, I., Jr., et al. (2013, July). A brief review of nature-inspired algorithms for optimization. Article in Elektrotehniski Vestnik/Electrotechnical Review, 80(3), 1–7.Google Scholar
  13. 13.
    Dorigo, M. (1992). Optimization, learning and natural algorithms. Ph.D. Thesis, Politecnico di Milano, Italy.Google Scholar
  14. 14.
    Yang, X.-S. (2010). Nature-inspired metaheuristic algorithms (2nd ed.). Bristol: Luniver Press.Google Scholar
  15. 15.
    Dervis, K., et al. (2007). A powerful and efficient algorithm for numerical function optimization: Artificial bee colony (ABC) algorithm. Journal of Global Optimization, 39(3), 459–471.MathSciNetCrossRefGoogle Scholar
  16. 16.
    Passino, K. M. (2002). Biomimicry of bacterial foraging for distributed optimization and control. IEEE Control Systems, 22(3), 52–67.MathSciNetCrossRefGoogle Scholar
  17. 17.
    Chen, T.-C., et al. (2007). A novel optimization approach: Bacterial-GA foraging. In Second International Conference Innovative Computing, Information and Control—ICICIC’07, IEEE (pp. 391–391).Google Scholar
  18. 18.
    Yang, X. S. (2010). A new metaheuristic bat-inspired algorithm. In Nature Inspired Cooperative Strategies for Optimization (NICSO 2010) (pp. 65–74).Google Scholar
  19. 19.
    Teodorović, D., & Dell’Orco, M. (2005). Bee colony optimization—a cooperative learning approach to complex transportation problems. In Advanced OR and AI Methods in Transportation: Proceedings of 16th Mini-EURO Conference and 10th Meeting of EWGT (13–16 September 2005) (pp. 51–60). Poznan: Publishing House of the Polish Operational and System Research.Google Scholar
  20. 20.
    Lucic, P., & Teodorovic, D. (2001). Bee system: Modeling combinatorial optimization transportation engineering problems by swarm intelligence. In Preprints of the TRISTAN IV-Triennial Symposium on Transportation Analysis (pp. 441–445).Google Scholar
  21. 21.
    Wedde, H. F., et al. (2004). Beehive: An efficient fault-tolerant routing algorithm inspired by honey bee behavior. In Lecture Notes in Computer Science, Book Series Vol. 3172 LNCS (pp. 83–94).Google Scholar
  22. 22.
    Tang, R., et al. (2012). Wolf search algorithm with ephemeral memory. In 7th International Conference on Digital Information Management (ICDIM) (pp. 165–172).Google Scholar
  23. 23.
    Pham, D. T., et al. (2006). The bees algorithm—A novel tool for complex optimization problems. In Proceedings of the 2nd Virtual International Conference on Intelligent Production Machines and Systems (IPROMS 2006) (pp. 454–459).Google Scholar
  24. 24.
    Drias, H., Sadeg, S., & Yahi, S. (2005). Cooperative bees swarm for solving the maximum weighted satisfiability problem. Journal of Computational Intelligence and Bio inspired Systems, Springer, LNCS, 3512, 318–325.Google Scholar
  25. 25.
    Padró, F. P. C. (2009). Bumblebees: A multi-agent combinatorial optimization algorithm inspired by social insect behavior. GEC-2009 (pp. 811–814).Google Scholar
  26. 26.
    Chu, S. A., et al. (2006). Cat swarm optimization. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 4099 LNAI (pp. 854–858).Google Scholar
  27. 27.
    Iordache, S. (2010). Consultant-guided search: A new metaheuristic for combinatorial optimization problems. In Proceedings of the 12th Annual Conference on Genetic and Evolutionary Computation (pp. 225–232).Google Scholar
  28. 28.
    Yang, X.-S., & Deb, S. (2009). Cuckoo search via Lévy flights. In World Congress on Nature & Biologically Inspired Computing, NaBIC 2009, IEEE (pp. 210–214).Google Scholar
  29. 29.
    Yang, X.-S., & Deb, S. (2010). Eagle strategy using Lévy walk and firefly algorithms for stochastic optimization. In Nature Inspired Cooperative Strategies for Optimization (NICSO2010) (pp. 101–111). Berlin: Springer.Google Scholar
  30. 30.
    Chu, Y., et al. (2008). A fast bacterial swarming algorithm for high-dimensional function optimization. In IEEE World Congress on Computational Intelligence (pp. 3135–3140).Google Scholar
  31. 31.
    Yang, X.-S. (2010). Firefly algorithm, stochastic test functions and design optimization. International Journal of Bio-Inspired Computation, 2(2), 78–84.CrossRefGoogle Scholar
  32. 32.
    Li, X. L., et al. (2002). Optimizing method based on autonomous animate: Fish-swarm algorithm. Xitong Gongcheng Lilun yu Shijian/System Engineering Theory and Practice, 22(11), 32.Google Scholar
  33. 33.
    Su, S., et al. (2007). Good lattice swarm algorithm for constrained engineering design optimization. In International Conference on Wireless Communications, Networking and Mobile Computing, IEEE (pp. 6421–6424).Google Scholar
  34. 34.
    Krishnanand, K. N., & Ghose, D. (2009). Glowworm swarm optimization: A new method for optimizing multi-modal functions. International Journal of Computational Intelligence Studies, 1(1), 93–119.CrossRefGoogle Scholar
  35. 35.
    Chen, H., et al. (2010). Hierarchical swarm model: A new approach to optimization. Discrete Dynamics in Nature and Society.Google Scholar
  36. 36.
    Mucherino, A., & Seref, O. (2007). Monkey search: A novel metaheuristic search for global optimization. Data Mining, Systems Analysis and Optimization in Biomedicine, 953, 162–173.CrossRefGoogle Scholar
  37. 37.
    Kennedy, J., & Eberhart, R. (1995). Particle swarm optimization. IEEE International Conference on Neural Networks, 4, 1942–1948.CrossRefGoogle Scholar
  38. 38.
    Yang, X.-S., et al. (2006). Application of virtual ant algorithms in the optimization of CFRP shear strengthened pre-cracked structures. In Computational Science–ICCS (pp. 834–837). Springer.Google Scholar
  39. 39.
    Yang, X. S. (2005). Engineering optimizations via nature-inspired virtual bee algorithms (Vol. 3562, pp. 317–323).Google Scholar
  40. 40.
    Ting, T. O., et al. (2012). Weightless swarm algorithm (WSA) for dynamic optimization problems. In Network and Parallel Computing (pp. 508–515). Springer.Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • M. Balasaraswathi
    • 1
  • V. Sivasankaran
    • 2
  • N. Akshaya
    • 3
  • Radika Baskar
    • 4
  • E. Suganya
    • 5
  1. 1.ECE, Saveetha School of Engineering, SIMATSChennaiIndia
  2. 2.ECE, Sreenivasa Institute of Technology and Management StudiesChittoorIndia
  3. 3.Accenture, Gateway Office ParksChennaiIndia
  4. 4.ECE, Saveetha School of Engineering, SIMATSChennaiIndia
  5. 5.School of CSE, VIT Bhopal UniversityBhopalIndia

Personalised recommendations