From the observations made on biology and nature, it can be seen that biological living creatures are very efficient in the functions of recognizing and eliminating danger around them. Their ability to adapt within the surrounding environment and self-healing capability are remarkable. Computers’ defensive systems have been approached by various biological inspired techniques. However, very few surveys examined the current cyber security status - in which most of them are focused on one or two methods (i.e. Artificial Immune System - AIS and/or Genetic Algorithms approaches).

Abraham, A., Grosan, C. and Martin-vide, C. (2007). Evolutionary design of intrusion detection programs, International Journal of Network Security 4(3), 328339.

Amza, C., Leordeanu, C. and Cristea, V. (2011). Hybrid network intrusion detection, in Intelligent Computer Communication and Processing (ICCP), 2011 IEEE International Conference, pp. 503–510.

Booz, Allen, H. (2011). Asserting global leadership in the cyber domain. http://www.boozallen.com/media/file/cyber-vision-2020.pdf.pdf

Catania, C. A. and Garino, C. G. (2012). Automatic network intrusion detection: Current techniques and open issues, Computers and Electrical Engineering 38(5),1062-1072

Chaure, R. and Shandilya, S. K. (2010). Firewall anamolies detection and removal techniques a survey, International Journal on Emerging Technologies 1(1), 71–74.

Cisco (2015a). Cisco advanced malware protection for networks data sheet. http://www.cisco.com/c/en/us/products/collateral/security/amp-appliances/datasheet-c78-733182.pdf

Cisco (2015b). Cisco firepower 8000 firewall data sheet. http://www.cisco.com/c/en/us/products/collateral/security/firepower-8000-series- appliances/datasheet-c78-732955.pdf

Crosbie, M. and Spafford, G. (1995). Applying genetic programming to intrusion detection, in Working Notes for the AAAI Symposium on Genetic Programming, MIT, Cambridge, MA, USA: AAAI, pp. 1–8.

Dhak, B. S. and Lade, S. (2012). An evolutionary approach to intrusion detection system using genetic algorithm, International Journal of Emerging Technology and Advanced Engineering 2(12), 632–637.

Fries, T. (2010). Evolutionary optimization of a fuzzy rule-based network intrusion detection sys- tem, in Fuzzy Information Processing Society (NAFIPS), 2010 Annual Meeting of the North American, pp. 1–6.

Garca-Teodoro, P., Daz-Verdejo, J., Maci-Fernndez, G. and Vzquez, E. (2009). Anomaly-based net- work intrusion detection techniques, systems and challenges, Computers and Security 28(12), 18– 28. http://www.sciencedirect.com/science/Article/pii/S0167404808000692

Gomez, J. and Dasgupta, D. (2002). Evolving fuzzy classifiers for intrusion detection, in Workshop on Information Assurance.

Gong, R. H., Zulkernine, M. and Abolmaesumi, P. (2005). A software implementation of a genetic algorithm based approach to network intrusion detection, in Sixth International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing, pp. 7695–2294.

Gouda, M. G. and Liu, A. X. (2007). Structured firewall design, Computer Networks 51(4), 1106–1120.

Haag, C., Lamont, G., Williams, P. and Peterson, G. (2007). An artificial immune system-inspired multiobjective evolutionary algorithm with application to the detection of distributed computer network intrusions, in L. de Castro, F. Von Zuben and H. Knidel, eds, Artificial Immune Systems, Vol. 4628 of Lecture Notes in Computer Science, Springer Berlin Heidelberg, pp. 420–435.

Hassan, M., Vidalis, S. and Mylonas, A. (2015). Cell based intrusion prevention system, in 14th European Conference on Information Warfare and Security ECCWS 2015, UK.

Helmer, G., Wong, J. S. K., Honavar, V. G. and Miller, L. (2002). Automated discovery of concise predictive rules for intrusion detection, J. Syst. Softw. 60(3), 165–175. http://dx.doi.org/10.1016/S0164-1212(01)00088-7

Ioannidis, S., Keromytis, A. D., Bellovin, S. M. and Smith, J. M. (2000). Implementing a distributed firewall, in Proceedings of the 7th ACM Conference on Computer and Communications Security ACM, 190–199. http://doi.acm.org/10.1145/352600.353052

Kenneth, K., Frank, M., Kelly, R. J. R. and Byrd, T. A. (2003). Defense mechanisms of biological cells: A framework for network security thinking. http://aisel.aisnet.org/cais/vol12/iss1/47

Kim, J., Bentley, P. J., Aickelin, U., Greensmith, J., Tedesco, G. and Twycross, J. (2007). Immune system approaches to intrusion detection - a review, Natural Computing 6(4), 413–466.

LaRoche, P., Zincir-Heywood, N. and Heywood, M. I. (2009). Evolving tcp/ip packets: a case study of port scans, in Proceedings of the Second IEEE international conference on Computational intelligence for security and defense applications, IEEE Press, pp. 281–288. http://dl.acm.org/citation.cfm?id=1736481.1736520

Lee, W., Stolfo, S. J. and Mok, K. W. (2000). Adaptive intrusion detection: A data mining approach, Artificial Intelligence Review 14(6), 533–567.

Li, W. (2004). Using genetic algorithm for network intrusion detection, ECSG. http://www.security.cse.msstate.edu/docs/Publications/wli/DOECSG2004.pdf

Ofria, C., Adami, C. and Collier, T. C. (2002). Design of evolvable computer languages, Evolutionary Computation IEEE Transactions on 6(4), 420–424.

Ojugo, A., Eboka, A., Okonta, O., (Mrs), R. Y. and Aghware, F. (2012). Genetic algorithm rule-based intrusion detection system (gaids), Journal of Emerging Trends in Computing and Information Sciences 3(8), 1118–1194.

Panda, M., Abraham, A. and Patra, M. R. (2012). A hybrid intelligent approach for network intrusion detection, Procedia Engineering 30(0), 1–9.

Powers, S. T. and He, J. (2008). A hybrid artificial immune system and self organising map for network intrusion detection, Information Sciences 178(15), 3024 – 3042.

Roubos, J. A., Setnes, M. and Abonyi, J. (2003), Learning fuzzy classification rules from labeled data, Information Sciences 150(12), 77–93. Recent Advances in Soft Computing. http://www.sciencedirect.com/science/article/pii/S0020025502003699

Rowland, C. (2002). Intrusion detection system. US Patent 6,405,318. https://www.google.com/patents/US6405318

Su, M.-Y., Yu, G.-J. and Lin, C.-Y. (2009). A real-time network intrusion detection system for large-scale attacks based on an incremental mining approach, Computers and Security 28(5), 301–309. http://www.sciencedirect.com/science/article/pii/S016740480800134X.

Sujatha, K. P., Priya, S. C. and A., K. (2012). Network intrusion detection system using genetic network programming with support vector machine, in Proceedings of the International Conference on Advances in Computing, Communications and Informatics, ICACCI ’12, ACM, New York, NY, USA, pp. 645–649. http://doi.acm.org/10.1145/2345396.2345501

Tettamanzi, A. and Tomassini, M. (1998). Soft Computing, Integrating Evolutionary, Neural, and Fuzzy Systems, 1st edn, Springer.

Tsai, C.-F., Hsu, Y.-F., Lin, C.-Y. and Lin, W.-Y. (2009). Intrusion detection by machine learning: A review, Expert Systems with Applications 36(10), 11994 – 12000.

Wegman, E. J. and Marchette, D. J. (2003). On some techniques for streaming data: A case study of internet packet headers. http://www.dmarchette.com/Papers/VisPacketHeadersRev1.pdf

Williams, L. R. (2011). Artificial cells as reified quines, https://www.cs.unm.edu/~williams/williams-ecal11.pdf.

Wu, S. X. and Banzhaf, W. (2010). The use of computational intelligence in intrusion detection systems: A review, Applied Soft Computing 10(1), 1–35. http://www.sciencedirect.com/science/article/pii/S1568494609000908

Zeleznikow, J. and Nolan, J. R. (2001). Using soft computing to build real world intelligent decision support systems in uncertain domains, Decision Support Systems 31(2), 263–285.