BioShieldNet: Advanced biologically inspired mechanisms for strengthening cybersecurity in distributed computing environments
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Abstract
The objective of this research is to develop BioShieldNet, an advanced cybersecurity framework inspired by biological systems, aimed at addressing the evolving threats in distributed computing environments. This study employs a combination of experimental research and simulation techniques to design and evaluate biologically-inspired security mechanisms, including adaptive immune responses, self-healing capabilities, and evolutionary adaptation. The framework integrates advanced machine learning algorithms and pattern recognition techniques to enhance threat detection and mitigation. Results indicate that BioShieldNet achieves a 97.8% detection accuracy for zero-day vulnerabilities and reduces false positives by 23% compared to traditional cybersecurity methods. Furthermore, the adaptive and self-healing capabilities of BioShieldNet improve system resilience, reducing response time to new threats by 35%. Scalability tests demonstrate the framework's efficiency in handling large-scale distributed environments, with a 15% increase in throughput. The findings underscore the significant potential of BioShieldNet to enhance cybersecurity, offering a robust and scalable solution for protecting complex network infrastructures. This research contributes to the field by providing a novel, interdisciplinary approach to cybersecurity, with broad implications for the development of resilient and adaptive security systems.
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References
Alam, S., Saini, J. R., & Kapoor, R. (2018). Bio-inspired algorithms in cloud computing: Survey and open research challenges. Journal of Cloud Computing: Advances, Systems and Applications, 7(1), 1-22. https://doi.org/10.1186/s13677-018-0117-1
Beigh, B. M., & Peer, M. A. (2019). A review of bio-inspired routing protocols for wireless sensor networks. Journal of Network and Computer Applications, 99, 43-58. https://doi.org/10.1016/j.jnca.2017.11.002
Blasiak, A., Mazur, M., & Ortyl, M. (2020). Bio-inspired models of secure distributed systems. Journal of Theoretical Biology, 507, 110455. https://doi.org/10.1016/j.jtbi.2020.110455
Dasgupta, D., Frazier, J., & Bowers, C. P. (2013). Bio-inspired cybersecurity for smart grid communications. IEEE Communications Magazine, 51(1), 70-78. https://doi.org/10.1109/MCOM.2013.6476870
Dasgupta, D., & Majumdar, S. (2015). Anomaly detection in cybersecurity: From machine learning to biological inspired techniques. Journal of Critical Infrastructure Protection, 10, 1-11. https://doi.org/10.1016/j.ijcip.2015.07.001
Dorigo, M., & Stützle, T. (2019). Ant colony optimization: Overview and recent advances. Handbook of Metaheuristics, 311-351. https://doi.org/10.1007/978-1-4939-8753-1_11
Gu, Y., Sun, D., & Wu, C. (2021). A survey of bio-inspired computing in cyber-physical systems. IEEE Access, 9, 4567-4583. https://doi.org/10.1109/ACCESS.2020.3047943
Li, H., Wang, H., & Lu, W. (2020). Bio-inspired neural networks for cybersecurity. IEEE Transactions on Neural Networks and Learning Systems, 31(8), 2944-2955. https://doi.org/10.1109/TNNLS.2019.2949041
Liu, C., & Zhang, S. (2020). Bio-inspired cryptographic techniques for secure communication. Journal of Cryptographic Engineering, 10(4), 453-468. https://doi.org/10.1007/s13389-020-00220-1
Niyaz, Q., Sun, W., & Javaid, A. Y. (2016). A deep learning based DDoS detection system in software-defined networking (SDN). EAI Endorsed Transactions on Security and Safety, 3(9), e4. https://doi.org/10.4108/eai.28-12-2017.153515
Parpinelli, R. S., & Lopes, H. S. (2011). New inspirations in swarm intelligence: A survey. International Journal of Bio-Inspired Computation, 3(1), 1-16. https://doi.org/10.1504/IJBIC.2011.038555
Pradhan, S., Panda, M., & Pati, B. (2017). Bio-inspired computational algorithms for information security: A review. Journal of Information Security and Applications, 35, 44-54. https://doi.org/10.1016/j.jisa.2017.06.003
Rao, S. S., & Srinivas, K. (2020). Intrusion detection in distributed computing environments: A bio-inspired approach. Journal of Network and Computer Applications, 163, 102672. https://doi.org/10.1016/j.jnca.2020.102672
Saha, S., Bhattacharyya, D., & Kim, T. (2021). Bio-inspired encryption techniques for secure distributed systems. International Journal of Information Management, 58, 102230. https://doi.org/10.1016/j.ijinfomgt.2020.102230
Saleem, Y., & Rehman, S. U. (2014). Bio-inspired routing protocols for wireless sensor networks: A review and future challenges. International Journal of Distributed Sensor Networks, 10(10), 873410. https://doi.org/10.1155/2014/873410
Schimit, P. H., & Filho, M. A. G. (2015). Bio-inspired algorithms applied to network security: A survey. Applied Soft Computing, 33, 15-28. https://doi.org/10.1016/j.asoc.2015.04.054
Sharma, M., & Bansal, R. K. (2015). Anomaly detection in network traffic using bio-inspired algorithm. International Journal of Computer Applications, 127(11), 9-12. https://doi.org/10.5120/ijca2015906511
Singh, A., & Dhawan, P. (2017). Bio-inspired optimization techniques in distributed computing: A survey. Journal of Network and Computer Applications, 97, 68-87. https://doi.org/10.1016/j.jnca.2017.08.001
Verma, S., & Kumar, E. (2016). Bio-inspired security mechanisms for cloud computing environments. Journal of Information Security, 7(2), 113-123. https://doi.org/10.4236/jis.2016.72008
Wang, Y., & Wang, X. (2018). Biologically inspired cybersecurity solutions: A survey. IEEE Access, 6, 67493-67510. https://doi.org/10.1109/ACCESS.2018.2879374