MODIFIED WOLF SHEEP PREDATION ALGORITHM FOR NETWORK THREAT REDUCTION

Автор: Audecious Mugwagwa, Colin Chibaya, Ernest Bhero
Организация: University of KwaZulu Natal, Sol Plaatje University

Категория:

Ключевые слова: cybersecurity, cyberthreats, self-organization, swarm intelligence, algorithms
Аннотация. Because most attacks target computers, intrusion detection has emerged as a key component of network security. This is a result of the widespread expansion of internet connectivity and information system accessibility on a global scale. The Wolf Sheep Predation Algorithm (WSPA), evolved from the Wolf Pack Algorithm. It models how wolves hunt in packs. This paper focused on the Lotka-Volterra predator-prey model. Due to its global convergence and computational strength, it has mostly been applied in a variety of engineering optimization issues. The method, however, has numerous flaws, including slow convergence and a tendency to quickly reach the local optimum. To address the above-mentioned flaws, this research developed the Modified Wolf Sheep Predation Algorithm (MWSPA) to reduce network threats. the algorithm models the wolves and sheep, where the wolves in this study represent the network security agent while the sheep represent network threats. The model suggests a better strategy to address the problem of slow convergence and quickly reach the local optimum by making sure that there is a balanced ecosystem at any point in time. This is achieved by ensuring that the network security agents(wolves) are not outnumbered by threats(sheep) and they do not become extinct when there is no food source. So in the absence of food, the MWSPA ensures the wolves can survive on grass and maintain their strength to hunt their next prey. This idea prevents the algorithm from crashing if the wolves die while the prey grows to infinity and consumes all the available grass. This therefore solves the problem of rapidly failing into a local optimum. This study aimed to identify the most pertinent features employed by wolves (network security agents) while hunting the sheep (network threats). We therefore established that sense of hearing and smell, splitting prey, encircling prey, assisting the hunter with the best chance of success, and looking for alternative prey as the most outstanding attributes used by wolves while hunting. The study further evaluated the MWSPA, and the outcomes demonstrate that the suggested algorithm outperforms its predecessor approach in a variety of search environments. Therefore, this shows that the MWSPA may possess the necessary qualities for creating a solution that will completely eradicate network threats and might provide leads in solving growing cybersecurity concerns globally.

Библиография:

1. Alok, M., Yehia, I. A., Memoona, J. A. & Asif, Q. G., 2022. Attributes impacting cybersecurity policy development: An evidence from seven nations. Elsevier, Computers & Security.
2. Ansam, K., Iqbal, G., Peter, V. & Joarder, K., 2019. Survey of intrusion detection systems: techniques, datasets and challenges. Springer, cybersecurity.
3. CISA, C. A. I. S. A., 2022. Building more resilient ICT Supply Chain: Lessons learned during the COVID-19 Pandemic, s.l.: CYBERSECURITY AND INFRASTRUCTURE SECURITY AGENCY.
4. Dipanjan, C., Sanchayan, B. & De, R., 2020. Survival chances of a prey swarm: how the cooperative interaction range affects the outcome. PubMed Central, Scientific Reports.
5. Eric, G. & Anca, J., 2022. Intrusion Detection in Internet of Things Systems: A Review on Design Approaches Leveraging Multi-Access Edge Computing, Machine Learning, and Datasets. National Library of Medical Science.
6. Frank, A., Subbey, S., Kobras, M. & Gjøsæter, H., 2021. Population dynamic regulators in an empirical predator-prey system. Science Direct, Journal of Theoretical Biology, Volume 527.
7. Government of Canada, 2022. An introduction to the cyber threat environment, ottawa: Canadan Centre for Cybersecurity.
8. Hans, d. B. & Marijn, J., 2017. Cybersecurity Awareness: The need for evidence-based framing strategies. Elsevier, Government Information Quarterly.
9. Heloise, P., 2022. The Cyber Threat Landscape in South Africa: A 10-Year Review. The African Journal of Information and Communication.
10. Jiaze, T., Huiling, C., Mingjing, W. & Amir, H. G., 2021. The Colony Predation Algorithm. Journal of Bionic Engineering.
11. Mugwagwa, A., Chibaya, C. & Bhero, E., 2023. A survey of inspiring swarm intelligence models for the design of a swarm-based ontology for addressing the cyber security problem. INTERNATIONAL JOURNAL OF RESEARCH IN BUSINESS AND SOCIAL SCIENCE, 12(4), pp. 483-494.
12. Muro, C., Escobedo, R., Specto, L. & Coppinger, R., 2021. Wolf-pack (Canis lupus) hunting strategies emerge from simple rules in computational simulations. Elsevier, Behavioural Processes, Volume 88, pp. 192-197.
13. NIST, N. I. o. S. a. T., 2011. Managing Information Security Risk, s.l.: nist.
14. Noah, B., Victor, L. & Frithjof, L., 2021. Seasonal dynamics of a generalist and a specialist predator on a single prey. Mathematics in Applied Sciences and Engineering .
15. Ponnusamy, V. et al., 2021. Intrusion Detection Systems in Internet of Things and Mobile Ad-Hoc Networks. Tech Scoence Press, Computer Systems Science & Engineering, .
16. Rui, T., Simon, F., Xin-She, Y. & Deb, S., 2012. Wolf search algorithm with ephemeral memory. 2012 Seventh International Conference on Digital Information Management (ICDIM).
17. Sikender, M. M. & Lakshmisri, S., 2018. Security Automation in Information Technology. SSRN Electronic Journal, pp. 901-905.
18. Thomas, J. H., Kevin, D. & Murray, L., 2021. Increasing availability of palatable prey induces predator-dependence and increases predation on unpalatable prey. PubMed Central, Scientific Reports .
19. Weitzenfeld, A. & Vallesa, A., 2006. A Biologically-Inspired Wolf Pack Multiple Robot Hunting Model. IEEE Latin American Robotics Symposium, LARS.
20. World Economic Forum, 2022. Global Cybersecurity Outlook 2022, Insights Report January 2022, s.l.: s.n.
21. Wu, H. & Zhang, F., 2014. Wolf pack algorithm for unconstrained global optimization.. Mathematical Problems in Engineering.
22. Xuan, C. et al., 2021. An improved Wolf pack algorithm for optimization problems: Design and evaluation. PLOS ONE.
23. Xuan, C. et al., 2021. An improved Wolf pack algorithm for optimization problems: Design and evaluation. PLos One.
24. Yuchong, L. & Qinghui, L., 2021. A comprehensive review study of cyber-attacks and cyber security; Emerging trends and recent developments. Elsevier, pp. 8176-8186.