Application of Reactive Artificial Intelligence Model to Predict Malicious Activities

Abstract

The critical infrastructure in the United States have been attacked countless time, and the federal, state, and private entities are concerned with securing their network infrastructure and sensitive data to prevent revenue loss and intellectual properties. Billion dollars are lost to cyberattacks globally, and the United States tops the list of countries that recorded more cyber-attacks. The healthcare industries have lost revenue due to litigation from compromised patient data and revenue paid as a ransom to cyber attackers. Integrating a reactive artificial neural network model into their cybersecurity architecture will monitor the activities of users' activities internally or externally accessing the organization's resources. The Newton method algorithm was applied to predict the malicious activities and the performance of the model support derivative matrix from the input variables that were used to train the model. The validation result of the algorithm has proven that an artificial neural network model can be deployed and integrated into the cybersecurity solution to predict the likelihood of an attack. Keywords: Healthcare, Cybersecurity, Artificial Intelligence, Threat Actors, Risk Assessment. Yamcharoen P., Folorunsho O.S., Bayewu A. & Ojo T.P. (2021): Application of Reactive Artificial Intelligence Model to Predict Malicious Activities. Journal of Advances in Mathematical & Computational Science. Vol. 9, No. 2. Pp 61-68. DOI: dx.doi.org/10.22624/AIMS/MATHS/V9N2P5 Available online at www.isteams.net/mathematics-computationaljournal.