Data-Driven Mathematical Modeling for AI-Based Security Applications

Abstract

Artificial intelligence (A.I.) is defined as the ability of a machine to perform cognitive functions that we associate with human minds, such as perceiving, reasoning, learning, interacting with the environment, problem solving, decision-making, and even demonstrating creativity. This field of artificial intelligence finds itself indispensable across various domains like shopping, fraud prevention, personalized learning, autonomous vehicles, voice assistants, etc. It is widely used in the field of cyber security. There are numerous AI based security models to delay security threats. Such algorithms could be classified under three heads – rule-based, shallow machine learning and deep learning algorithms. Fuzzy logic and fuzzy neural networks fall under rule-based algorithm. support vector machine (SVM), naïve bayes, decision tree, random forest, k-nearest neighbour and ensemble learning are some shallow ML algorithms that could be employed for cyber security.