Data-Driven Mathematical Modeling for AI-Based Security Applications

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Author(s): Sini Anna Alex (Ramaiah Institute of Technology, India), Parkavi A. (Ramaiah Institute of Technology, India)and Sangeetha V. (Ramaiah Institute of Technology, India)
Copyright: 2023
Pages: 17
Source title: Handbook of Research on Data-Driven Mathematical Modeling in Smart Cities
Source Author(s)/Editor(s): Sabyasachi Pramanik (Haldia Institute of Technology, India)and K. Martin Sagayam (Karunya Institute of Technology and Sciences, India)
DOI: 10.4018/978-1-6684-6408-3.ch010

Keywords: Data Analysis and Statistics / Engineering Science Reference / Knowledge Management and Library Science / Mathematics & Statistics

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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.

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Data-Driven Mathematical Modeling for AI-Based Security Applications

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