Understanding AI and ML

Dilli Prasad Sharma (), Arash Habibi Lashkari (), Mahdi Daghmehchi Firoozjaei (), Samaneh Mahdavifar () and Pulei Xiong ()
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Dilli Prasad Sharma: University of Toronto
Arash Habibi Lashkari: York University
Mahdi Daghmehchi Firoozjaei: MacEwan University
Samaneh Mahdavifar: McGill University
Pulei Xiong: National Research Council of Canada

Chapter Chapter 2 in Understanding AI in Cybersecurity and Secure AI, 2025, pp 15-35 from Springer

Abstract: Abstract This chapter introduces the foundational concepts of Artificial Intelligence (AI) and Machine Learning (ML), highlighting their importance and applications in cybersecurity. It provides an overview of supervised, unsupervised, semi-supervised, and reinforcement learning approaches, including linear regression, logistic regression, decision trees, support vector machines, and k-nearest neighbors, focusing on their cybersecurity roles. It also covers deep learning models like Feedforward Neural Networks, Convolutional Neural Networks, Recurrent Neural Networks, Long Short-Term Memory networks, Autoencoders, and Transfer Learning and their applications in solving complex cybersecurity problems such as anomaly detection and malware detection.

Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:spr:prochp:978-3-031-91524-6_2

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DOI: 10.1007/978-3-031-91524-6_2

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