Applications Of Linear Algebra in Machine Learning for Cybersecurity

Cybersecurity systems increasingly rely on machine learning to process security-related information and distinguish ordinary activity from behavior that may indicate a threat.This review argues that linear algebra is the mathematical layer that makes these learning applications possible by organizing security observations as vectors and matrices and by supporting transformations used before and during machine learning.Eigenvalues and eigenvectors are also important in dimensionality-reduction techniques such as Principal Component Analysis (PCA).Six review papers are discussed, covering intrusion detection, malware, anomalous behavior, phishing, and network attacks.The purpose is to connect the cybersecurity applications reported in those studies with the mathematical representation on which learning algorithms depend.This connection also provides a practical way to relate linear algebra concepts studied in computer science to cybersecurity applications.