A Comparative Study of Anomaly Detection Algorithms for Cybersecurity Applications

Abstract

Anomaly detection plays a pivotal role in cybersecurity by identifying irregular activities that could indicate potential security breaches, such as cyberattacks or unauthorized access. This paper presents a comparative study of several anomaly detection algorithms and evaluates their performance in the context of cybersecurity applications. The study focuses on both traditional statistical methods and modern machine learning algorithms, including clustering-based, supervised, and unsupervised techniques. Key algorithms examined include k-Nearest Neighbors (k-NN), Support Vector Machines (SVM), Isolation Forests, Principal Component Analysis (PCA), and neural network-based approaches like Autoencoders. The study assesses each algorithm's accuracy, robustness, scalability, and computational efficiency using a set of benchmark datasets typical in cybersecurity, such as intrusion detection system (IDS) logs and network traffic data. By comparing the strengths and weaknesses of these algorithms, this paper aims to provide a comprehensive understanding of their applicability in real-world cybersecurity scenarios, offering insights into their suitability for different types of threats and operational environments.