Unsupervised Learning Techniques in Cybersecurity

Abstract

The rapid evolution of cyber threats has created a critical need for innovative techniques
that can effectively detect, mitigate, and respond to these threats in real time.
Unsupervised learning, a subset of machine learning, has emerged as a powerful tool for
cybersecurity professionals. Unlike supervised learning, which relies on labeled datasets,
unsupervised learning techniques analyze vast amounts of unlabeled data to identify
patterns, anomalies, and hidden structures. This paper explores the application of
unsupervised learning in cybersecurity, including techniques such as clustering, anomaly
detection, and association rule mining. It provides a detailed overview of how these
methods are used to detect unknown threats, uncover hidden attack vectors, and enhance
threat intelligence. Through case studies and real-world examples, this paper
demonstrates the effectiveness of unsupervised learning in identifying previously
unknown malware, detecting insider threats, and monitoring network traffic for unusual
activity. Additionally, it addresses the challenges of deploying unsupervised learning
models, such as managing false positives, handling large-scale datasets, and ensuring
model interpretability. The paper also discusses future trends, including the integration of
unsupervised learning with other advanced technologies like deep learning, federated
learning, and quantum computing, highlighting its potential to transform the
cybersecurity landscape.