• Bohdan Vihurskyi

Anomaly detection is a critical component of network security, allowing organizations to identify and respond to abnormal activities that may indicate security threats. Unsupervised learning algorithms play a crucial role in anomaly detection by analyzing patterns in data without the need for labeled examples. This article provides an overview of several unsupervised learning algorithms commonly used for anomaly detection in network security, including clustering, isolation forests, auto-encoders, and principal component analysis (PCA). Real-world case studies and applications demonstrate how these algorithms can be deployed to detect intrusions, malware, DDoS attacks, and other security threats. Additionally, the article discusses challenges, considerations, and future directions in the field, such as advancements in deep learning, model interpretability, privacy-preserving techniques, and the integration of edge computing and IoT technologies.

Anomaly Detection, Unsupervised Learning, Network Security, Clustering, Isolation Forests, Auto-encoders, Principal Component Analysis (PCA).

"EXPLORING UNSUPERVISED LEARNING ALGORITHMS FOR ANOMALY DETECTION IN NETWORK SECURITY", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 5, page no.i24-i33, May-2024, Available :http://www.jetir.org/papers/JETIR2405805.pdf

"EXPLORING UNSUPERVISED LEARNING ALGORITHMS FOR ANOMALY DETECTION IN NETWORK SECURITY", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 5, page no. ppi24-i33, May-2024, Available at : http://www.jetir.org/papers/JETIR2405805.pdf