Multi-Perspective Content Delivery Networks Security Framework Using Optimized Unsupervised Anomaly Detection

TL;DR

This paper introduces a multi-perspective unsupervised anomaly detection framework for CDN security, effectively identifying DoS and CPA attacks using real-world log data and advanced machine learning models.

Contribution

It presents a novel multi-perspective feature engineering and validation approach combined with optimized unsupervised models for CDN anomaly detection.

Findings

Effective detection of abnormal behaviors in CDN logs

Identification of attack types and compromised nodes

Validated results with real-world data and expert confirmation

Abstract

Content delivery networks (CDNs) provide efficient content distribution over the Internet. CDNs improve the connectivity and efficiency of global communications, but their caching mechanisms may be breached by cyber-attackers. Among the security mechanisms, effective anomaly detection forms an important part of CDN security enhancement. In this work, we propose a multi-perspective unsupervised learning framework for anomaly detection in CDNs. In the proposed framework, a multi-perspective feature engineering approach, an optimized unsupervised anomaly detection model that utilizes an isolation forest and a Gaussian mixture model, and a multi-perspective validation method, are developed to detect abnormal behaviors in CDNs mainly from the client Internet Protocol (IP) and node perspectives, therefore to identify the denial of service (DoS) and cache pollution attack (CPA) patterns. Experimental results are presented based on the analytics of eight days of real-world CDN log data provided by a major CDN operator. Through experiments, the abnormal contents, compromised nodes, malicious IPs, as well as their corresponding attack types, are identified effectively by the proposed framework and validated by multiple cybersecurity experts. This shows the effectiveness of the proposed method when applied to real-world CDN data.