Intelligent model for the detection and classification of encrypted network traffic in cloud infrastructure
Muhammad Dawood, Chunagbai Xiao, Shanshan Tu, Faiz Abdullah Alotaibi, Mrim M. Alnfiai, Muhammad Farhan
TL;DR
The paper presents a machine learning approach to detect and classify encrypted DNS traffic, aiming to improve cybersecurity in cloud environments.
Contribution
The study introduces a novel ML-based classification method for encrypted DNS traffic detection and evaluation of model performance.
Findings
The AdaBoost model achieved 75% accuracy for malicious traffic and 73% for DoH traffic.
The QDA model showed high accuracy of 99% for malicious and 98% for non-DoH traffic.
SVC-RBF model reached 76% accuracy in distinguishing malicious from non-DoH traffic.
Abstract
This article explores detecting and categorizing network traffic data using machine-learning (ML) methods, specifically focusing on the Domain Name Server (DNS) protocol. DNS has long been susceptible to various security flaws, frequently exploited over time, making DNS abuse a major concern in cybersecurity. Despite advanced attack, tactics employed by attackers to steal data in real-time, ensuring security and privacy for DNS queries and answers remains challenging. The evolving landscape of internet services has allowed attackers to launch cyber-attacks on computer networks. However, implementing Secure Socket Layer (SSL)-encrypted Hyper Text Transfer Protocol (HTTP) transmission, known as HTTPS, has significantly reduced DNS-based assaults. To further enhance security and mitigate threats like man-in-the-middle attacks, the security community has developed the concept of DNS over…
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Taxonomy
TopicsNetwork Security and Intrusion Detection · Internet Traffic Analysis and Secure E-voting · Advanced Malware Detection Techniques