Intelligent model for the detection and classification of encrypted network traffic in cloud infrastructure

被引:0
|
作者
Dawood, Muhammad [1 ]
Xiao, Chunagbai [1 ]
Tu, Shanshan [1 ]
Alotaibi, Faiz Abdullah [2 ]
Alnfiai, Mrim M. [3 ]
Farhan, Muhammad [4 ]
机构
[1] Beijing Univ Technol, Fac Informat Technol, Beijing, Peoples R China
[2] King Saud Univ, Dept Informat Sci, Coll Humanities & Social Sci, Riyadh, Saudi Arabia
[3] Taif Univ, Dept Informat Technol, Coll Comp & Informat Technol, Taif, Saudi Arabia
[4] Al Akhawayn Univ Ifrane, Sch Sci & Engn, Ifrane, Morocco
基金
北京市自然科学基金;
关键词
Cloud security; Traffic classification; Intelligent model; Machine learning; SDN; ATTACK DETECTION;
D O I
暂无
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
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 HTTPS (DoH). DoH aims to combat the eavesdropping and tampering of DNS data during communication. This study employs a ML-based classification approach on a dataset for traffic analysis. The AdaBoost model effectively classified Malicious and Non-DoH traffic, with accuracies of 75% and 73% for DoH traffic. The support vector classification model with a Radial Basis Function (SVC-RBF) achieved a 76% accuracy in classifying between malicious and non-DoH traffic. The quadratic discriminant analysis (QDA) model achieved 99% accuracy in classifying malicious traffic and 98% in classifying non-DoH traffic.
引用
收藏
页数:25
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