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A Lightweight Double-Stage Scheme to Identify Malicious DNS over HTTPS Traffic Using a Hybrid Learning Approach.

Qasem Abu Al-Haija¹, Manar Alohaly², Ammar Odeh³

¹Department of Cybersecurity, Princess Sumaya University for Technology (PSUT), Amman 11941, Jordan.

Sensors (Basel, Switzerland)

|April 13, 2023

Summary

This study introduces a new method to detect malicious DNS over HTTPS (DoH) traffic. The hybrid learning approach efficiently identifies threats with high accuracy, enhancing internet security.

Keywords:

DNS over HTTPS (DoH)Domain Name System (DNS)artificial intelligence cybersecurity machine learning

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Area of Science:

Computer Science
Cybersecurity
Network Security

Background:

The Domain Name System (DNS) is crucial for internet functionality but faces significant security vulnerabilities.
Attackers exploit DNS loopholes using advanced information-stealing techniques, challenging secure traffic delivery.
DNS over HTTPS (DoH) was developed to encrypt DNS traffic and enhance security over covert channels.

Purpose of the Study:

To propose a lightweight, double-stage scheme for identifying malicious DNS over HTTPS (DoH) traffic.
To enhance the security of DNS protocol by accurately distinguishing benign from malicious DoH traffic.
To develop a hybrid learning approach for efficient and accurate malicious DoH traffic detection.

Main Methods:

A two-layer hybrid learning system was developed for malicious DoH traffic identification.
The first layer uses random fine trees (RF) to classify traffic as DoH or non-DoH.
The second layer employs Adaboost trees (ADT) to differentiate between benign and malicious DoH traffic, utilizing PCA for feature selection and RUS for sample minimization.

Main Results:

The proposed system achieved high predictive accuracy, reaching 99.4% and 100% in its two layers.
Low predictive overhead was reported at 0.83 µs for layer one and 2.27 µs for layer two.
The model effectively utilizes a reduced feature set (18%) and sample set (17%) with balanced classes, outperforming existing models.

Conclusions:

The proposed lightweight, double-stage scheme offers a superior method for detecting malicious DoH traffic.
The hybrid learning approach, combined with PCA and RUS, provides high accuracy and efficiency.
This research significantly contributes to securing DNS traffic against advanced cyber threats.