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Found 9 papers, 4 papers with code
Towards Robust Domain Generation Algorithm Classification
In this work, we conduct a comprehensive study on the robustness of domain generation algorithm (DGA) classifiers.
False Sense of Security: Leveraging XAI to Analyze the Reasoning and True Performance of Context-less DGA Classifiers
The problem of revealing botnet activity through Domain Generation Algorithm (DGA) detection seems to be solved, considering that available deep learning classifiers achieve accuracies of over 99. 9%.
Detecting Unknown DGAs without Context Information
While binary classifiers can label domains of yet unknown DGAs as malicious, multiclass classifiers can only assign domains to DGAs that are known at the time of training, limiting the ability to uncover new malware families.
Sharing FANCI Features: A Privacy Analysis of Feature Extraction for DGA Detection
The goal of Domain Generation Algorithm (DGA) detection is to recognize infections with bot malware and is often done with help of Machine Learning approaches that classify non-resolving Domain Name System (DNS) traffic and are trained on possibly sensitive data.
The More, the Better? A Study on Collaborative Machine Learning for DGA Detection
In this paper, we complement the research area of DGA detection by conducting a comprehensive collaborative learning study, including a total of 13, 440 evaluation runs.
Finding Phish in a Haystack: A Pipeline for Phishing Classification on Certificate Transparency Logs
In this paper, we present a pipeline that facilitates such evaluations by addressing a number of problems when working with CT log data.
First Step Towards EXPLAINable DGA Multiclass Classification
Numerous malware families rely on domain generation algorithms (DGAs) to establish a connection to their command and control (C2) server.
Making Use of NXt to Nothing: The Effect of Class Imbalances on DGA Detection Classifiers
Numerous machine learning classifiers have been proposed for binary classification of domain names as either benign or malicious, and even for multiclass classification to identify the domain generation algorithm (DGA) that generated a specific domain name.
Analyzing the Real-World Applicability of DGA Classifiers
In this context, we propose one novel classifier based on residual neural networks for each of the two tasks and extensively evaluate them as well as previously proposed classifiers in a unified setting.
