Weakly Supervised Deep Learning for the Detection of Domain Generation Algorithms [PDF]
IEEE Access, 2019 Domain generation algorithms (DGAs) have become commonplace in malware that seeks to establish command and control communication between an infected machine and the botmaster.
Bin Yu +6 more
doaj +10 more sources
Domain generation algorithms detection with feature extraction and Domain Center construction. [PDF]
PLoS ONE, 2023 Network attacks using Command and Control (C&C) servers have increased significantly. To hide their C&C servers, attackers often use Domain Generation Algorithms (DGA), which automatically generate domain names for C&C servers.
Xinjie Sun, Zhifang Liu
doaj +4 more sources
Exploiting statistical and structural features for the detection of Domain Generation Algorithms [PDF]
Journal of Information Security and Applications, 2021 Nowadays, malware campaigns have reached a high level of sophistication, thanks to the use of cryptography and covert communication channels over traditional protocols and services. In this regard, a typical approach to evade botnet identification and takedown mechanisms is the use of domain fluxing through the use of Domain Generation Algorithms (DGAs)
Constantinos Patsakis, Fran Casiño
exaly +5 more sources
AdamW+: Machine Learning Framework to Detect Domain Generation Algorithms for Malware [PDF]
IEEE AccessAdvanced Persistent Threats commonly use Domain Generation Algorithms to evade advanced detection methods to establish communication with their command and control servers.
Awais Javed +5 more
doaj +4 more sources
LLMs for Domain Generation Algorithm Detection [PDF]
CoRRThis work analyzes the use of large language models (LLMs) for detecting domain generation algorithms (DGAs). We perform a detailed evaluation of two important techniques: In-Context Learning (ICL) and Supervised Fine-Tuning (SFT), showing how they can improve detection.
Reynier Leyva La O +2 more
openalex +3 more sources
Use of subword tokenization for domain generation algorithm classification [PDF]
Cybersecurity, 2023 AbstractDomain name generation algorithm (DGA) classification is an essential but challenging problem. Both feature-extracting machine learning (ML) methods and deep learning (DL) models such as convolutional neural networks and long short-term memory have been developed. However, the performance of these approaches varies with different types of DGAs.
Sea Ran Cleon Liew, Ngai-Fong Law
openalex +4 more sources
Detecting Stealthy Domain Generation Algorithms Using Heterogeneous Deep Neural Network Framework
IEEE Access, 2020 Distinguishing malicious domain names generated by various domain generation algorithms (DGA) is critical for defending a network against sophisticated network attacks.
Luhui Yang +4 more
doaj +3 more sources
Towards Robust Domain Generation Algorithm Classification [PDF]
Proceedings of the 19th ACM Asia Conference on Computer and Communications SecurityIn this work, we conduct a comprehensive study on the robustness of domain generation algorithm (DGA) classifiers. We implement 32 white-box attacks, 19 of which are very effective and induce a false-negative rate (FNR) of $\approx$ 100\% on unhardened classifiers.
Arthur Drichel, Marc Meyer, Ulrike Meyer
openalex +3 more sources
An orderly algorithm for generation of Condorcet Domains [PDF]
Condorcet domains are fundamental objects in the theory of majority voting; they are sets of linear orders with the property that if every voter picks a linear order from this set, assuming that the number of voters is odd, and alternatives are ranked according to the pairwise majority ranking, then the result is a linear order on the set of all ...
Bei Zhou, Klas Markström
openalex +3 more sources
A Domain Analysis to Specify Design Defects and Generate Detection Algorithms [PDF]
, 2008 Quality experts often need to identify in software systems design defects, which are recurring design problems, that hinder development\ud and maintenance.
Duchien, Laurence +3 more
core +10 more sources