Results 11 to 20 of about 1,290,938 (334)

PKDGA: A Partial Knowledge-Based Domain Generation Algorithm for Botnets [PDF]

open access: yesIEEE Transactions on Information Forensics and Security, 2023
Domain generation algorithms (DGAs) can be categorized into three types: zero-knowledge, partial-knowledge, and full-knowledge. While prior research merely focused on zero-knowledge and full-knowledge types, we characterize their anti-detection ability and practicality and find that zero-knowledge DGAs present low anti-detection ability against ...
Lihai Nie, Laiping Zhao, Keqiu Li
exaly   +4 more sources

OpenSMax: Unknown Domain Generation Algorithm Detection

open access: hybridEuropean Conference on Artificial Intelligence, 2020
Yao Lai   +4 more
semanticscholar   +3 more sources

Detecting Domain Generation Algorithms with Bi-LSTM

open access: diamondComputers, Materials & Continua, 2019
: Botnets often use domain generation algorithms (DGA) to connect to a command and control (C2) server, which enables the compromised hosts connect to the C2 server for accessing many domains.
Liang Ding   +4 more
semanticscholar   +3 more sources

Class Incremental Deep Learning: A Computational Scheme to Avoid Catastrophic Forgetting in Domain Generation Algorithm Multiclass Classification [PDF]

open access: goldApplied Sciences
Domain Generation Algorithms (DGAs) are algorithms present in most malware used by botnets and advanced persistent threats. These algorithms dynamically generate domain names to maintain and obfuscate communication between the infected device and the ...
João Rafael Gregório   +2 more
doaj   +2 more sources

DomainGAN: Generating Adversarial Examples to Attack Domain Generation\n Algorithm Classifiers [PDF]

open access: greenCoRR, 2019
Domain Generation Algorithms (DGAs) are frequently used to generate numerous domains for use by botnets. These domains are often utilized as rendezvous points for servers that malware has command and control over. There are many algorithms that are used to generate domains, however many of these algorithms are simplistic and easily detected by ...
Isaac Corley   +2 more
openalex   +3 more sources

Inline Detection of Domain Generation Algorithms with Context-Sensitive Word Embeddings [PDF]

open access: green2018 IEEE International Conference on Big Data (Big Data), 2018
Domain generation algorithms (DGAs) are frequently employed by malware to generate domains used for connecting to command-and-control (C2) servers. Recent work in DGA detection leveraged deep learning architectures like convolutional neural networks ...
Joewie J. Koh, Barton Rhodes
openalex   +3 more sources

Exploring and comparing various machine and deep learning technique algorithms to detect domain generation algorithms of malicious variants

open access: diamondComputer Science and Information Technology, 2022
Domain generation algorithm (DGA) is used as the main source of script in different groups of malwares, which generates the domain names of points and will further be used for command-and-control servers.
Anoop Reddy Thatipalli   +3 more
openalex   +3 more sources

Survey of DGA Domain Name Detection Based on Character Feature [PDF]

open access: yesJisuanji kexue, 2023
Recent years have seen extensive adoption of domain generation algorithms(DGA) by botnets.Efficient detection of DGA domain name is of great significance for discovering botnets and ensuring cyber security.DGA domain name detection me-thod based on ...
WANG Yu, WANG Zuchao, PAN Rui
doaj   +1 more source

Toward Optimal LSTM Neural Networks for Detecting Algorithmically Generated Domain Names

open access: yesIEEE Access, 2021
Malware detection is a problem that has become particularly challenging over the last decade. A common strategy for detecting malware is to scan network traffic for malicious connections between infected devices and their command and control (C&C)
Jose Selvi   +2 more
doaj   +1 more source

Failure Modes of Domain Generalization Algorithms

open access: yes2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022
Domain generalization algorithms use training data from multiple domains to learn models that generalize well to unseen domains. While recently proposed benchmarks demonstrate that most of the existing algorithms do not outperform simple baselines, the established evaluation methods fail to expose the impact of various factors that contribute to the ...
Tigran Galstyan   +4 more
openaire   +2 more sources

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