Zobrazeno 1 - 10
of 400
pro vyhledávání: '"Drichel A"'
Publikováno v:
Telemática, Vol 22 (2024)
El presente trabajo propone el diseño y simulación de una antena fractal alfombra de Sierpinski que opera en la banda de 2.4GHz para su empleo en aplicaciones de tecnología de identificación por radiofrecuencia o RFID (Radio Frequency Identificac
Externí odkaz:
https://doaj.org/article/7424232af97c4607b780cef60792b3c8
Publikováno v:
ACM Asia Conference on Computer and Communications Security (ASIA CCS 2024)
In 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 unharden
Externí odkaz:
http://arxiv.org/abs/2404.06236
Autor:
Drichel, Arthur, Meyer, Ulrike
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%. However, these classifiers provide a false sense o
Externí odkaz:
http://arxiv.org/abs/2307.04358
New malware emerges at a rapid pace and often incorporates Domain Generation Algorithms (DGAs) to avoid blocking the malware's connection to the command and control (C2) server. Current state-of-the-art classifiers are able to separate benign from ma
Externí odkaz:
http://arxiv.org/abs/2205.14940
Autor:
Matzutt, Roman, Kalde, Benedikt, Pennekamp, Jan, Drichel, Arthur, Henze, Martin, Wehrle, Klaus
Publikováno v:
in IEEE Transactions on Network and Service Management, vol. 18, no. 3, pp. 3064-3078, Sept. 2021
Popular cryptocurrencies continue to face serious scalability issues due to their ever-growing blockchains. Thus, modern blockchain designs began to prune old blocks and rely on recent snapshots for their bootstrapping processes instead. Unfortunatel
Externí odkaz:
http://arxiv.org/abs/2111.13525
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 se
Externí odkaz:
http://arxiv.org/abs/2110.05849
Domain generation algorithms (DGAs) prevent the connection between a botnet and its master from being blocked by generating a large number of domain names. Promising single-data-source approaches have been proposed for separating benign from DGA-gene
Externí odkaz:
http://arxiv.org/abs/2109.11830
Finding Phish in a Haystack: A Pipeline for Phishing Classification on Certificate Transparency Logs
Current popular phishing prevention techniques mainly utilize reactive blocklists, which leave a ``window of opportunity'' for attackers during which victims are unprotected. One possible approach to shorten this window aims to detect phishing attack
Externí odkaz:
http://arxiv.org/abs/2106.12343
Numerous malware families rely on domain generation algorithms (DGAs) to establish a connection to their command and control (C2) server. Counteracting DGAs, several machine learning classifiers have been proposed enabling the identification of the D
Externí odkaz:
http://arxiv.org/abs/2106.12336
Publikováno v:
In The 15th International Conference on Availability, Reliability and Security (ARES 2020), ACM, 9 pages
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 domai
Externí odkaz:
http://arxiv.org/abs/2007.00300