6 citations · 8 across the 12 of their papers we have counts for
12 papers
GRASP -- Graph-Based Anomaly Detection Through Self-Supervised Classification
Robin Buchta, Carsten Kleiner, Felix Heine +1
Advanced persistent threat (APT) attacks remain difficult to detect due to their stealth, adaptability, and use of legitimate system components. Provenance-based intrusion detectio…
Strategies for Tracking Individual IP Packets Towards DDoS
Peter Hillmann, Frank Tietze, Gabi Dreo Rodosek
The identification of the exact path that packets are routed in the network is quite a challenge. This paper presents a novel, efficient traceback strategy in combination with a de…
How well can machine-generated texts be identified and can language models be trained to avoid identification?
Sinclair Schneider, Florian Steuber, Joao A. G. Schneider +1
With the rise of generative pre-trained transformer models such as GPT-3, GPT-NeoX, or OPT, distinguishing human-generated texts from machine-generated ones has become important. W…
Realizable Universal Adversarial Perturbations for Malware
Raphael Labaca-Castro, Luis Muñoz-González, Feargus Pendlebury +3
Machine learning classifiers are vulnerable to adversarial examples -- input-specific perturbations that manipulate models' output. Universal Adversarial Perturbations (UAPs), whic…
Dragoon: Advanced Modelling of IP Geolocation by use of Latency Measurements
Peter Hillmann, Lars Stiemert, Gabi Dreo Rodosek +1
IP Geolocation is a key enabler for many areas of application like determination of an attack origin, targeted advertisement, and Content Delivery Networks. Although IP Geolocation…
A Novel Approach to Solve K-Center Problems with Geographical Placement
Peter Hillmann, Tobias Uhlig, Gabi Dreo Rodosek +1
The facility location problem is a well-known challenge in logistics that is proven to be NP-hard. In this paper we specifically simulate the geographical placement of facilities t…