Zobrazeno 1 - 10
of 10
pro vyhledávání: '"Maximilian Bachl"'
Autor:
Theresa Mayo, Marlen Haderlein, Barbara Schuster, Anna Wiesmüller, Christian Hummel, Maximilian Bachl, Manfred Schmidt, Rainer Fietkau, Luitpold Distel
Publikováno v:
Radiation Oncology, Vol 15, Iss 1, Pp 1-9 (2019)
Abstract Background Individual radiosensitivity is influencing the outcome of radiation therapy. A general ex vivo testing is very work-intensive. It is of interest to see if a significant prediction concerning the sensitivity can be made by in vivo
Externí odkaz:
https://doaj.org/article/c6c067ce19fa492fa7117b81e75fbea5
Publikováno v:
Applied Sciences, Vol 10, Iss 12, p 4307 (2020)
The increased interest in secure and reliable communications has turned the analysis of network traffic data into a predominant topic. A high number of research papers propose methods to classify traffic, detect anomalies, or identify attacks. Althou
Externí odkaz:
https://doaj.org/article/c82337843d97460e97cd4feb32a7fce8
Publikováno v:
LCN
The increasing number of different, incompatible congestion control algorithms has led to an increased deployment of fair queuing. Fair queuing isolates each network flow and can thus guarantee fairness for each flow even if the flows' congestion con
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::ecdb42ac13dd52463799872494fbe41b
https://doi.org/10.1109/lcn48667.2020.9314771
https://doi.org/10.1109/lcn48667.2020.9314771
Publikováno v:
BigDataService
Recurrent Neural Networks (RNNs) yield attractive properties for constructing Intrusion Detection Systems (IDSs) for network data. With the rise of ubiquitous Machine Learning (ML) systems, malicious actors have been catching up quickly to find new w
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::07a28a741420882ccf97124e3f4bd47b
https://doi.org/10.1109/bigdataservice49289.2020.00030
https://doi.org/10.1109/bigdataservice49289.2020.00030
Publikováno v:
CNS
Recurrent Neural Networks (RNNs) have been shown to be valuable for constructing Intrusion Detection Systems (IDSs) for network data. They allow determining if a flow is malicious or not already before it is over, making it possible to take action im
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::f593d480769e16d5fc3bb6fac842e381
Publikováno v:
CSNet
Fully Connected Neural Networks (FCNNs) have been the core of most state-of-the-art Machine Learning (ML) applications in recent years and also have been widely used for Intrusion Detection Systems (IDSs). Experimental results from the last years sho
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::fc78cd1f41cf357f760a7fa6db8bb705
Autor:
Maximilian Bachl, Manfred Schmidt, Christian Hummel, Barbara Schuster, Theresa Mayo, Luitpold Distel, Rainer Fietkau, Anna Wiesmüller, Marlen Haderlein
Publikováno v:
Radiation Oncology (London, England)
Radiation Oncology, Vol 15, Iss 1, Pp 1-9 (2019)
Radiation Oncology, Vol 15, Iss 1, Pp 1-9 (2019)
Background Individual radiosensitivity is influencing the outcome of radiation therapy. A general ex vivo testing is very work-intensive. It is of interest to see if a significant prediction concerning the sensitivity can be made by in vivo irradiati
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::e68831dd81759c417ee668620f3a7bbf
https://doi.org/10.1186/s13014-019-1444-4
https://doi.org/10.1186/s13014-019-1444-4
Publikováno v:
BS
Recent model-based congestion control algorithms such as BBR use repeated measurements at the endpoint to build a model of the network connection and use it to achieve optimal throughput with low queuing delay. Conversely, applying this model-based a
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::21c3ad0eda2f40ee650cf241d665a9cd
http://arxiv.org/abs/1910.10604
http://arxiv.org/abs/1910.10604
Publikováno v:
ICC
This paper proposes Reactive Adaptive eXperience based congestion control (Rax), a new method of congestion control (CC) that uses online reinforcement learning (RL) to maintain an optimum congestion window with respect to a given reward function and
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::05a0eb466da481b22747482997f9bd8f
https://doi.org/10.1109/icc.2019.8761187
https://doi.org/10.1109/icc.2019.8761187
Publikováno v:
Reproducibility@SIGCOMM
The selection of features for network traffic analysis and anomaly detection is a challenge for experts who aim to build systems that discover traffic patterns, characterize networks, and improve security. There are no major guidelines or best practi
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::b0af855725dcbc54e8282097e56b4a87
https://doi.org/10.1145/3097766.3097771
https://doi.org/10.1145/3097766.3097771