Výsledky vyhledávání - "Lukas Machlica"

On Model Evaluation Under Non-constant Class Imbalance

Autor: Lukas Machlica, Vojtěch Franc, Tomáš Komárek, Jan Brabec

Publikováno v: Lecture Notes in Computer Science ISBN: 9783030504229
ICCS (4)

Many real-world classification problems are significantly class-imbalanced to detriment of the class of interest. The standard set of proper evaluation metrics is well-known but the usual assumption is that the test dataset imbalance equals the real-

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::32fef37d90cbf4cfd7b6ce1655ec0dee
https://doi.org/10.1007/978-3-030-50423-6_6

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Joint Detection of Malicious Domains and Infected Clients

Autor: Rene Knaebel, Tobias Scheffer, Lukas Machlica, Paul Prasse, Tomas Pevny

Detection of malware-infected computers and detection of malicious web domains based on their encrypted HTTPS traffic are challenging problems, because only addresses, timestamps, and data volumes are observable. The detection problems are coupled, b

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::499179b9bd155aa5151d4cf645b41ad1

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Decision-Forest Voting Scheme for Classification of Rare Classes in Network Intrusion Detection

Autor: Lukas Machlica, Jan Brabec

Publikováno v: 2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC).

In this paper, Bayesian based aggregation of decision trees in an ensemble (decision forest) is investigated. The focus is laid on multi-class classification with number of samples significantly skewed toward one of the classes. The algorithm leverag

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::86bcea6ba613c2e92c5620302893c06d
https://doi.org/10.1109/smc.2018.00563

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Malware Detection by Analysing Network Traffic with Neural Networks

Autor: Lukas Machlica, Tomas Pevny, Paul Prasse, Jiri Havelka, Tobias Scheffer

Publikováno v: IEEE Symposium on Security and Privacy Workshops

In order to evade network-traffic analysis, an increasing proportion of malware uses the encrypted HTTPS protocol. We study the problem of detecting malware on client computers based on HTTPS traffic analysis. Here, malware has to be detected based o

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::97cbc5f05e53db3d87d1b0331407eff6
https://doi.org/10.1109/spw.2017.8

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Malware Detection by Analysing Encrypted Network Traffic with Neural Networks

Autor: Paul Prasse, Jiří Havelka, Lukas Machlica, Tobias Scheffer, Tomáš Pevný

Publikováno v: Machine Learning and Knowledge Discovery in Databases ISBN: 9783319712451
ECML/PKDD (2)

We study the problem of detecting malware on client computers based on the analysis of HTTPS traffic. Here, malware has to be detected based on the host address, timestamps, and data volume information of the computer’s network traffic. We develop

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::a71e307864204139f1042c0ae57e8afc
https://doi.org/10.1007/978-3-319-71246-8_5

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