@unpublished{Arnold2009,
  author    = {Arnold, Holger},
  title     = {A linearized DPLL calculus with clause learning (2nd, revised version)},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus-29080},
  year      = {2009},
  abstract  = {Many formal descriptions of DPLL-based SAT algorithms either do not include all essential proof techniques applied by modern SAT solvers or are bound to particular heuristics or data structures. This makes it difficult to analyze proof-theoretic properties or the search complexity of these algorithms. In this paper we try to improve this situation by developing a nondeterministic proof calculus that models the functioning of SAT algorithms based on the DPLL calculus with clause learning. This calculus is independent of implementation details yet precise enough to enable a formal analysis of realistic DPLL-based SAT algorithms.},
  language  = {en}
}
@unpublished{PrasseGrubenMachlikaetal.2016,
  author    = {Prasse, Paul and Gruben, Gerrit and Machlika, Lukas and Pevny, Tomas and Sofka, Michal and Scheffer, Tobias},
  title     = {Malware Detection by HTTPS Traffic Analysis},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-100942},
  pages     = {10},
  year      = {2016},
  abstract  = {In order to evade detection by network-traffic analysis, a growing proportion of malware uses the encrypted HTTPS protocol. We explore the problem of detecting malware on client computers based on HTTPS traffic analysis. In this setting, malware has to be detected based on the host IP address, ports, timestamp, and data volume information of TCP/IP packets that are sent and received by all the applications on the client. We develop a scalable protocol that allows us to collect network flows of known malicious and benign applications as training data and derive a malware-detection method based on a neural networks and sequence classification. We study the method's ability to detect known and new, unknown malware in a large-scale empirical study.},
  language  = {en}
}