TY - JOUR A1 - Prasse, Paul A1 - Knaebel, Rene A1 - Machlica, Lukas A1 - Pevny, Tomas A1 - Scheffer, Tobias T1 - Joint detection of malicious domains and infected clients JF - Machine learning N2 - 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, because infected clients tend to interact with malicious domains. Traffic data can be collected at a large scale, and antivirus tools can be used to identify infected clients in retrospect. Domains, by contrast, have to be labeled individually after forensic analysis. We explore transfer learning based on sluice networks; this allows the detection models to bootstrap each other. In a large-scale experimental study, we find that the model outperforms known reference models and detects previously unknown malware, previously unknown malware families, and previously unknown malicious domains. KW - Machine learning KW - Neural networks KW - Computer security KW - Traffic data KW - Https traffic Y1 - 2019 U6 - https://doi.org/10.1007/s10994-019-05789-z SN - 0885-6125 SN - 1573-0565 VL - 108 IS - 8-9 SP - 1353 EP - 1368 PB - Springer CY - Dordrecht ER - TY - THES A1 - Ashouri, Mohammadreza T1 - TrainTrap BT - a hybrid technique for vulnerability analysis in JAVA Y1 - 2020 ER - TY - JOUR A1 - Laskov, Pavel A1 - Gehl, Christian A1 - Krüger, Stefan A1 - Müller, Klaus-Robert T1 - Incremental support vector learning: analysis, implementation and applications JF - Journal of machine learning research N2 - Incremental Support Vector Machines (SVM) are instrumental in practical applications of online learning. This work focuses on the design and analysis of efficient incremental SVM learning, with the aim of providing a fast, numerically stable and robust implementation. A detailed analysis of convergence and of algorithmic complexity of incremental SVM learning is carried out. Based on this analysis, a new design of storage and numerical operations is proposed, which speeds up the training of an incremental SVM by a factor of 5 to 20. The performance of the new algorithm is demonstrated in two scenarios: learning with limited resources and active learning. Various applications of the algorithm, such as in drug discovery, online monitoring of industrial devices and and surveillance of network traffic, can be foreseen. KW - incremental SVM KW - online learning KW - drug discovery KW - intrusion detection Y1 - 2006 SN - 1532-4435 VL - 7 SP - 1909 EP - 1936 PB - MIT Press CY - Cambridge, Mass. ER - TY - JOUR A1 - Steuer, Ralf A1 - Humburg, Peter A1 - Selbig, Joachim T1 - Validation and functional annotation of expression-based clusters based on gene ontology JF - BMC bioinformatics N2 - Background: The biological interpretation of large-scale gene expression data is one of the paramount challenges in current bioinformatics. In particular, placing the results in the context of other available functional genomics data, such as existing bio-ontologies, has already provided substantial improvement for detecting and categorizing genes of interest. One common approach is to look for functional annotations that are significantly enriched within a group or cluster of genes, as compared to a reference group. Results: In this work, we suggest the information-theoretic concept of mutual information to investigate the relationship between groups of genes, as given by data-driven clustering, and their respective functional categories. Drawing upon related approaches (Gibbons and Roth, Genome Research 12: 1574-1581, 2002), we seek to quantify to what extent individual attributes are sufficient to characterize a given group or cluster of genes. Conclusion: We show that the mutual information provides a systematic framework to assess the relationship between groups or clusters of genes and their functional annotations in a quantitative way. Within this framework, the mutual information allows us to address and incorporate several important issues, such as the interdependence of functional annotations and combinatorial combinations of attributes. It thus supplements and extends the conventional search for overrepresented attributes within a group or cluster of genes. In particular taking combinations of attributes into account, the mutual information opens the way to uncover specific functional descriptions of a group of genes or clustering result. All datasets and functional annotations used in this study are publicly available. All scripts used in the analysis are provided as additional files. Y1 - 2006 U6 - https://doi.org/10.1186/1471-2105-7-380 SN - 1471-2105 VL - 7 IS - 380 PB - BioMed Central CY - London ER - TY - CHAP A1 - Kiy, Alexander A1 - Hafer, Jörg A1 - Schumann, Marlen A1 - Enke, Uta ED - Lucke, Ulrike ED - Schwill, Andreas ED - Zender, Raphael T1 - Digitale Teilnehmerzertifikate und Open Badges verbinden BT - Der E-Teaching-Badge T2 - DeLFI 2016 - Die 14. E-Learning Fachtagung Informatik 11.-14. September 2016 Potsdam N2 - Während Qualifikationen und Kompetenzen, die auf informellem Wege erworben werden, immer mehr Beachtung finden, stellt sowohl deren Darstellung als auch die Anerkennung ein meist unüberwindbares Hindernis für Ausstellende und Erwerbende dar. Vermehrt wird unterdessen von klassisch papiergebundenen auf digitale Teilnahmezertifikate umgestellt, um den Nachweis von Kompetenz- und Qualifikationserwerb zu vereinfachen. In diesem Zusammenhang kann die Verbindung von digitalen Teilnahmezertifikaten und Open Badges einen Mehrwert für die öffentliche Darstellung und Verifikation bieten. KW - Teilnehmerzertifikate KW - Peer-Review KW - Open Badges KW - Open Badge Infrastructure KW - OBI Y1 - 2016 UR - http://subs.emis.de/LNI/Proceedings/Proceedings262/article21.html SN - 978-3-88579-656-5 IS - P-262 SP - 285 EP - 287 PB - Gesellschaft für Informatik CY - Bonn ER - TY - JOUR A1 - Sarsakov, Vladimir A1 - Schaub, Torsten H. A1 - Tompits, Hans A1 - Woltran, Stefan T1 - A compiler for nested logic programming Y1 - 2004 SN - 3-540- 20721-x ER - TY - JOUR A1 - Linke, Thomas A1 - Tompits, Hans A1 - Woltran, Stefan T1 - On Acyclic and head-cycle free nested logic programs Y1 - 2004 SN - 3-540-22671-01 ER - TY - JOUR A1 - Linke, Thomas A1 - Tompits, Hans A1 - Woltran, Stefan T1 - On acyclic and head-cycle free nested logic programs Y1 - 2004 ER - TY - JOUR A1 - Delgrande, James Patrick A1 - Schaub, Torsten H. A1 - Tompits, Hans A1 - Woltran, Stefan T1 - On Computing belief change operations using quantifield boolean formulas N2 - In this paper, we show how an approach to belief revision and belief contraction can be axiomatized by means of quantified Boolean formulas. Specifically, we consider the approach of belief change scenarios, a general framework that has been introduced for expressing different forms of belief change. The essential idea is that for a belief change scenario (K, R, C), the set of formulas K, representing the knowledge base, is modified so that the sets of formulas R and C are respectively true in, and consistent with the result. By restricting the form of a belief change scenario, one obtains specific belief change operators including belief revision, contraction, update, and merging. For both the general approach and for specific operators, we give a quantified Boolean formula such that satisfying truth assignments to the free variables correspond to belief change extensions in the original approach. Hence, we reduce the problem of determining the results of a belief change operation to that of satisfiability. This approach has several benefits. First, it furnishes an axiomatic specification of belief change with respect to belief change scenarios. This then leads to further insight into the belief change framework. Second, this axiomatization allows us to identify strict complexity bounds for the considered reasoning tasks. Third, we have implemented these different forms of belief change by means of existing solvers for quantified Boolean formulas. As well, it appears that this approach may be straightforwardly applied to other specific approaches to belief change Y1 - 2004 SN - 0955-792X ER - TY - JOUR A1 - Delgrande, James Patrick A1 - Schaub, Torsten H. A1 - Tompits, Hans A1 - Woltran, Stefan T1 - On computing solutions to belief change scenarios Y1 - 2001 SN - 3-540- 42464-4 ER -