@article{PrasseKnaebelMachlicaetal.2019, author = {Prasse, Paul and Knaebel, Rene and Machlica, Lukas and Pevny, Tomas and Scheffer, Tobias}, title = {Joint detection of malicious domains and infected clients}, series = {Machine learning}, volume = {108}, journal = {Machine learning}, number = {8-9}, publisher = {Springer}, address = {Dordrecht}, issn = {0885-6125}, doi = {10.1007/s10994-019-05789-z}, pages = {1353 -- 1368}, year = {2019}, abstract = {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.}, language = {en} } @article{CabalarFandinnoSchaubetal.2019, author = {Cabalar, Pedro and Fandinno, Jorge and Schaub, Torsten H. and Schellhorn, Sebastian}, title = {Gelfond-Zhang aggregates as propositional formulas}, series = {Artificial intelligence}, volume = {274}, journal = {Artificial intelligence}, publisher = {Elsevier}, address = {Amsterdam}, issn = {0004-3702}, doi = {10.1016/j.artint.2018.10.007}, pages = {26 -- 43}, year = {2019}, abstract = {Answer Set Programming (ASP) has become a popular and widespread paradigm for practical Knowledge Representation thanks to its expressiveness and the available enhancements of its input language. One of such enhancements is the use of aggregates, for which different semantic proposals have been made. In this paper, we show that any ASP aggregate interpreted under Gelfond and Zhang's (GZ) semantics can be replaced (under strong equivalence) by a propositional formula. Restricted to the original GZ syntax, the resulting formula is reducible to a disjunction of conjunctions of literals but the formulation is still applicable even when the syntax is extended to allow for arbitrary formulas (including nested aggregates) in the condition. Once GZ-aggregates are represented as formulas, we establish a formal comparison (in terms of the logic of Here-and-There) to Ferraris' (F) aggregates, which are defined by a different formula translation involving nested implications. In particular, we prove that if we replace an F-aggregate by a GZ-aggregate in a rule head, we do not lose answer sets (although more can be gained). This extends the previously known result that the opposite happens in rule bodies, i.e., replacing a GZ-aggregate by an F-aggregate in the body may yield more answer sets. Finally, we characterize a class of aggregates for which GZ- and F-semantics coincide.}, language = {en} } @phdthesis{Ashouri2020, author = {Ashouri, Mohammadreza}, title = {TrainTrap}, school = {Universit{\"a}t Potsdam}, pages = {XIX, 103}, year = {2020}, language = {en} } @article{AguadoCabalarFandinoetal.2019, author = {Aguado, Felicidad and Cabalar, Pedro and Fandi{\~n}o, Jorge and Pearce, David and Perez, Gilberto and Vidal-Peracho, Concepcion}, title = {Revisiting Explicit Negation in Answer Set Programming}, series = {Theory and practice of logic programming}, volume = {19}, journal = {Theory and practice of logic programming}, number = {5-6}, publisher = {Cambridge Univ. Press}, address = {New York}, issn = {1471-0684}, doi = {10.1017/S1471068419000267}, pages = {908 -- 924}, year = {2019}, language = {en} } @article{LaskovGehlKruegeretal.2006, author = {Laskov, Pavel and Gehl, Christian and Kr{\"u}ger, Stefan and M{\"u}ller, Klaus-Robert}, title = {Incremental support vector learning: analysis, implementation and applications}, series = {Journal of machine learning research}, volume = {7}, journal = {Journal of machine learning research}, publisher = {MIT Press}, address = {Cambridge, Mass.}, issn = {1532-4435}, pages = {1909 -- 1936}, year = {2006}, abstract = {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.}, language = {en} } @article{SteuerHumburgSelbig2006, author = {Steuer, Ralf and Humburg, Peter and Selbig, Joachim}, title = {Validation and functional annotation of expression-based clusters based on gene ontology}, series = {BMC bioinformatics}, volume = {7}, journal = {BMC bioinformatics}, number = {380}, publisher = {BioMed Central}, address = {London}, issn = {1471-2105}, doi = {10.1186/1471-2105-7-380}, pages = {12}, year = {2006}, abstract = {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.}, language = {en} } @article{SarsakovSchaubTompitsetal.2004, author = {Sarsakov, Vladimir and Schaub, Torsten H. and Tompits, Hans and Woltran, Stefan}, title = {A compiler for nested logic programming}, isbn = {3-540- 20721-x}, year = {2004}, language = {en} } @article{LinkeTompitsWoltran2004, author = {Linke, Thomas and Tompits, Hans and Woltran, Stefan}, title = {On Acyclic and head-cycle free nested logic programs}, isbn = {3-540-22671-01}, year = {2004}, language = {en} } @article{LinkeTompitsWoltran2004, author = {Linke, Thomas and Tompits, Hans and Woltran, Stefan}, title = {On acyclic and head-cycle free nested logic programs}, year = {2004}, language = {en} } @article{DelgrandeSchaubTompitsetal.2004, author = {Delgrande, James Patrick and Schaub, Torsten H. and Tompits, Hans and Woltran, Stefan}, title = {On Computing belief change operations using quantifield boolean formulas}, issn = {0955-792X}, year = {2004}, abstract = {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}, language = {en} }