@article{49459, author = {{Masuch, K. and Greve, M. and Trang, Simon Thanh-Nam}}, journal = {{Computers & Security}}, title = {{{Apologize or Justify? Examining the Impact of Data Breach Response Actions on Stock Value of Affected Companies}}}, volume = {{12}}, year = {{2022}}, } @inbook{54585, author = {{Manzoor, Ali and Saleem, Muhammad and Ngonga Ngomo, Axel-Cyrille}}, booktitle = {{The Semantic Web – ISWC 2022}}, isbn = {{9783031194320}}, issn = {{0302-9743}}, publisher = {{Springer International Publishing}}, title = {{{REBench: Microbenchmarking Framework for Relation Extraction Systems}}}, doi = {{10.1007/978-3-031-19433-7_37}}, year = {{2022}}, } @article{45849, abstract = {{AbstractDependence Logic was introduced by Jouko Väänänen in 2007. We study a propositional variant of this logic(PDL)and investigate a variety of parameterisations with respect to central decision problems. The model checking problem (MC) ofPDLisNP-complete (Ebbing and Lohmann, SOFSEM 2012). The subject of this research is to identify a list of parameterisations (formula-size, formula-depth, treewidth, team-size, number of variables) under which MC becomes fixed-parameter tractable. Furthermore, we show that the number of disjunctions or the arity of dependence atoms (dep-arity) as a parameter both yield a paraNP-completeness result. Then, we consider the satisfiability problem (SAT) which classically is known to beNP-complete as well (Lohmann and Vollmer, Studia Logica 2013). There we are presenting a different picture: under team-size, or dep-arity SAT isparaNP-complete whereas under all other mentioned parameters the problem isFPT. Finally, we introduce a variant of the satisfiability problem, asking for a team of a given size, and show for this problem an almost complete picture.}}, author = {{Mahmood, Yasir and Meier, Arne}}, issn = {{1012-2443}}, journal = {{Annals of Mathematics and Artificial Intelligence}}, keywords = {{Applied Mathematics, Artificial Intelligence}}, number = {{2-3}}, pages = {{271--296}}, publisher = {{Springer Science and Business Media LLC}}, title = {{{Parameterised complexity of model checking and satisfiability in propositional dependence logic}}}, doi = {{10.1007/s10472-021-09730-w}}, volume = {{90}}, year = {{2022}}, } @article{45847, abstract = {{Abstract In this paper, we investigate the parameterized complexity of model checking for Dependence and Independence logic, which are well studied logics in the area of Team Semantics. We start with a list of nine immediate parameterizations for this problem, namely the number of disjunctions (i.e. splits)/(free) variables/universal quantifiers, formula-size, the tree-width of the Gaifman graph of the input structure, the size of the universe/team and the arity of dependence atoms. We present a comprehensive picture of the parameterized complexity of model checking and obtain a division of the problem into tractable and various intractable degrees. Furthermore, we also consider the complexity of the most important variants (data and expression complexity) of the model checking problem by fixing parts of the input.}}, author = {{Kontinen, Juha and Meier, Arne and Mahmood, Yasir}}, issn = {{0955-792X}}, journal = {{Journal of Logic and Computation}}, keywords = {{Logic, Hardware and Architecture, Arts and Humanities (miscellaneous), Software, Theoretical Computer Science}}, number = {{8}}, pages = {{1624--1644}}, publisher = {{Oxford University Press (OUP)}}, title = {{{A parameterized view on the complexity of dependence and independence logic}}}, doi = {{10.1093/logcom/exac070}}, volume = {{32}}, year = {{2022}}, } @inproceedings{45846, author = {{Kontinen, Juha and Meier, Arne and Mahmood, Yasir}}, booktitle = {{Logical Foundations of Computer Science}}, isbn = {{9783030930998}}, issn = {{0302-9743}}, publisher = {{Springer International Publishing}}, title = {{{A Parameterized View on the Complexity of Dependence Logic}}}, doi = {{10.1007/978-3-030-93100-1_9}}, year = {{2022}}, } @inproceedings{47289, author = {{Huaman, Nicolas and Krause, Alexander and Wermke, Dominik and Klemmer, Jan H. and Stransky, Christian and Acar, Yasemin and Fahl, Sascha}}, booktitle = {{Eighteenth Symposium on Usable Privacy and Security, SOUPS 2022, Boston, MA, USA, August 7-9, 2022}}, editor = {{Chiasson, Sonia and Kapadia, Apu}}, pages = {{313–330}}, publisher = {{USENIX Association}}, title = {{{If You Can’t Get Them to the Lab: Evaluating a Virtual Study Environment with Security Information Workers}}}, year = {{2022}}, } @inproceedings{47844, author = {{Jancar, Jan and Fourné, Marcel and Braga, Daniel De Almeida and Sabt, Mohamed and Schwabe, Peter and Barthe, Gilles and Fouque, Pierre-Alain and Acar, Yasemin}}, booktitle = {{2022 IEEE Symposium on Security and Privacy (SP)}}, publisher = {{IEEE}}, title = {{{“They’re not that hard to mitigate”: What Cryptographic Library Developers Think About Timing Attacks}}}, doi = {{10.1109/sp46214.2022.9833713}}, year = {{2022}}, } @inproceedings{47286, author = {{Gutfleisch, Marco and Klemmer, Jan H. and Busch, Niklas and Acar, Yasemin and Sasse, M. Angela and Fahl, Sascha}}, booktitle = {{43rd IEEE Symposium on Security and Privacy, SP 2022, San Francisco, CA, USA, May 22-26, 2022}}, pages = {{893–910}}, publisher = {{IEEE}}, title = {{{How Does Usable Security (Not) End Up in Software Products? Results From a Qualitative Interview Study}}}, doi = {{10.1109/SP46214.2022.9833756}}, year = {{2022}}, } @inproceedings{47287, author = {{Stransky, Christian and Wiese, Oliver and Roth, Volker and Acar, Yasemin and Fahl, Sascha}}, booktitle = {{43rd IEEE Symposium on Security and Privacy, SP 2022, San Francisco, CA, USA, May 22-26, 2022}}, pages = {{860–875}}, publisher = {{IEEE}}, title = {{{27 Years and 81 Million Opportunities Later: Investigating the Use of Email Encryption for an Entire University}}}, doi = {{10.1109/SP46214.2022.9833755}}, year = {{2022}}, } @inproceedings{47283, author = {{Kaur, Harjot and Amft, Sabrina and Votipka, Daniel and Acar, Yasemin and Fahl, Sascha}}, booktitle = {{31st USENIX Security Symposium, USENIX Security 2022, Boston, MA, USA, August 10-12, 2022}}, editor = {{Butler, Kevin R. B. and Thomas, Kurt}}, pages = {{4041–4058}}, publisher = {{USENIX Association}}, title = {{{Where to Recruit for Security Development Studies: Comparing Six Software Developer Samples}}}, year = {{2022}}, } @article{47290, author = {{Huaman, Nicolas and Amft, Sabrina and Oltrogge, Marten and Acar, Yasemin and Fahl, Sascha}}, journal = {{IEEE Secur. Priv.}}, number = {{2}}, pages = {{49–60}}, title = {{{They Would Do Better If They Worked Together: Interaction Problems Between Password Managers and the Web}}}, doi = {{10.1109/MSEC.2021.3123795}}, volume = {{20}}, year = {{2022}}, } @inproceedings{47843, author = {{Wermke, Dominik and Wohler, Noah and Klemmer, Jan H. and Fourné, Marcel and Acar, Yasemin and Fahl, Sascha}}, booktitle = {{2022 IEEE Symposium on Security and Privacy (SP)}}, publisher = {{IEEE}}, title = {{{Committed to Trust: A Qualitative Study on Security & Trust in Open Source Software Projects}}}, doi = {{10.1109/sp46214.2022.9833686}}, year = {{2022}}, } @inproceedings{47288, author = {{Jancar, Jan and Fourné, Marcel and Braga, Daniel De Almeida and Sabt, Mohamed and Schwabe, Peter and Barthe, Gilles and Fouque, Pierre-Alain and Acar, Yasemin}}, booktitle = {{43rd IEEE Symposium on Security and Privacy, SP 2022, San Francisco, CA, USA, May 22-26, 2022}}, pages = {{632–649}}, publisher = {{IEEE}}, title = {{{"They’re not that hard to mitigate": What Cryptographic Library Developers Think About Timing Attacks}}}, doi = {{10.1109/SP46214.2022.9833713}}, year = {{2022}}, } @inproceedings{47285, author = {{Wermke, Dominik and Wöhler, Noah and Klemmer, Jan H. and Fourné, Marcel and Acar, Yasemin and Fahl, Sascha}}, booktitle = {{43rd IEEE Symposium on Security and Privacy, SP 2022, San Francisco, CA, USA, May 22-26, 2022}}, pages = {{1880–1896}}, publisher = {{IEEE}}, title = {{{Committed to Trust: A Qualitative Study on Security & Trust in Open Source Software Projects}}}, doi = {{10.1109/SP46214.2022.9833686}}, year = {{2022}}, } @inproceedings{47284, author = {{Munyendo, Collins W. and Acar, Yasemin and Aviv, Adam J.}}, booktitle = {{43rd IEEE Symposium on Security and Privacy, SP 2022, San Francisco, CA, USA, May 22-26, 2022}}, pages = {{2304–2319}}, publisher = {{IEEE}}, title = {{{"Desperate Times Call for Desperate Measures": User Concerns with Mobile Loan Apps in Kenya}}}, doi = {{10.1109/SP46214.2022.9833779}}, year = {{2022}}, } @article{47281, author = {{Krause, Alexander and Klemmer, Jan H. and Huaman, Nicolas and Wermke, Dominik and Acar, Yasemin and Fahl, Sascha}}, journal = {{CoRR}}, title = {{{Committed by Accident: Studying Prevention and Remediation Strategies Against Secret Leakage in Source Code Repositories}}}, doi = {{10.48550/arXiv.2211.06213}}, volume = {{abs/2211.06213}}, year = {{2022}}, } @inproceedings{46307, abstract = {{Exploratory Landscape Analysis is a powerful technique for numerically characterizing landscapes of single-objective continuous optimization problems. Landscape insights are crucial both for problem understanding as well as for assessing benchmark set diversity and composition. Despite the irrefutable usefulness of these features, they suffer from their own ailments and downsides. Hence, in this work we provide a collection of different approaches to characterize optimization landscapes. Similar to conventional landscape features, we require a small initial sample. However, instead of computing features based on that sample, we develop alternative representations of the original sample. These range from point clouds to 2D images and, therefore, are entirely feature-free. We demonstrate and validate our devised methods on the BBOB testbed and predict, with the help of Deep Learning, the high-level, expert-based landscape properties such as the degree of multimodality and the existence of funnel structures. The quality of our approaches is on par with methods relying on the traditional landscape features. Thereby, we provide an exciting new perspective on every research area which utilizes problem information such as problem understanding and algorithm design as well as automated algorithm configuration and selection.}}, author = {{Seiler, Moritz and Prager, Raphael Patrick and Kerschke, Pascal and Trautmann, Heike}}, booktitle = {{Proceedings of the Genetic and Evolutionary Computation Conference}}, isbn = {{9781450392372}}, pages = {{657–665}}, publisher = {{Association for Computing Machinery}}, title = {{{A Collection of Deep Learning-based Feature-Free Approaches for Characterizing Single-Objective Continuous Fitness Landscapes}}}, doi = {{10.1145/3512290.3528834}}, year = {{2022}}, } @inproceedings{46304, abstract = {{In recent years, feature-based automated algorithm selection using exploratory landscape analysis has demonstrated its great potential in single-objective continuous black-box optimization. However, feature computation is problem-specific and can be costly in terms of computational resources. This paper investigates feature-free approaches that rely on state-of-the-art deep learning techniques operating on either images or point clouds. We show that point-cloud-based strategies, in particular, are highly competitive and also substantially reduce the size of the required solver portfolio. Moreover, we highlight the effect and importance of cost-sensitive learning in automated algorithm selection models.}}, author = {{Prager, Raphael Patrick and Seiler, Moritz and Trautmann, Heike and Kerschke, Pascal}}, booktitle = {{Parallel Problem Solving from Nature — PPSN XVII}}, editor = {{Rudolph, Günter and Kononova, Anna V. and Aguirre, Hernán and Kerschke, Pascal and Ochoa, Gabriela and Tušar, Tea}}, isbn = {{978-3-031-14714-2}}, pages = {{3–17}}, publisher = {{Springer International Publishing}}, title = {{{Automated Algorithm Selection in Single-Objective Continuous Optimization: A Comparative Study of Deep Learning and Landscape Analysis Methods}}}, doi = {{10.1007/978-3-031-14714-2_1}}, year = {{2022}}, } @inproceedings{46303, abstract = {{Social media platforms are essential for information sharing and, thus, prone to coordinated dis- and misinformation campaigns. Nevertheless, research in this area is hampered by strict data sharing regulations imposed by the platforms, resulting in a lack of benchmark data. Previous work focused on circumventing these rules by either pseudonymizing the data or sharing fragments. In this work, we will address the benchmarking crisis by presenting a methodology that can be used to create artificial campaigns out of original campaign building blocks. We conduct a proof-of-concept study using the freely available generative language model GPT-Neo in this context and demonstrate that the campaign patterns can flexibly be adapted to an underlying social media stream and evade state-of-the-art campaign detection approaches based on stream clustering. Thus, we not only provide a framework for artificial benchmark generation but also demonstrate the possible adversarial nature of such benchmarks for challenging and advancing current campaign detection methods.}}, author = {{Pohl, Janina Susanne and Assenmacher, Dennis and Seiler, Moritz and Trautmann, Heike and Grimme, Christian}}, booktitle = {{Workshop Proceedings of the 16$^th$ International Conference on Web and Social Media (ICWSM)}}, editor = {{the Advancement of Artificial Intelligence (AAAI) Association, for}}, pages = {{1–10}}, publisher = {{AAAI Press}}, title = {{{Artificial Social Media Campaign Creation for Benchmarking and Challenging Detection Approaches}}}, doi = {{10.36190/2022.91}}, year = {{2022}}, } @article{46309, abstract = {{Due to the rise of continuous data-generating applications, analyzing data streams has gained increasing attention over the past decades. A core research area in stream data is stream classification, which categorizes or detects data points within an evolving stream of observations. Areas of stream classification are diverse—ranging, e.g., from monitoring sensor data to analyzing a wide range of (social) media applications. Research in stream classification is related to developing methods that adapt to the changing and potentially volatile data stream. It focuses on individual aspects of the stream classification pipeline, e.g., designing suitable algorithm architectures, an efficient train and test procedure, or detecting so-called concept drifts. As a result of the many different research questions and strands, the field is challenging to grasp, especially for beginners. This survey explores, summarizes, and categorizes work within the domain of stream classification and identifies core research threads over the past few years. It is structured based on the stream classification process to facilitate coordination within this complex topic, including common application scenarios and benchmarking data sets. Thus, both newcomers to the field and experts who want to widen their scope can gain (additional) insight into this research area and find starting points and pointers to more in-depth literature on specific issues and research directions in the field.}}, author = {{Clever, Lena and Pohl, Janina Susanne and Bossek, Jakob and Kerschke, Pascal and Trautmann, Heike}}, journal = {{Applied Sciences}}, number = {{8}}, pages = {{1–44}}, title = {{{Process-Oriented Stream Classification Pipeline: A Literature Review}}}, doi = {{10.3390/app12189094}}, volume = {{12}}, year = {{2022}}, }