TY - JOUR A1 - Koch, Christian A1 - Katharina, Müller A1 - Eldar, Sultanow T1 - Which programming languages do hackers use? BT - A survey at the German Chaos Computer Club N2 - There are numerous articles about the programming languages most commonly used by hackers. Among them, however, there are hardly any scientific studies. One reason might be that hackers mainly operate anonymously and are difficult to reach. This paper aims to shed light on this interesting and relevant research question. In order to find answers, we conducted a survey among the members of the German Chaos Computer Club. As one of the world's largest organisations for information security and hacking, the club provides a good basis for our study. We examine the question of which programming languages are used by hackers as well as the importance of the programming language for their work. The paper offers first insights into the topic and can provide a starting point for further research. KW - Cybersecurity KW - Hacking KW - Programming Language KW - Chaos Computer Club KW - Computersicherheit KW - Programmiersprache KW - Chaos Computer Club Y1 - 2022 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:92-opus4-9237 SP - 3 EP - 13 PB - arXiv ER - TY - JOUR A1 - Koch, Christian T1 - Which Programming Languages Do Hackers Use? BT - Analyzing the Exploit Database with Python JF - Towards Data Science N2 - In 2021, together with fellow scientists, we conducted a survey at the German Chaos Computer Club (CCC). Our goal was to find out which programming languages are most commonly used by hackers. This article follows up the survey and compares its findings with an analysis of the Exploit Database. Readers get step-by-step instructions on how to set up the analysis environment and a summary of the results. Thus, the article not only covers applied techniques, but also offers insights into the world of cybersecurity. KW - Cybersecurity KW - Cybersecurity KW - Data Science KW - Hacking KW - Python Y1 - 2023 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:92-opus4-9214 UR - https://towardsdatascience.com/which-programming-languages-do-hackers-use-ac3ed9d3e8f8 PB - Medium ER - TY - GEN A1 - Koch, Christian A1 - Riehle, Dirk T1 - Schema Inference with Explainable AI for Data Engineering in Government Institutions BT - Systematic Review Protocol N2 - Data engineering is an integral part of the data science process. It comprises tasks such as data ingestion, data transformation, and data quality assurance. In order to fulfill these tasks, schema inference is an important capability. Its goal is to detect the structure of a dataset and to derive metadata on hierarchies, data types, etc. Artificial intelligence (AI) has the potential to automate schema inference and thus increase the efficiency of the data science process. However, as government institutions are subject to special regulations, explainability of AI models can be a mandatory requirement. Goal of this research protocol is to plan a systematic review of literature on schema inference with explainable AI (XAI) for data engineering in government institutions. KW - Explainable Artificial Intelligence KW - Explainable Artificial Intelligence KW - Schema Inference KW - Data Engineering KW - Data Science KW - Government Y1 - 2023 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:92-opus4-11609 SP - 2 EP - 6 ER - TY - CHAP A1 - Dotenco, Sergiu A1 - Götzelmann, Timo A1 - Gallwitz, Florian T1 - Smartphone Input Using an Integrated Projector and a Monocular Camera T2 - Lecture Notes in Computer Science N2 - Touch input on modern smartphones can be tedious, especially if the touchscreen is small. Smartphones with integrated projectors can be used to overcome this limitation by projecting the screen contents onto a surface, allowing the user to interact with the projection by means of simple hand gestures. In this work, we propose a novel approach for projector smartphones that allows the user to remotely interact with the smartphone screen via its projection. We detect user's interaction using the built-in camera, and forward detected hand gestures as touch input events to the operating system. In order to avoid costly computations, we additionally use built-in motion sensors. We verify the proposed method using an implementation for the consumer smartphone Samsung Galaxy Beam equipped with a deflection mirror. KW - Mobile computing KW - Touch input KW - Projector KW - Smartphone KW - Limited input space KW - App KW - Programmierung KW - Projektionsapparat Y1 - 2014 UR - http://link.springer.com/chapter/10.1007/978-3-319-07227-2_13 SN - 978-3-319-07226-5 VL - Volume 8512 PB - Springer ER - TY - CHAP A1 - Schäff, Christian A1 - Pugliese, Gaston A1 - Götzelmann, Timo T1 - Behavior Based Web User Identification T2 - GI-Edition / Seminars N2 - This paper examines different approaches for the identification of users by their personal behavior and discusses techniques which could be used in the context of websites. Such web tracking approaches have the potential to identify users even if they use multiple or shared devices. For web pages mouse and touch input are widely used. Therefore, we propose a survey to evaluate the feasibility to identify users by their interaction behavior. KW - User identification KW - Web tracking KW - User input KW - Interaction KW - Biometric KW - Authentifikation KW - Web-Seite KW - Mensch-Maschine-Kommunikation Y1 - 2014 SN - 978-3-88579-447-9 SN - 1614-3213 N1 - Volltext verfügbar in der Zentralbibliothek in: Gesellschaft für Informatik GI-Edition: Informatiktage 2014. ISBN 978-3-88579-447-9. Signatur: 001/SQ 1200-2014+1 VL - Volume S-13 PB - KöllenDruck+Verlag CY - Bonn ER - TY - RPRT A1 - Koch, Christian A1 - Stadi, Markus T1 - Why Retraining Can Be Harder Than Training BT - A neural network perspective on learning, unlearning and relearning T2 - Towards Data Science N2 - In a rapidly changing world, humans are required to quickly adapt to a new environment. Neural networks show why this is easier said than done. Our article uses a perceptron to demonstrate why unlearning and relearning can be costlier than learning from scratch. KW - Künstliche Intelligenz KW - maschinelles Lernen KW - neuronales Netz Y1 - 2024 UR - https://towardsdatascience.com/why-retraining-can-be-harder-than-training-489b3bc6ae02 SP - ohne Zählung PB - Medium ER - TY - GEN A1 - Koch, Christian A1 - Kogelheide, Lucie A1 - Lorenz, Raphael T1 - Post-Quantum Cryptography with Python and Linux BT - A beginner’s guide T2 - Towards Data Science N2 - If we believe Edward Snowden, encryption is "the only true protection against surveillance". However, advances in quantum technology might endanger this safeguard. Our article discusses why quantum computing poses a threat to data security and what to do about it. Instead of a purely theoretical analysis, we build on code examples using Python, C, and Linux. KW - Computersicherheit KW - Cybersecurity KW - Encryption KW - Quantum Computing KW - Python KW - Linux Y1 - 2023 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:92-opus4-10814 UR - https://medium.com/towards-data-science/post-quantum-cryptography-with-python-and-linux-17b1ca1b3e1 PB - Medium ER - TY - GEN A1 - Koch, Christian A1 - Stadi, Markus A1 - Berle, Lukas T1 - From Data Engineering to Prompt Engineering BT - Solving data preparation tasks with ChatGPT T2 - Towards Data Science N2 - Data engineering makes up a large part of the data science process. In CRISP-DM this process stage is called "data preparation". It comprises tasks such as data ingestion, data transformation and data quality assurance. In our article we solve typical data engineering tasks using ChatGPT and Python. By doing so, we explore the link between data engineering and the new discipline of prompt engineering. KW - Prompt Engineering KW - Data Engineering KW - ChatGPT KW - Large Language Model KW - Python KW - Künstliche Intelligenz KW - Natürlichsprachiges System Y1 - 2023 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:92-opus4-10829 UR - https://medium.com/towards-data-science/from-data-engineering-to-prompt-engineering-5debd1c636e0 PB - Medium ER - TY - GEN A1 - Koch, Christian A1 - Riehle, Dirk A1 - Müller, Katharina T1 - Schema Inference for Tabular Data with Explainable Artificial Intelligence BT - Systematic Review Protocol - Third Version N2 - Data engineering is an integral part of the data science process. It comprises tasks such as data ingestion, data transformation, and data quality assurance. In order to fulfill these tasks, schema inference is an important capability. Its goal is to detect the structure of a dataset and to derive metadata on hierarchies, data types, etc. Artificial intelligence (AI) has the potential to automate schema inference and thus increase the efficiency of the data science process. However, as government institutions are subject to special regulations, explainability of AI models can be a mandatory requirement. Goal of this research protocol is to plan a systematic review of literature on schema inference for tabular data with explainable AI (XAI). This third version was derived from two earlier review protocols. KW - Explainable Artificial Intelligence KW - Data Engineering KW - Data Science KW - Explainable Artificial Intelligence KW - Government KW - Schema Inference Y1 - 2024 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:92-opus4-15601 SP - 2 EP - 6 ER -