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(2016)
Erneuertes Gestern?
(2016)
Tarkovskijs Scham
(2016)
Der Jüdische Friedhof in Potsdam ist der einzige authentische Gedächtnisort, der vom Lebenszyklus der jüdischen Bevölkerung in der ehemaligen preußischen Residenz- und Garnisonstadt zeugt. Er ist zudem Ausdruck des unterschiedlichen Umgangs der Nachgeborenen mit ihrem Kulturgut. Außerdem ist dieser Jüdische Friedhof zurzeit als einziger in Deutschland durch die UNESCO als Welterbe anerkannt.
Da die jüdische Geschichte Potsdams bislang nur wenig bekannt ist, entstand ein durch die Stiftung „Erinnerung, Verantwortung und Zukunft“ gefördertes Projekt, in dem sich Schüler*innen des Potsdamer Humboldt-Gymnasiums im Rahmen eines Seminarkurses mit dem jüdischen Erbe ihrer Stadt intensiv auseinandersetzten. Neben einer Annäherung an das Thema über verschiedene, den Friedhof betreffende Sachthemen beschäftigten sich die Jugendlichen mit einzelnen jüdischen Potsdamern, ihren Familienschicksalen und Lebenskonzepten. Ergänzend wurden Aspekte des religiösen Verständnisses von Tod und Trauer im Judentum vorgestellt.
Die Ergebnisse all dieser Ausarbeitungen sind im vorliegenden Lehrmaterial vereinigt und dienen als Anregung für Lehrende und Lernende, die jüdische Geschichte ihrer jeweiligen Heimatorte zu thematisieren.
Computer Security deals with the detection and mitigation of threats to computer networks, data, and computing hardware. This
thesis addresses the following two computer security problems: email spam campaign and malware detection.
Email spam campaigns can easily be generated using popular dissemination tools by specifying simple grammars that serve as message templates. A grammar is disseminated to nodes of a bot net, the nodes create messages by instantiating the grammar at random. Email spam campaigns can encompass huge data volumes and therefore pose a threat to the stability of the infrastructure of email service providers that have to store them. Malware -software that serves a malicious purpose- is affecting web servers, client computers via active content, and client computers through executable files. Without the help of malware detection systems it would be easy for malware creators to collect sensitive information or to infiltrate computers.
The detection of threats -such as email-spam messages, phishing messages, or malware- is an adversarial and therefore intrinsically
difficult problem. Threats vary greatly and evolve over time. The detection of threats based on manually-designed rules is therefore
difficult and requires a constant engineering effort. Machine-learning is a research area that revolves around the analysis of data and the discovery of patterns that describe aspects of the data. Discriminative learning methods extract prediction models from data that are optimized to predict a target attribute as accurately as possible. Machine-learning methods hold the promise of automatically identifying patterns that robustly and accurately detect threats. This thesis focuses on the design and analysis of discriminative learning methods for the two computer-security problems under investigation: email-campaign and malware detection.
The first part of this thesis addresses email-campaign detection. We focus on regular expressions as a syntactic framework, because regular expressions are intuitively comprehensible by security engineers and administrators, and they can be applied as a detection mechanism in an extremely efficient manner. In this setting, a prediction model is provided with exemplary messages from an email-spam campaign. The prediction model has to generate a regular expression that reveals the syntactic pattern that underlies the entire campaign, and that a security engineers finds comprehensible and feels confident enough to use the expression to blacklist further messages at the email server. We model this problem as two-stage learning problem with structured input and output spaces which can be solved using standard cutting plane methods. Therefore we develop an appropriate loss function, and derive a decoder for the resulting optimization problem.
The second part of this thesis deals with the problem of predicting whether a given JavaScript or PHP file is malicious or benign. Recent malware analysis techniques use static or dynamic features, or both. In fully dynamic analysis, the software or script is executed and observed for malicious behavior in a sandbox environment. By contrast, static analysis is based on features that can be extracted directly from the program file. In order to bypass static detection mechanisms, code obfuscation techniques are used to spread a malicious program file in many different syntactic variants. Deobfuscating the code before applying a static classifier can be subjected to mostly static code analysis and can overcome the problem of obfuscated malicious code, but on the other hand increases the computational costs of malware detection by an order of magnitude. In this thesis we present a cascaded architecture in which a classifier first performs a static analysis of the original code and -based on the outcome of this first classification step- the code may be deobfuscated and classified again. We explore several types of features including token $n$-grams, orthogonal sparse bigrams, subroutine-hashings, and syntax-tree features and study the robustness of detection methods and feature types against the evolution of malware over time. The developed tool scans very large file collections quickly and accurately.
Each model is evaluated on real-world data and compared to reference methods. Our approach of inferring regular expressions to filter emails belonging to an email spam campaigns leads to models with a high true-positive rate at a very low false-positive rate that is an order of magnitude lower than that of a commercial content-based filter. Our presented system -REx-SVMshort- is being used by a commercial email service provider and complements content-based and IP-address based filtering.
Our cascaded malware detection system is evaluated on a high-quality data set of almost 400,000 conspicuous PHP files and a collection of more than 1,00,000 JavaScript files. From our case study we can conclude that our system can quickly and accurately process large data collections at a low false-positive rate.