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Helping Johnny to Analyze Malware: A Usability-Optimized Decompiler and Malware Analysis User Study
(2016)
Botnets
(2013)
Malware poses one of the major threats to all currently operated computer systems. The scale of the problem becomes obvious by looking at the global economic loss caused by different kinds of malware, which is estimated to be more than US$ 10 billion every year. Botnets, a special kind of malware, are used to reap economic gains by criminals as well as for politically motivated activities. In contrast to other kinds of malware, botnets utilize a hidden communication channel to receive commands from their operator and communicate their current status. The ability to execute almost arbitrary commands on the infected machines makes botnets a general-purpose tool to perform malicious cyber-activities. (Verlagsangaben)
This paper presents methods for the reduction and compression of meteorological data for web-based wind flow visualizations, which are tailored to the flow visualization technique. Flow data sets represent a large amount of data and are therefore not well suited for mobile networks with low data throughput rates and high latency. Using the mechanisms introduced in this paper, an efficient transfer of thinned out and compressed data can be achieved, while keeping the accuracy of the visualized information almost at the same quality level as for the original data.
This paper addresses the urgent need for international standardization of Context Metadata for e-Learning environments. In particular, E-Learning when distributed over the Internet, can synchronously and asynchronously reach a huge number of learners but also has to deal with a variety of different cultures and societies and the related complications. A lot of the differences strongly demand adaptation processes in which especially the contents are being modified to fit the needs in the targeted contexts. In our approach solving this task, we determined a list of around 160 significant possible differences and defined those as context metadata. In this paper, we show the results of our research regarding to the determination of context related influence factors as well as approaches to deal with them and present a first specification of the representing context-metadata.
Recent years have seen extensive adoption of domain generation algorithms (DGA) by modern botnets. The main goal is to generate a large number of domain names and then use a small subset for actual C&C communication. This makes DGAs very compelling for botmasters to harden the infrastructure of their botnets and make it resilient to blacklisting and attacks such as takedown efforts. While early DGAs were used as a backup communication mechanism, several new botnets use them as their primary communication method, making it extremely important to study DGAs in detail.
In this paper, we perform a comprehensive measurement study of the DGA landscape by analyzing 43 DGAbased malware families and variants. We also present a taxonomy for DGAs and use it to characterize and compare the properties of the studied families. By reimplementing the algorithms, we pre-compute all possible domains they generate, covering the majority of known and active DGAs. Then, we study the registration status of over 18 million DGA domains and show that corresponding malware families and related campaigns can be reliably identified by pre-computing future DGA domains. We also give insights into botmasters’ strategies regarding domain registration and identify several pitfalls in previous takedown efforts of DGA-based botnets. We will share the dataset for future research and will also provide a web service to check domains for potential DGA identity.
Most people use disaster apps infrequently, primarily only in situations of turmoil, when they are physically or emotionally vulnerable. Personal data may be necessary to help them, data protections may be waived. In some circumstances, free movement and liberties may be curtailed for public protection, as was seen in the current COVID pandemic. Consuming and producing disaster data can deepen problems arising at the confluence of surveillance and disaster capitalism, where data has become a tool for solutionist instrumentarian power (Zuboff 2019, Klein 2008) and part of a destructive mode of one world worlding (Law 2015, Escobar 2020). The special use of disaster apps prompts us to ask what role consumer protection could play in safeguarding democratic liberties. Within this work, a set of current approaches are briefly reviewed and two case studies are presented of what we call appropriation or design against datafication. These combine document analysis and literature research with several months of online and field ethnographic observation. The first case study examines disaster app use in response to the 2010 Haiti earthquake, the second explores COVID Contact Tracing in Taiwan in 2020/21. Against this backdrop we ask, ‘how could and how should consumer protection respond to problems of surveillance disaster capitalism?’ Drawing on our work with the is IT ethical? Exchange, a co-designed community platform and knowledge exchange for disaster information sharing, and a Societal Readiness Assessment Framework that we are developing alongside it, we explore how co-design methodologies could help define answers.
In this paper, we present a solution how to test cultural influences on E-Learning in a global context. Based on a metadata approach, we show how specifically cultural influence factors can be determined to transfer and adapt learning environments. We present a method how those influence factors can be validated for both, to improve the dynamical meta-data specification and to be used in the development of (international) E-Learning scenarios.