Helmholtz Center for Information Security
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How well do you blend into the crowd? - d-convergence: A novel paradigm for quantifying privacy in the age of Big-Data
No full textIn the Compression Hornet's Nest: A Security Study of Data Compression in Network Services
Get PDFIn this paper, we investigate the current use of data compression in network services that are at the core of modern web-based applications. While compression reduces network traffic, if not properly implemented it may make an application vulnerable to DoS attacks. Despite the popularity of similar attacks in the past, such as zip bombs or XML bombs, current protocol specifications and design patterns indicate that developers are still mostly unaware of the proper way to handle compressed streams in protocols and web applications. In this paper, we show that denial of services due to improper handling of data compression is a persistent and widespread threat. In our experiments, we review three popular communication protocols and test 19 implementations against highly-compressed protocol messages. Based on the results of our analysis, we list 12 common pitfalls that we observed at the implementation, specification, and configuration levels. Additionally, we discuss a number of previously unknown resource exhaustion vulnerabilities that can be exploited to mount DoS attacks against popular network service implementations
Mining Apps for Abnormal Usage of Sensitive Data
No full textWhat is it that makes an app malicious? One important factor is that malicious apps treat sensitive data differently from benign apps. To capture such differences, we mined 2,866 benign Android applications for their data flow from sensitive sources, and compare these flows against those found in malicious apps. We find that (a) for every sensitive source, the data ends up in a small number of typical sinks; (b) these sinks differ considerably between benign and malicious apps; (c) these differences can be used to flag malicious apps due to their abnormal data flow; and (d) malicious apps can be identified by their abnormal data flow alone, without requiring known malware samples. In our evaluation, our mudflow prototype correctly identified 86.4% of all novel malware, and 90.1% of novel malware leaking sensitive data
