1,478 research outputs found
NEMESYS: Enhanced Network Security for Seamless Service Provisioning in the Smart Mobile Ecosystem
As a consequence of the growing popularity of smart mobile devices, mobile
malware is clearly on the rise, with attackers targeting valuable user
information and exploiting vulnerabilities of the mobile ecosystems. With the
emergence of large-scale mobile botnets, smartphones can also be used to launch
attacks on mobile networks. The NEMESYS project will develop novel security
technologies for seamless service provisioning in the smart mobile ecosystem,
and improve mobile network security through better understanding of the threat
landscape. NEMESYS will gather and analyze information about the nature of
cyber-attacks targeting mobile users and the mobile network so that appropriate
counter-measures can be taken. We will develop a data collection infrastructure
that incorporates virtualized mobile honeypots and a honeyclient, to gather,
detect and provide early warning of mobile attacks and better understand the
modus operandi of cyber-criminals that target mobile devices. By correlating
the extracted information with the known patterns of attacks from wireline
networks, we will reveal and identify trends in the way that cyber-criminals
launch attacks against mobile devices.Comment: Accepted for publication in Proceedings of the 28th International
Symposium on Computer and Information Sciences (ISCIS'13); 9 pages; 1 figur
Understanding Android Obfuscation Techniques: A Large-Scale Investigation in the Wild
In this paper, we seek to better understand Android obfuscation and depict a
holistic view of the usage of obfuscation through a large-scale investigation
in the wild. In particular, we focus on four popular obfuscation approaches:
identifier renaming, string encryption, Java reflection, and packing. To obtain
the meaningful statistical results, we designed efficient and lightweight
detection models for each obfuscation technique and applied them to our massive
APK datasets (collected from Google Play, multiple third-party markets, and
malware databases). We have learned several interesting facts from the result.
For example, malware authors use string encryption more frequently, and more
apps on third-party markets than Google Play are packed. We are also interested
in the explanation of each finding. Therefore we carry out in-depth code
analysis on some Android apps after sampling. We believe our study will help
developers select the most suitable obfuscation approach, and in the meantime
help researchers improve code analysis systems in the right direction
ConXsense - Automated Context Classification for Context-Aware Access Control
We present ConXsense, the first framework for context-aware access control on
mobile devices based on context classification. Previous context-aware access
control systems often require users to laboriously specify detailed policies or
they rely on pre-defined policies not adequately reflecting the true
preferences of users. We present the design and implementation of a
context-aware framework that uses a probabilistic approach to overcome these
deficiencies. The framework utilizes context sensing and machine learning to
automatically classify contexts according to their security and privacy-related
properties. We apply the framework to two important smartphone-related use
cases: protection against device misuse using a dynamic device lock and
protection against sensory malware. We ground our analysis on a sociological
survey examining the perceptions and concerns of users related to contextual
smartphone security and analyze the effectiveness of our approach with
real-world context data. We also demonstrate the integration of our framework
with the FlaskDroid architecture for fine-grained access control enforcement on
the Android platform.Comment: Recipient of the Best Paper Awar
Detecting Repackaged Android Applications Using Perceptual Hashing
The last decade has shown a steady rate of Android device dominance in market share and the emergence of hundreds of thousands of apps available to the public. Because of the ease of reverse engineering Android applications, repackaged malicious apps that clone existing code have become a severe problem in the marketplace. This research proposes a novel repackaged detection system based on perceptual hashes of vetted Android apps and their associated dynamic user interface (UI) behavior. Results show that an average hash approach produces 88% accuracy (indicating low false negative and false positive rates) in a sample set of 4878 Android apps, including 2151 repackaged apps. The approach is the first dynamic method proposed in the research community using image-based hashing techniques with reasonable performance to other known dynamic approaches and the possibility for practical implementation at scale for new applications entering the Android market
Visualizing the outcome of dynamic analysis of Android malware with VizMal
Malware detection techniques based on signature extraction require security analysts to manually inspect samples to find evidences of malicious behavior. This time-consuming task received little attention by researchers and practitioners, as most of the effort is on the identification as malware or non-malware of an entire sample. There are no tools for supporting the analyst in identifying when the malicious behavior occurs, given a sample. In this paper we propose VizMal, a tool able to visualize the execution traces of Android applications and to highlight which portions of the traces correspond to a potentially malicious behavior. The aim of VizMal is twofold: assisting the malware analyst during the inspection of an application and pushing the research community to organize and focus its effort on the malicious behavior localization. VizMal is able to discern if the application behavior during each second of execution are legitimate or malicious and to show this information in a simple and understandable way. We validate VizMal experimentally and by means of a user study: the results are promising and confirm that the tool can be useful
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