2,562 research outputs found
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
Artificial intelligence in the cyber domain: Offense and defense
Artificial intelligence techniques have grown rapidly in recent years, and their applications in practice can be seen in many fields, ranging from facial recognition to image analysis. In the cybersecurity domain, AI-based techniques can provide better cyber defense tools and help adversaries improve methods of attack. However, malicious actors are aware of the new prospects too and will probably attempt to use them for nefarious purposes. This survey paper aims at providing an overview of how artificial intelligence can be used in the context of cybersecurity in both offense and defense.Web of Science123art. no. 41
Analysis and evaluation of SafeDroid v2.0, a framework for detecting malicious Android applications
Android smartphones have become a vital component of the daily routine of millions of people, running a plethora of applications available in the official and alternative marketplaces. Although there are many security mechanisms to scan and filter malicious applications, malware is still able to reach the devices of many end-users. In this paper, we introduce the SafeDroid v2.0 framework, that is a flexible, robust, and versatile open-source solution for statically analysing Android applications, based on machine learning techniques. The main goal of our work, besides the automated production of fully sufficient prediction and classification models in terms of maximum accuracy scores and minimum negative errors, is to offer an out-of-the-box framework that can be employed by the Android security researchers to efficiently experiment to find effective solutions: the SafeDroid v2.0 framework makes it possible to test many different combinations of machine learning classifiers, with a high degree of freedom and flexibility in the choice of features to consider, such as dataset balance and dataset selection. The framework also provides a server, for generating experiment reports, and an Android application, for the verification of the produced models in real-life scenarios. An extensive campaign of experiments is also presented to show how it is possible to efficiently find competitive solutions: the results of our experiments confirm that SafeDroid v2.0 can reach very good performances, even with highly unbalanced dataset inputs and always with a very limited overhead
apk2vec: Semi-supervised multi-view representation learning for profiling Android applications
Building behavior profiles of Android applications (apps) with holistic, rich
and multi-view information (e.g., incorporating several semantic views of an
app such as API sequences, system calls, etc.) would help catering downstream
analytics tasks such as app categorization, recommendation and malware analysis
significantly better. Towards this goal, we design a semi-supervised
Representation Learning (RL) framework named apk2vec to automatically generate
a compact representation (aka profile/embedding) for a given app. More
specifically, apk2vec has the three following unique characteristics which make
it an excellent choice for largescale app profiling: (1) it encompasses
information from multiple semantic views such as API sequences, permissions,
etc., (2) being a semi-supervised embedding technique, it can make use of
labels associated with apps (e.g., malware family or app category labels) to
build high quality app profiles, and (3) it combines RL and feature hashing
which allows it to efficiently build profiles of apps that stream over time
(i.e., online learning). The resulting semi-supervised multi-view hash
embeddings of apps could then be used for a wide variety of downstream tasks
such as the ones mentioned above. Our extensive evaluations with more than
42,000 apps demonstrate that apk2vec's app profiles could significantly
outperform state-of-the-art techniques in four app analytics tasks namely,
malware detection, familial clustering, app clone detection and app
recommendation.Comment: International Conference on Data Mining, 201
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