1,062 research outputs found
R2-D2: ColoR-inspired Convolutional NeuRal Network (CNN)-based AndroiD Malware Detections
The influence of Deep Learning on image identification and natural language
processing has attracted enormous attention globally. The convolution neural
network that can learn without prior extraction of features fits well in
response to the rapid iteration of Android malware. The traditional solution
for detecting Android malware requires continuous learning through
pre-extracted features to maintain high performance of identifying the malware.
In order to reduce the manpower of feature engineering prior to the condition
of not to extract pre-selected features, we have developed a coloR-inspired
convolutional neuRal networks (CNN)-based AndroiD malware Detection (R2-D2)
system. The system can convert the bytecode of classes.dex from Android archive
file to rgb color code and store it as a color image with fixed size. The color
image is input to the convolutional neural network for automatic feature
extraction and training. The data was collected from Jan. 2017 to Aug 2017.
During the period of time, we have collected approximately 2 million of benign
and malicious Android apps for our experiments with the help from our research
partner Leopard Mobile Inc. Our experiment results demonstrate that the
proposed system has accurate security analysis on contracts. Furthermore, we
keep our research results and experiment materials on http://R2D2.TWMAN.ORG.Comment: Verison 2018/11/15, IEEE BigData 2018, Seattle, WA, USA, Dec 10-13,
2018. (Accepted
Partial Evaluation of String Obfuscations for Java Malware Detection
The fact that Java is platform independent gives hackers the opportunity to write exploits that can target users on any platform, which has a JVM implementation. Metasploit is a well-known source of Javaexploits and to circumvent detection by Anti Virus (AV) software, obfuscation techniques are routinely applied to make an exploit more difficult to recognise. Popular obfuscation techniques for Java include stringobfuscation and applying reflection to hide method calls; two techniques that can either be used together or independently. This paper shows how to apply partial evaluation to remove these obfuscations and thereby improve AV matching. The paper presents a partial evaluator for Jimple, which is an intermediate language for JVM bytecode designed for optimisation and program analysis, and demonstrates how partially evaluated Jimple code, when transformed back into Java, improves the detection rates of a number of commercial AV products
Static Analysis for Extracting Permission Checks of a Large Scale Framework: The Challenges And Solutions for Analyzing Android
A common security architecture is based on the protection of certain
resources by permission checks (used e.g., in Android and Blackberry). It has
some limitations, for instance, when applications are granted more permissions
than they actually need, which facilitates all kinds of malicious usage (e.g.,
through code injection). The analysis of permission-based framework requires a
precise mapping between API methods of the framework and the permissions they
require. In this paper, we show that naive static analysis fails miserably when
applied with off-the-shelf components on the Android framework. We then present
an advanced class-hierarchy and field-sensitive set of analyses to extract this
mapping. Those static analyses are capable of analyzing the Android framework.
They use novel domain specific optimizations dedicated to Android.Comment: IEEE Transactions on Software Engineering (2014). arXiv admin note:
substantial text overlap with arXiv:1206.582
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