3,946 research outputs found
Automatically combining static malware detection techniques
Malware detection techniques come in many different flavors, and cover different effectiveness and efficiency trade-offs. This paper evaluates a number of machine learning techniques to combine multiple static Android malware detection techniques using automatically constructed decision trees. We identify the best methods to construct the trees. We demonstrate that those trees classify sample apps better and faster than individual techniques alone
A Multi-view Context-aware Approach to Android Malware Detection and Malicious Code Localization
Existing Android malware detection approaches use a variety of features such
as security sensitive APIs, system calls, control-flow structures and
information flows in conjunction with Machine Learning classifiers to achieve
accurate detection. Each of these feature sets provides a unique semantic
perspective (or view) of apps' behaviours with inherent strengths and
limitations. Meaning, some views are more amenable to detect certain attacks
but may not be suitable to characterise several other attacks. Most of the
existing malware detection approaches use only one (or a selected few) of the
aforementioned feature sets which prevent them from detecting a vast majority
of attacks. Addressing this limitation, we propose MKLDroid, a unified
framework that systematically integrates multiple views of apps for performing
comprehensive malware detection and malicious code localisation. The rationale
is that, while a malware app can disguise itself in some views, disguising in
every view while maintaining malicious intent will be much harder.
MKLDroid uses a graph kernel to capture structural and contextual information
from apps' dependency graphs and identify malice code patterns in each view.
Subsequently, it employs Multiple Kernel Learning (MKL) to find a weighted
combination of the views which yields the best detection accuracy. Besides
multi-view learning, MKLDroid's unique and salient trait is its ability to
locate fine-grained malice code portions in dependency graphs (e.g.,
methods/classes). Through our large-scale experiments on several datasets
(incl. wild apps), we demonstrate that MKLDroid outperforms three
state-of-the-art techniques consistently, in terms of accuracy while
maintaining comparable efficiency. In our malicious code localisation
experiments on a dataset of repackaged malware, MKLDroid was able to identify
all the malice classes with 94% average recall
An investigation of a deep learning based malware detection system
We investigate a Deep Learning based system for malware detection. In the
investigation, we experiment with different combination of Deep Learning
architectures including Auto-Encoders, and Deep Neural Networks with varying
layers over Malicia malware dataset on which earlier studies have obtained an
accuracy of (98%) with an acceptable False Positive Rates (1.07%). But these
results were done using extensive man-made custom domain features and investing
corresponding feature engineering and design efforts. In our proposed approach,
besides improving the previous best results (99.21% accuracy and a False
Positive Rate of 0.19%) indicates that Deep Learning based systems could
deliver an effective defense against malware. Since it is good in automatically
extracting higher conceptual features from the data, Deep Learning based
systems could provide an effective, general and scalable mechanism for
detection of existing and unknown malware.Comment: 13 Pages, 4 figure
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