7,489 research outputs found
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
Effective methods to detect metamorphic malware: A systematic review
The succeeding code for metamorphic Malware is routinely rewritten to
remain stealthy and undetected within infected environments. This characteristic is
maintained by means of encryption and decryption methods, obfuscation through
garbage code insertion, code transformation and registry modification which makes
detection very challenging. The main objective of this study is to contribute an
evidence-based narrative demonstrating the effectiveness of recent proposals. Sixteen
primary studies were included in this analysis based on a pre-defined protocol. The
majority of the reviewed detection methods used Opcode, Control Flow Graph (CFG)
and API Call Graph. Key challenges facing the detection of metamorphic malware
include code obfuscation, lack of dynamic capabilities to analyse code and application
difficulty. Methods were further analysed on the basis of their approach, limitation,
empirical evidence and key parameters such as dataset, Detection Rate (DR) and
False Positive Rate (FPR)
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