95,514 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
Handling Homographs in Neural Machine Translation
Homographs, words with different meanings but the same surface form, have
long caused difficulty for machine translation systems, as it is difficult to
select the correct translation based on the context. However, with the advent
of neural machine translation (NMT) systems, which can theoretically take into
account global sentential context, one may hypothesize that this problem has
been alleviated. In this paper, we first provide empirical evidence that
existing NMT systems in fact still have significant problems in properly
translating ambiguous words. We then proceed to describe methods, inspired by
the word sense disambiguation literature, that model the context of the input
word with context-aware word embeddings that help to differentiate the word
sense be- fore feeding it into the encoder. Experiments on three language pairs
demonstrate that such models improve the performance of NMT systems both in
terms of BLEU score and in the accuracy of translating homographs.Comment: NAACL201
- …