22,571 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
Matrix Completion With Variational Graph Autoencoders: Application in Hyperlocal Air Quality Inference
Inferring air quality from a limited number of observations is an essential
task for monitoring and controlling air pollution. Existing inference methods
typically use low spatial resolution data collected by fixed monitoring
stations and infer the concentration of air pollutants using additional types
of data, e.g., meteorological and traffic information. In this work, we focus
on street-level air quality inference by utilizing data collected by mobile
stations. We formulate air quality inference in this setting as a graph-based
matrix completion problem and propose a novel variational model based on graph
convolutional autoencoders. Our model captures effectively the spatio-temporal
correlation of the measurements and does not depend on the availability of
additional information apart from the street-network topology. Experiments on a
real air quality dataset, collected with mobile stations, shows that the
proposed model outperforms state-of-the-art approaches
Deep Learning with Long Short-Term Memory for Time Series Prediction
Time series prediction can be generalized as a process that extracts useful
information from historical records and then determines future values. Learning
long-range dependencies that are embedded in time series is often an obstacle
for most algorithms, whereas Long Short-Term Memory (LSTM) solutions, as a
specific kind of scheme in deep learning, promise to effectively overcome the
problem. In this article, we first give a brief introduction to the structure
and forward propagation mechanism of the LSTM model. Then, aiming at reducing
the considerable computing cost of LSTM, we put forward the Random Connectivity
LSTM (RCLSTM) model and test it by predicting traffic and user mobility in
telecommunication networks. Compared to LSTM, RCLSTM is formed via stochastic
connectivity between neurons, which achieves a significant breakthrough in the
architecture formation of neural networks. In this way, the RCLSTM model
exhibits a certain level of sparsity, which leads to an appealing decrease in
the computational complexity and makes the RCLSTM model become more applicable
in latency-stringent application scenarios. In the field of telecommunication
networks, the prediction of traffic series and mobility traces could directly
benefit from this improvement as we further demonstrate that the prediction
accuracy of RCLSTM is comparable to that of the conventional LSTM no matter how
we change the number of training samples or the length of input sequences.Comment: 9 pages, 5 figures, 14 reference
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