3,172 research outputs found
The Dark Side(-Channel) of Mobile Devices: A Survey on Network Traffic Analysis
In recent years, mobile devices (e.g., smartphones and tablets) have met an
increasing commercial success and have become a fundamental element of the
everyday life for billions of people all around the world. Mobile devices are
used not only for traditional communication activities (e.g., voice calls and
messages) but also for more advanced tasks made possible by an enormous amount
of multi-purpose applications (e.g., finance, gaming, and shopping). As a
result, those devices generate a significant network traffic (a consistent part
of the overall Internet traffic). For this reason, the research community has
been investigating security and privacy issues that are related to the network
traffic generated by mobile devices, which could be analyzed to obtain
information useful for a variety of goals (ranging from device security and
network optimization, to fine-grained user profiling).
In this paper, we review the works that contributed to the state of the art
of network traffic analysis targeting mobile devices. In particular, we present
a systematic classification of the works in the literature according to three
criteria: (i) the goal of the analysis; (ii) the point where the network
traffic is captured; and (iii) the targeted mobile platforms. In this survey,
we consider points of capturing such as Wi-Fi Access Points, software
simulation, and inside real mobile devices or emulators. For the surveyed
works, we review and compare analysis techniques, validation methods, and
achieved results. We also discuss possible countermeasures, challenges and
possible directions for future research on mobile traffic analysis and other
emerging domains (e.g., Internet of Things). We believe our survey will be a
reference work for researchers and practitioners in this research field.Comment: 55 page
Discriminative models for multi-instance problems with tree-structure
Modeling network traffic is gaining importance in order to counter modern
threats of ever increasing sophistication. It is though surprisingly difficult
and costly to construct reliable classifiers on top of telemetry data due to
the variety and complexity of signals that no human can manage to interpret in
full. Obtaining training data with sufficiently large and variable body of
labels can thus be seen as prohibitive problem. The goal of this work is to
detect infected computers by observing their HTTP(S) traffic collected from
network sensors, which are typically proxy servers or network firewalls, while
relying on only minimal human input in model training phase. We propose a
discriminative model that makes decisions based on all computer's traffic
observed during predefined time window (5 minutes in our case). The model is
trained on collected traffic samples over equally sized time window per large
number of computers, where the only labels needed are human verdicts about the
computer as a whole (presumed infected vs. presumed clean). As part of training
the model itself recognizes discriminative patterns in traffic targeted to
individual servers and constructs the final high-level classifier on top of
them. We show the classifier to perform with very high precision, while the
learned traffic patterns can be interpreted as Indicators of Compromise. In the
following we implement the discriminative model as a neural network with
special structure reflecting two stacked multi-instance problems. The main
advantages of the proposed configuration include not only improved accuracy and
ability to learn from gross labels, but also automatic learning of server types
(together with their detectors) which are typically visited by infected
computers
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
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