46,165 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
BCFA: Bespoke Control Flow Analysis for CFA at Scale
Many data-driven software engineering tasks such as discovering programming
patterns, mining API specifications, etc., perform source code analysis over
control flow graphs (CFGs) at scale. Analyzing millions of CFGs can be
expensive and performance of the analysis heavily depends on the underlying CFG
traversal strategy. State-of-the-art analysis frameworks use a fixed traversal
strategy. We argue that a single traversal strategy does not fit all kinds of
analyses and CFGs and propose bespoke control flow analysis (BCFA). Given a
control flow analysis (CFA) and a large number of CFGs, BCFA selects the most
efficient traversal strategy for each CFG. BCFA extracts a set of properties of
the CFA by analyzing the code of the CFA and combines it with properties of the
CFG, such as branching factor and cyclicity, for selecting the optimal
traversal strategy. We have implemented BCFA in Boa, and evaluated BCFA using a
set of representative static analyses that mainly involve traversing CFGs and
two large datasets containing 287 thousand and 162 million CFGs. Our results
show that BCFA can speedup the large scale analyses by 1%-28%. Further, BCFA
has low overheads; less than 0.2%, and low misprediction rate; less than 0.01%.Comment: 12 page
An Empirical Study on Android-related Vulnerabilities
Mobile devices are used more and more in everyday life. They are our cameras,
wallets, and keys. Basically, they embed most of our private information in our
pocket. For this and other reasons, mobile devices, and in particular the
software that runs on them, are considered first-class citizens in the
software-vulnerabilities landscape. Several studies investigated the
software-vulnerabilities phenomenon in the context of mobile apps and, more in
general, mobile devices. Most of these studies focused on vulnerabilities that
could affect mobile apps, while just few investigated vulnerabilities affecting
the underlying platform on which mobile apps run: the Operating System (OS).
Also, these studies have been run on a very limited set of vulnerabilities.
In this paper we present the largest study at date investigating
Android-related vulnerabilities, with a specific focus on the ones affecting
the Android OS. In particular, we (i) define a detailed taxonomy of the types
of Android-related vulnerability; (ii) investigate the layers and subsystems
from the Android OS affected by vulnerabilities; and (iii) study the
survivability of vulnerabilities (i.e., the number of days between the
vulnerability introduction and its fixing). Our findings could help OS and apps
developers in focusing their verification & validation activities, and
researchers in building vulnerability detection tools tailored for the mobile
world
apk2vec: Semi-supervised multi-view representation learning for profiling Android applications
Building behavior profiles of Android applications (apps) with holistic, rich
and multi-view information (e.g., incorporating several semantic views of an
app such as API sequences, system calls, etc.) would help catering downstream
analytics tasks such as app categorization, recommendation and malware analysis
significantly better. Towards this goal, we design a semi-supervised
Representation Learning (RL) framework named apk2vec to automatically generate
a compact representation (aka profile/embedding) for a given app. More
specifically, apk2vec has the three following unique characteristics which make
it an excellent choice for largescale app profiling: (1) it encompasses
information from multiple semantic views such as API sequences, permissions,
etc., (2) being a semi-supervised embedding technique, it can make use of
labels associated with apps (e.g., malware family or app category labels) to
build high quality app profiles, and (3) it combines RL and feature hashing
which allows it to efficiently build profiles of apps that stream over time
(i.e., online learning). The resulting semi-supervised multi-view hash
embeddings of apps could then be used for a wide variety of downstream tasks
such as the ones mentioned above. Our extensive evaluations with more than
42,000 apps demonstrate that apk2vec's app profiles could significantly
outperform state-of-the-art techniques in four app analytics tasks namely,
malware detection, familial clustering, app clone detection and app
recommendation.Comment: International Conference on Data Mining, 201
A Security Monitoring Framework For Virtualization Based HEP Infrastructures
High Energy Physics (HEP) distributed computing infrastructures require
automatic tools to monitor, analyze and react to potential security incidents.
These tools should collect and inspect data such as resource consumption, logs
and sequence of system calls for detecting anomalies that indicate the presence
of a malicious agent. They should also be able to perform automated reactions
to attacks without administrator intervention. We describe a novel framework
that accomplishes these requirements, with a proof of concept implementation
for the ALICE experiment at CERN. We show how we achieve a fully virtualized
environment that improves the security by isolating services and Jobs without a
significant performance impact. We also describe a collected dataset for
Machine Learning based Intrusion Prevention and Detection Systems on Grid
computing. This dataset is composed of resource consumption measurements (such
as CPU, RAM and network traffic), logfiles from operating system services, and
system call data collected from production Jobs running in an ALICE Grid test
site and a big set of malware. This malware was collected from security
research sites. Based on this dataset, we will proceed to develop Machine
Learning algorithms able to detect malicious Jobs.Comment: Proceedings of the 22nd International Conference on Computing in High
Energy and Nuclear Physics, CHEP 2016, 10-14 October 2016, San Francisco.
Submitted to Journal of Physics: Conference Series (JPCS
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