1,060 research outputs found
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 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
Learning Fast and Slow: PROPEDEUTICA for Real-time Malware Detection
In this paper, we introduce and evaluate PROPEDEUTICA, a novel methodology
and framework for efficient and effective real-time malware detection,
leveraging the best of conventional machine learning (ML) and deep learning
(DL) algorithms. In PROPEDEUTICA, all software processes in the system start
execution subjected to a conventional ML detector for fast classification. If a
piece of software receives a borderline classification, it is subjected to
further analysis via more performance expensive and more accurate DL methods,
via our newly proposed DL algorithm DEEPMALWARE. Further, we introduce delays
to the execution of software subjected to deep learning analysis as a way to
"buy time" for DL analysis and to rate-limit the impact of possible malware in
the system. We evaluated PROPEDEUTICA with a set of 9,115 malware samples and
877 commonly used benign software samples from various categories for the
Windows OS. Our results show that the false positive rate for conventional ML
methods can reach 20%, and for modern DL methods it is usually below 6%.
However, the classification time for DL can be 100X longer than conventional ML
methods. PROPEDEUTICA improved the detection F1-score from 77.54% (conventional
ML method) to 90.25%, and reduced the detection time by 54.86%. Further, the
percentage of software subjected to DL analysis was approximately 40% on
average. Further, the application of delays in software subjected to ML reduced
the detection time by approximately 10%. Finally, we found and discussed a
discrepancy between the detection accuracy offline (analysis after all traces
are collected) and on-the-fly (analysis in tandem with trace collection). Our
insights show that conventional ML and modern DL-based malware detectors in
isolation cannot meet the needs of efficient and effective malware detection:
high accuracy, low false positive rate, and short classification time.Comment: 17 pages, 7 figure
DeepAPT: Nation-State APT Attribution Using End-to-End Deep Neural Networks
In recent years numerous advanced malware, aka advanced persistent threats
(APT) are allegedly developed by nation-states. The task of attributing an APT
to a specific nation-state is extremely challenging for several reasons. Each
nation-state has usually more than a single cyber unit that develops such
advanced malware, rendering traditional authorship attribution algorithms
useless. Furthermore, those APTs use state-of-the-art evasion techniques,
making feature extraction challenging. Finally, the dataset of such available
APTs is extremely small.
In this paper we describe how deep neural networks (DNN) could be
successfully employed for nation-state APT attribution. We use sandbox reports
(recording the behavior of the APT when run dynamically) as raw input for the
neural network, allowing the DNN to learn high level feature abstractions of
the APTs itself. Using a test set of 1,000 Chinese and Russian developed APTs,
we achieved an accuracy rate of 94.6%
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