755 research outputs found
Android HIV: A Study of Repackaging Malware for Evading Machine-Learning Detection
Machine learning based solutions have been successfully employed for
automatic detection of malware in Android applications. However, machine
learning models are known to lack robustness against inputs crafted by an
adversary. So far, the adversarial examples can only deceive Android malware
detectors that rely on syntactic features, and the perturbations can only be
implemented by simply modifying Android manifest. While recent Android malware
detectors rely more on semantic features from Dalvik bytecode rather than
manifest, existing attacking/defending methods are no longer effective. In this
paper, we introduce a new highly-effective attack that generates adversarial
examples of Android malware and evades being detected by the current models. To
this end, we propose a method of applying optimal perturbations onto Android
APK using a substitute model. Based on the transferability concept, the
perturbations that successfully deceive the substitute model are likely to
deceive the original models as well. We develop an automated tool to generate
the adversarial examples without human intervention to apply the attacks. In
contrast to existing works, the adversarial examples crafted by our method can
also deceive recent machine learning based detectors that rely on semantic
features such as control-flow-graph. The perturbations can also be implemented
directly onto APK's Dalvik bytecode rather than Android manifest to evade from
recent detectors. We evaluated the proposed manipulation methods for
adversarial examples by using the same datasets that Drebin and MaMadroid (5879
malware samples) used. Our results show that, the malware detection rates
decreased from 96% to 1% in MaMaDroid, and from 97% to 1% in Drebin, with just
a small distortion generated by our adversarial examples manipulation method.Comment: 15 pages, 11 figure
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
PlaceRaider: Virtual Theft in Physical Spaces with Smartphones
As smartphones become more pervasive, they are increasingly targeted by
malware. At the same time, each new generation of smartphone features
increasingly powerful onboard sensor suites. A new strain of sensor malware has
been developing that leverages these sensors to steal information from the
physical environment (e.g., researchers have recently demonstrated how malware
can listen for spoken credit card numbers through the microphone, or feel
keystroke vibrations using the accelerometer). Yet the possibilities of what
malware can see through a camera have been understudied. This paper introduces
a novel visual malware called PlaceRaider, which allows remote attackers to
engage in remote reconnaissance and what we call virtual theft. Through
completely opportunistic use of the camera on the phone and other sensors,
PlaceRaider constructs rich, three dimensional models of indoor environments.
Remote burglars can thus download the physical space, study the environment
carefully, and steal virtual objects from the environment (such as financial
documents, information on computer monitors, and personally identifiable
information). Through two human subject studies we demonstrate the
effectiveness of using mobile devices as powerful surveillance and virtual
theft platforms, and we suggest several possible defenses against visual
malware
StoryDroid: Automated Generation of Storyboard for Android Apps
Mobile apps are now ubiquitous. Before developing a new app, the development
team usually endeavors painstaking efforts to review many existing apps with
similar purposes. The review process is crucial in the sense that it reduces
market risks and provides inspiration for app development. However, manual
exploration of hundreds of existing apps by different roles (e.g., product
manager, UI/UX designer, developer) in a development team can be ineffective.
For example, it is difficult to completely explore all the functionalities of
the app in a short period of time. Inspired by the conception of storyboard in
movie production, we propose a system, StoryDroid, to automatically generate
the storyboard for Android apps, and assist different roles to review apps
efficiently. Specifically, StoryDroid extracts the activity transition graph
and leverages static analysis techniques to render UI pages to visualize the
storyboard with the rendered pages. The mapping relations between UI pages and
the corresponding implementation code (e.g., layout code, activity code, and
method hierarchy) are also provided to users. Our comprehensive experiments
unveil that StoryDroid is effective and indeed useful to assist app
development. The outputs of StoryDroid enable several potential applications,
such as the recommendation of UI design and layout code
An analysis of android malware classification services
The increasing number of Android malware forced antivirus (AV) companies to rely on automated classification techniques to determine the family and class of suspicious samples. The research community relies heavily on such labels to carry out prevalence studies of the threat ecosystem and to build datasets that are used to validate and benchmark novel detection and classification methods. In this work, we carry out an extensive study of the Android malware ecosystem by surveying white papers and reports from 6 key players in the industry, as well as 81 papers from 8 top security conferences, to understand how malware datasets are used by both. We, then, explore the limitations associated with the use of available malware classification services, namely VirusTotal (VT) engines, for determining the family of an Android sample. Using a dataset of 2.47 M Android malware samples, we find that the detection coverage of VT's AVs is generally very low, that the percentage of samples flagged by any 2 AV engines does not go beyond 52%, and that common families between any pair of AV engines is at best 29%. We rely on clustering to determine the extent to which different AV engine pairs agree upon which samples belong to the same family (regardless of the actual family name) and find that there are discrepancies that can introduce noise in automatic label unification schemes. We also observe the usage of generic labels and inconsistencies within the labels of top AV engines, suggesting that their efforts are directed towards accurate detection rather than classification. Our results contribute to a better understanding of the limitations of using Android malware family labels as supplied by common AV engines.This work has been supported by the “Ramon y Cajal” Fellowship RYC-2020-029401
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