38,126 research outputs found
Defending against Sybil Devices in Crowdsourced Mapping Services
Real-time crowdsourced maps such as Waze provide timely updates on traffic,
congestion, accidents and points of interest. In this paper, we demonstrate how
lack of strong location authentication allows creation of software-based {\em
Sybil devices} that expose crowdsourced map systems to a variety of security
and privacy attacks. Our experiments show that a single Sybil device with
limited resources can cause havoc on Waze, reporting false congestion and
accidents and automatically rerouting user traffic. More importantly, we
describe techniques to generate Sybil devices at scale, creating armies of
virtual vehicles capable of remotely tracking precise movements for large user
populations while avoiding detection. We propose a new approach to defend
against Sybil devices based on {\em co-location edges}, authenticated records
that attest to the one-time physical co-location of a pair of devices. Over
time, co-location edges combine to form large {\em proximity graphs} that
attest to physical interactions between devices, allowing scalable detection of
virtual vehicles. We demonstrate the efficacy of this approach using
large-scale simulations, and discuss how they can be used to dramatically
reduce the impact of attacks against crowdsourced mapping services.Comment: Measure and integratio
Active Virtual Network Management Prediction: Complexity as a Framework for Prediction, Optimization, and Assurance
Research into active networking has provided the incentive to re-visit what
has traditionally been classified as distinct properties and characteristics of
information transfer such as protocol versus service; at a more fundamental
level this paper considers the blending of computation and communication by
means of complexity. The specific service examined in this paper is network
self-prediction enabled by Active Virtual Network Management Prediction.
Computation/communication is analyzed via Kolmogorov Complexity. The result is
a mechanism to understand and improve the performance of active networking and
Active Virtual Network Management Prediction in particular. The Active Virtual
Network Management Prediction mechanism allows information, in various states
of algorithmic and static form, to be transported in the service of prediction
for network management. The results are generally applicable to algorithmic
transmission of information. Kolmogorov Complexity is used and experimentally
validated as a theory describing the relationship among algorithmic
compression, complexity, and prediction accuracy within an active network.
Finally, the paper concludes with a complexity-based framework for Information
Assurance that attempts to take a holistic view of vulnerability analysis
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
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