• Publications
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Using probabilistic generative models for ranking risks of Android apps
TLDR
We propose to use probabilistic generative models for risk scoring schemes, and identify several such models, ranging from the simple Naive Bayes, to advanced hierarchical mixture models. Expand
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Android permissions: a perspective combining risks and benefits
TLDR
We investigate the feasibility of using both the permissions an app requests, the category of the app, and what permissions are requested by other apps in the same category to better inform users whether the risks of installing an app is commensurate with its expected benefit. Expand
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Where the Sidewalk Ends: Extending the Internet AS Graph Using Traceroutes from P2P Users
TLDR
An accurate Internet topology graph is important in many areas of networking, from understanding ISP business relationships to diagnosing network anomalies. Expand
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Demystifying the dark side of the middle: a field study of middlebox failures in datacenters
TLDR
We perform a large-scale empirical study of middlebox failures over two years in a service provider network comprising thousands of middleboxes across tens of datacenters. Expand
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Generating Summary Risk Scores for Mobile Applications
TLDR
We present a wide range of techniques to generate both risk signals and risk scores that are based on heuristics as well as principled machine learning techniques. Expand
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When the network crumbles: an empirical study of cloud network failures and their impact on services
TLDR
We describe a large-scale study analyzing and correlating failure events over three years across multiple datacenters and thousands of network elements such as Access routers, Aggregation switches, Top-of-Rack switches, and long-haul links. Expand
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Plagiarizing Smartphone Applications: Attack Strategies and Defense Techniques
TLDR
We propose three detection schemes that rely on syntactic fingerprinting to detect plagiarized applications under different levels of obfuscation used by the attacker. Expand
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The Dark Menace: Characterizing Network-based Attacks in the Cloud
TLDR
We present the first large-scale characterization of inbound attacks towards the cloud and outbound attacks from the cloud. Expand
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On the Practicality of Integrity Attacks on Document-Level Sentiment Analysis
TLDR
We identify and demonstrate integrity attacks against document-level sentiment analysis that take into account practical constraints dictated by the characteristics of data generated by a sentiment analysis application and demonstrate their effectiveness. Expand
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Juggling the Jigsaw: Towards Automated Problem Inference from Network Trouble Tickets
TLDR
This paper presents NetSieve, a system that aims to do automated problem inference from network trouble tickets to infer the problem symptoms, troubleshooting activities and resolution actions. Expand
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