Udi Weinsberg
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Featured researches published by Udi Weinsberg.
international world wide web conferences | 2015
Xinyu Xing; Wei Meng; Byoungyoung Lee; Udi Weinsberg; Anmol Sheth; Roberto Perdisci; Wenke Lee
Malvertising is a malicious activity that leverages advertising to distribute various forms of malware. Because advertising is the key revenue generator for numerous Internet companies, large ad networks, such as Google, Yahoo and Microsoft, invest a lot of effort to mitigate malicious ads from their ad networks. This drives adversaries to look for alternative methods to deploy malvertising. In this paper, we show that browser extensions that use ads as their monetization strategy often facilitate the deployment of malvertising. Moreover, while some extensions simply serve ads from ad networks that support malvertising, other extensions maliciously alter the content of visited webpages to force users into installing malware. To measure the extent of these behaviors we developed Expector, a system that automatically inspects and identifies browser extensions that inject ads, and then classifies these ads as malicious or benign based on their landing pages. Using Expector, we automatically inspected over 18,000 Chrome browser extensions. We found 292 extensions that inject ads, and detected 56 extensions that participate in malvertising using 16 different ad networks and with a total user base of 602,417.
ieee international conference computer and communications | 2016
Stefan Dernbach; Nina Taft; James F. Kurose; Udi Weinsberg; Christophe Diot; Azin Ashkan
The majority of Internet traffic is now dominated by streamed video content. As video quality continues to increase, the strain that streaming traffic places on the network infrastructure also increases. Caching content closer to users, e.g., using Content Distribution Networks, is a common solution to reduce the load on the network. A simple approach to selecting what to put in regional caches is to put the videos that are most popular globally across the entire customer base. However, this approach ignores distinct regional taste. In this paper we explore the question of how a video content provider could go about determining whether or not they should use a cache filling policy based solely upon global popularity or take into account regional tastes as well. We propose a model that captures the overlap between inter-regional and intra-regional preferences. We focus on movie content and derive a synthetic model that captures “taste” using matrix factorization, similarly to the method used in recommender systems. Our model enables us to widely explore the parameter space, and derive a set of metrics providers can use to determine whether populating caches according to regional of global tastes provides better cache performance.
Archive | 2016
Valeria Nikolaenko; Udi Weinsberg; Stratis Ioannidis; Marc Joye; Nina Taft
Archive | 2013
Stratis Ioannidis; Udi Weinsberg; Smriti Bhagat
Archive | 2013
Valeria Nikolaenko; Udi Weinsberg; Stratis Ioannidis; Marc Joye; Nina Taft
Archive | 2013
Udi Weinsberg; Smriti Bhagat; Stratis Ioannidis; Nina Taft
Archive | 2015
Smriti Bhagat; Udi Weinsberg; Stratis Ioannidis; Nina Taft
Archive | 2013
Anmol Sheth; Udi Weinsberg; Jaideep Chandrashekar; Bin Liu
international conference on machine learning | 2018
Dimitrios Kalimeris; Yaron Singer; Karthik Subbian; Udi Weinsberg
Archive | 2013
Udi Weinsberg; Smriti Bhagat