J
Jon Oberheide
Researcher at University of Michigan
Publications - 33
Citations - 3119
Jon Oberheide is an academic researcher from University of Michigan. The author has contributed to research in topics: Authentication protocol & Authentication. The author has an hindex of 20, co-authored 33 publications receiving 3068 citations.
Papers
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Proceedings ArticleDOI
Internet inter-domain traffic
TL;DR: The majority of inter-domain traffic by volume now flows directly between large content providers, data center / CDNs and consumer networks, and this analysis shows significant changes in inter-AS traffic patterns and an evolution of provider peering strategies.
Book ChapterDOI
Automated classification and analysis of internet malware
TL;DR: This paper examines the ability of existing host-based anti-virus products to provide semantically meaningful information about the malicious software and tools used by attackers and proposes a new classification technique that describes malware behavior in terms of system state changes rather than in sequences or patterns of system calls.
Proceedings Article
CloudAV: N-version antivirus in the network cloud
TL;DR: It is shown that the average length of time to detect new threats by an antivirus engine is 48 days and that retrospective detection can greatly minimize the impact of this delay, and a new model for malware detection on end hosts based on providing antivirus as an in-cloud network service is advocated.
Patent
Network service for the detection, analysis and quarantine of malicious and unwanted files
TL;DR: In this paper, a system is provided for detecting, analyzing and quarantining unwanted files in a network environment, where a host agent residing on a computing device in the network environment detects a new file introduced to the computing device and sends the new file to a network service for analysis.
Proceedings ArticleDOI
Virtualized in-cloud security services for mobile devices
TL;DR: This paper proposes a new model whereby mobile antivirus functionality is moved to an off-device network service employing multiple virtualized malware detection engines, and demonstrates how the in-cloud model enhances mobile security and reduces on-device software complexity, while allowing for new services such as platform-specific behavioral analysis engines.