Zobrazeno 1 - 10
of 92
pro vyhledávání: '"PAULEY, ERIC"'
Autor:
King, Rachel, Burke, Quinn, Beugin, Yohan, Hoak, Blaine, Li, Kunyang, Pauley, Eric, Sheatsley, Ryan, McDaniel, Patrick
The Tor anonymity network allows users such as political activists and those under repressive governments to protect their privacy when communicating over the internet. At the same time, Tor has been demonstrated to be vulnerable to several classes o
Externí odkaz:
http://arxiv.org/abs/2408.14646
Signature-based Intrusion Detection Systems (SIDSs) are traditionally used to detect malicious activity in networks. A notable example of such a system is Snort, which compares network traffic against a series of rules that match known exploits. Curr
Externí odkaz:
http://arxiv.org/abs/2402.09644
Autor:
Héon, Alban, Sheatsley, Ryan, Burke, Quinn, Hoak, Blaine, Pauley, Eric, Beugin, Yohan, McDaniel, Patrick
Location data privacy has become a serious concern for users as Location Based Services (LBSs) have become an important part of their life. It is possible for malicious parties having access to geolocation data to learn sensitive information about th
Externí odkaz:
http://arxiv.org/abs/2309.06263
Autor:
Burke, Quinn, Beugin, Yohan, Hoak, Blaine, King, Rachel, Pauley, Eric, Sheatsley, Ryan, Yu, Mingli, He, Ting, La Porta, Thomas, McDaniel, Patrick
Cloud file systems offer organizations a scalable and reliable file storage solution. However, cloud file systems have become prime targets for adversaries, and traditional designs are not equipped to protect organizations against the myriad of attac
Externí odkaz:
http://arxiv.org/abs/2305.18639
Autor:
Pauley, Eric, Domico, Kyle, Hoak, Blaine, Sheatsley, Ryan, Burke, Quinn, Beugin, Yohan, Kirda, Engin, McDaniel, Patrick
Public clouds necessitate dynamic resource allocation and sharing. However, the dynamic allocation of IP addresses can be abused by adversaries to source malicious traffic, bypass rate limiting systems, and even capture traffic intended for other clo
Externí odkaz:
http://arxiv.org/abs/2210.14999
Adversarial examples, inputs designed to induce worst-case behavior in machine learning models, have been extensively studied over the past decade. Yet, our understanding of this phenomenon stems from a rather fragmented pool of knowledge; at present
Externí odkaz:
http://arxiv.org/abs/2209.04521
Autor:
Beugin, Yohan, Burke, Quinn, Hoak, Blaine, Sheatsley, Ryan, Pauley, Eric, Tan, Gang, Hussain, Syed Rafiul, McDaniel, Patrick
Millions of consumers depend on smart camera systems to remotely monitor their homes and businesses. However, the architecture and design of popular commercial systems require users to relinquish control of their data to untrusted third parties, such
Externí odkaz:
http://arxiv.org/abs/2208.09776
Public clouds provide scalable and cost-efficient computing through resource sharing. However, moving from traditional on-premises service management to clouds introduces new challenges; failure to correctly provision, maintain, or decommission elast
Externí odkaz:
http://arxiv.org/abs/2204.05122
Autor:
Beugin, Yohan, Burke, Quinn, Hoak, Blaine, Sheatsley, Ryan, Pauley, Eric, Tan, Gang, Hussain, Syed Rafiul, McDaniel, Patrick
Publikováno v:
PoPETS (Proceedings on Privacy Enhancing Technologies Symposium) 2022
Millions of consumers depend on smart camera systems to remotely monitor their homes and businesses. However, the architecture and design of popular commercial systems require users to relinquish control of their data to untrusted third parties, such
Externí odkaz:
http://arxiv.org/abs/2201.09338
Autor:
Sheatsley, Ryan, Hoak, Blaine, Pauley, Eric, Beugin, Yohan, Weisman, Michael J., McDaniel, Patrick
Machine learning is vulnerable to adversarial examples-inputs designed to cause models to perform poorly. However, it is unclear if adversarial examples represent realistic inputs in the modeled domains. Diverse domains such as networks and phishing
Externí odkaz:
http://arxiv.org/abs/2105.08619