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Titlebook: Computer Security - ESORICS 2010; 15th European Sympos Dimitris Gritzalis,Bart Preneel,Marianthi Theohari Conference proceedings 2010 Sprin

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樓主: 變成小松鼠
51#
發(fā)表于 2025-3-30 09:51:35 | 只看該作者
Darrel W. Amundsen,Gary B. Ferngrendependent verification is desirable because the scheduler is part of the runtime environment and, hence, usually not known when a program is analyzed. Unlike for other system properties, it is not straightforward to achieve scheduler independence when verifying information flow security, and the exi
52#
發(fā)表于 2025-3-30 15:04:14 | 只看該作者
53#
發(fā)表于 2025-3-30 17:13:47 | 只看該作者
The Case for Contract in Medical Ethicsal impact, their real world deployment is typically hindered by their significant complexity compared to other (non-interactive) crypto primitives. Moreover, their design and implementation are time-consuming and error-prone..We contribute to overcoming these challenges as follows: We present a comp
54#
發(fā)表于 2025-3-30 22:07:11 | 只看該作者
55#
發(fā)表于 2025-3-31 02:23:31 | 只看該作者
56#
發(fā)表于 2025-3-31 08:38:51 | 只看該作者
The Case for Contract in Medical Ethicsd identification accuracy. Recently, traffic morphing [1] was proposed to thwart website fingerprinting by changing the packet size distribution so as to mimic some other website, while minimizing bandwidth overhead. In this paper, we point out that packet ordering information, though noisy, can be
57#
發(fā)表于 2025-3-31 12:48:07 | 只看該作者
58#
發(fā)表于 2025-3-31 17:23:42 | 只看該作者
Mario A. Orlandi,Harold P. Freeman of a network communication. Such technique has wide applications in network security and privacy. Although several methods have been proposed to detect various flow watermarks, few can effectively detect spread-spectrum flow watermarks. Moreover, there is currently no solution that allows end users
59#
發(fā)表于 2025-3-31 19:04:03 | 只看該作者
60#
發(fā)表于 2025-3-31 23:29:44 | 只看該作者
Edward M. Donnelly MD,Andrew S. Blum MD, PHDeavily on manual verification to achieve a low false positive rate, which, however, tends to be slow in responding to the huge volume of unique phishing URLs created by toolkits. Our goal here is to combine the best aspects of human verified blacklists and heuristic-based methods, i.e., the low fals
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