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In April 2003 the U.S. Federal Energy Regulatory Commission proposed a complicated market design—the Wholesale Power Market Platform (WPMP)—for common adoption by all US wholesale power markets. Versions of the WPMP have been implemented in New England, New York, the mid-Atlantic states, the Midwest, the Southwest, and California. Strong opposition to the WPMP persists among some industry stakeholders, however, due largely to a perceived lack of adequate performance testing. This study reports on the model development and open-source implementation (in Java) of a computational wholesale power market organized in accordance with core WPMP features and operating over a realistically rendered transmission grid. The traders within this market model are strategic profit-seeking agents whose learning behaviors are based on data from human-subject experiments. Our key experimental focus is the complex interplay among structural conditions, market protocols, and learning behaviors in relation to short-term and longer-term market performance. Findings for a dynamic 5-node transmission grid test case are presented for concrete illustration. This article is an abridged version of Sun and Tesfatsion ST07a  相似文献
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User-input-validation (UIV) is the first barricade that protects web applications from application-level attacks. Most UIV test tools cannot detect semantics-related vulnerabilities in validators, such as filling a five-digit number to a field that accepts a year. To address this issue, we propose a new approach to generate test inputs for UIV based on the analysis of client-side information. In particular, we use input-field information to generate valid inputs, and then perturb valid inputs to generate invalid test inputs. We conducted an empirical study to evaluate our approach. The empirical result shows that, in comparison to existing vulnerability scanners, our approach is more effective than existing vulnerability scanners in finding semantics-related vulnerabilities of UIV for web applications.  相似文献
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输入验证型漏洞在Web安全领域颇受重视,但其在安卓安全研究领域却在很大程度上被忽视。我们发现由于安卓系统中独特的框架层设计,安卓设备需要对系统服务(System Service)进行具体的输入验证分析。本文工作对安卓系统中的输入验证型漏洞进行了分析,1)我们分析了系统服务的攻击面,介绍了目前系统服务中的输入验证的实现情况;2)我们开发了一个漏洞扫描器,通过向系统服务发送带有畸形参数的请求对其进行模糊测试。在对安卓系统中90多个服务和1900多个函数进行综合的分析后,我们发现了16个系统服务漏洞。最后,我们把这些漏洞报告给谷歌并得到了谷歌的确认。  相似文献
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