This paper investigates the design architectures for reliable high-yield low operating voltage non-volatile flip-flops (NVFF) for zero-leakage and instantaneously-on ultra-low power applications in scaled CMOS technologies. A reliable thin-gate oxide NVFF, integrating OxRAM current-based storing and restoring solutions is designed and analyzed in 28nm FD-SOI. The proposed class of NVFF designs has been optimized for optimal OxRAM programming conditions that improve endurance and minimize programming power, while ensuring high yield. The OxRAM device silicon measurements show that a low programming current benefits endurance, but at the expense of a reduced memory window (ROFF/RON). Statistical analysis demonstrates that a low NVFF operating voltage in restore mode can be achieved with a narrow memory window by using the current-based restoring. In a representative design, compared to a standard FF, the non-volatility is added at the cost of less than 3% of performance and up to 3.5%-13% of active energy increase, with 108 cycles of endurance. Then compared with the data-retention FF supplied at 0.5V, NVFF reduces the sleep power consumption for standby modes longer than 0.34s for uniform Q switching (0.17s-0.6s) Finally, the low variability of the FD-SOI technology enables 3 sigma yield restore down to 0.7V.

In this paper we describe a virtual 3D lab created using Open Wonderland to enable simultaneous execution of different computer science simulator modules in a collaborative, immersive workspace. Simulators were divided into functional modules that can be reused and combined for different teaching topics based on statistics of their usage. We discuss some important issues in our platform setup, describe our learning environment and logging module we developed in order to track system usage. Preliminary usability testing confirms the efficiency of this approach.

The number and the importance of web applications have increased rapidly over the last years. At the same time, the quantity and impact of security vulnerabilities in such applications have grown as well. Since manual code reviews are time-consuming, error-prone and costly, the need for automated solutions has become evident. In this paper, we address the problem of vulnerable web applications by means of static source code analysis. More precisely, we use flow-sensitive, interprocedural and context-sensitive data flow analysis to discover vulnerable points in a program. In addition to the taint analysis at the core of our engine, we employ a precise alias analysis targeted at the unique reference semantics commonly found in scripting languages. Moreover, we enhance the quality and quantity of the generated vulnerability reports by employing an iterative two-phase algorithm for fast and precise resolution of file inclusions. The presented concepts are targeted at the general class of taint-style vulnerabilities and can be easily applied to the detection of vulnerability types such as SQL injection, cross-site scripting (XSS), and command injection. We implemented the presented concepts in Pixy, a high-precision static analysis tool aimed at detecting cross-site scripting and SQL injection vulnerabilities in PHP programs. To demonstrate the effectiveness of our techniques, we analyzed a number of popular, open-source web applications and discovered hundreds of previously unknown vulnerabilities. Both the high analysis speed as well as the low number of generated false positives show that our techniques can be used for conducting effective security audits.

Web applications are ubiquitous, perform mission- critical tasks, and handle sensitive user data. Unfortunately, web applications are often implemented by developers with limited security skills, and, as a result, they contain vulnerabilities. Most of these vulnerabilities stem from the lack of input validation. That is, web applications use malicious input as part of a sensitive operation, without having properly checked or sanitized the input values prior to their use. Past research on vulnerability analysis has mostly focused on identifying cases in which a web application directly uses external input in critical operations. However, little research has been performed to analyze the correctness of the sanitization process. Thus, whenever a web application applies some sanitization routine to potentially malicious input, the vulnerability analysis assumes that the result is innocuous. Unfortunately, this might not be the case, as the sanitization process itself could be incorrect or incomplete. In this paper, we present a novel approach to the analysis of the sanitization process. More precisely, we combine static and dynamic analysis techniques to identify faulty sanitization procedures that can be bypassed by an attacker. We implemented our approach in a tool, called Saner, and we applied it to a number of real-world applications. Our results demonstrate that we were able to identify several novel vulnerabilities that stem from erroneous sanitization procedures.

The number and the importance of Web applications have increased rapidly over the last years. At the same time, the quantity and impact of security vulnerabilities in such applications have grown as well. Since manual code reviews are time-consuming, error-prone and costly, the need for automated solutions has become evident. In this paper, we address the problem of vulnerable Web applications by means of static source code analysis. More precisely, we use flow-sensitive, interprocedural and context-sensitive dataflow analysis to discover vulnerable points in a program. In addition, alias and literal analysis are employed to improve the correctness and precision of the results. The presented concepts are targeted at the general class of taint-style vulnerabilities and can be applied to the detection of vulnerability types such as SQL injection, cross-site scripting, or command injection. Pixy, the open source prototype implementation of our concepts, is targeted at detecting cross-site scripting vulnerabilities in PHP scripts. Using our tool, we discovered and reported 15 previously unknown vulnerabilities in three Web applications, and reconstructed 36 known vulnerabilities in three other Web applications. The observed false positive rate is at around 50% (i.e., one false positive for each vulnerability) and therefore, low enough to permit effective security audits

As the popularity of the web increases and web applications become tools of everyday use, the role of web security has been gaining importance as well. The last years have shown a significant increase in the number of web-based attacks. For example, there has been extensive press coverage of recent security incidences involving the loss of sensitive credit card information belonging to millions of customers.Many web application security vulnerabilities result from generic input validation problems. Examples of such vulnerabilities are SQL injection and Cross-Site Scripting (XSS). Although the majority of web vulnerabilities are easy to understand and to avoid, many web developers are, unfortunately, not security-aware. As a result, there exist many web sites on the Internet that are vulnerable.This paper demonstrates how easy it is for attackers to automatically discover and exploit application-level vulnerabilities in a large number of web applications. To this end, we developed SecuBat, a generic and modular web vulnerability scanner that, similar to a port scanner, automatically analyzes web sites with the aim of finding exploitable SQL injection and XSS vulnerabilities. Using SecuBat, we were able to find many potentially vulnerable web sites. To verify the accuracy of SecuBat, we picked one hundred interesting web sites from the potential victim list for further analysis and confirmed exploitable flaws in the identified web pages. Among our victims were well-known global companies and a finance ministry. Of course, we notified the administrators of vulnerable sites about potential security problems. More than fifty responded to request additional information or to report that the security hole was closed.