Anti-virus vendors are confronted with a multitude of potentially malicious samples today. Receiving thousands of new samples every day is not uncommon. The signatures that detect confirmed malicious threats are mainly still created manually, so it is important to discriminate between samples that pose a new unknown threat and those that are mere variants of known malware. This survey article provides an overview of techniques based on dynamic analysis that are used to analyze potentially malicious samples. It also covers analysis programs that leverage these It also covers analysis programs that employ these techniques to assist human analysts in assessing, in a timely and appropriate manner, whether a given sample deserves closer manual inspection due to its unknown malicious behavior.

Web applications have become an integral part of the daily lives of millions of users. Unfortunately, web applications are also frequently targeted by attackers, and attacks such as XSS and SQL injection are still common. In this paper, we present an empirical study of more than 7000 input validation vulnerabilities with the aim of gaining deeper insights into how these common web vulnerabilities can be prevented. In particular, we focus on the relationship between the specific programming language used to develop web applications and the vulnerabilities that are commonly reported. Our findings suggest that most SQL injection and a significant number of XSS vulnerabilities can be prevented using straight-forward validation mechanisms based on common data types. We elaborate on these common data types, and discuss how support could be provided in web application frameworks.

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Web applications have become an integral part of the daily lives of millions of users. Unfortunately, web applications are also frequently targeted by attackers, and criticial vulnerabilities such as XSS and SQL injection are still common. As a consequence, much effort in the past decade has been spent on mitigating web application vulnerabilities.Current techniques focus mainly on sanitization: either on automated sanitization, the detection of missing sanitizers, the correctness of sanitizers, or the correct placement of sanitizers. However, these techniques are either not able to prevent new forms of input validation vulnerabilities such as HTTP Parameter Pollution, come with large runtime overhead, lack precision, or require significant modifications to the client and/or server infrastructure.In this paper, we present IPAAS, a novel technique for preventing the exploitation of XSS and SQL injection vulnerabilities based on automated data type detection of input parameters. IPAAS automatically and transparently augments otherwise insecure web application development environments with input validators that result in significant and tangible security improvements for real systems. We implemented IPAAS for PHP and evaluated it on five real-world web applications with known XSS and SQL injection vulnerabilities. Our evaluation demonstrates that IPAAS would have prevented 83% of SQL injection vulnerabilities and 65% of XSS vulnerabilities while incurring no developer burden.

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