No abstract

No abstract

No abstract

No abstract

Misuse-based intrusion detection systems rely on models of attacks to identify the manifestation of intrusive behavior. Therefore, the ability of these systems to reliably detect attacks is strongly affected by the quality of their models, which are often called "signatures." A perfect model would be able to detect all the instances of an attack without making mistakes, that is, it would produce a 100% detection rate with 0 false alarms. Unfortunately, writing good models (or good signatures) is hard. Attacks that exploit a specific vulnerability may do so in completely different ways, and writing models that take into account all possible variations is very difficult. For this reason, it would be beneficial to have testing tools that are able to evaluate the "goodness" of detection signatures. This work describes a technique to test and evaluate misuse detection models in the case of network-based intrusion detection systems. The testing technique is based on a mechanism that generates a large number of variations of an exploit by applying mutant operators to an exploit template. These mutant exploits are then run against a victim host protected by a network-based intrusion detection system. The results of the systems in detecting these variations provide a quantitative basis for the evaluation of the quality of the corresponding detection model.

No abstract

Web developers routinely rely on third-party Java-Script libraries such as jQuery to enhance the functionality of their sites. However, if not properly maintained, such dependencies can create attack vectors allowing a site to be compromised.In this paper, we conduct the first comprehensive study of client-side JavaScript library usage and the resulting security implications across the Web. Using data from over 133 k websites, we show that 37 % of them include at least one library with a known vulnerability; the time lag behind the newest release of a library is measured in the order of years. In order to better understand why websites use so many vulnerable or outdated libraries, we track causal inclusion relationships and quantify different scenarios. We observe sites including libraries in ad hoc and often transitive ways, which can lead to different versions of the same library being loaded into the same document at the same time. Furthermore, we find that libraries included transitively, or via ad and tracking code, are more likely to be vulnerable. This demonstrates that not only website administrators, but also the dynamic architecture and developers of third-party services are to blame for the Web's poor state of library management.The results of our work underline the need for more thorough approaches to dependency management, code maintenance and third-party code inclusion on the Web.

Abstract. Content Security Policy (CSP) has been proposed as a principled and robust browser security mechanism against content injection attacks such as XSS. When configured correctly, CSP renders malicious code injection and data exfiltration exceedingly difficult for attackers. However, despite the promise of these security benefits and being implemented in almost all major browsers, CSP adoption is minuscule-our measurements show that CSP is deployed in enforcement mode on only 1% of the Alexa Top 100. In this paper, we present the results of a long-term study to determine challenges in CSP deployments that can prevent wide adoption. We performed weekly crawls of the Alexa Top 1M to measure adoption of web security headers, and find that CSP both significantly lags other security headers, and that the policies in use are often ineffective at actually preventing content injection. In addition, we evaluate the feasibility of deploying CSP from the perspective of a security-conscious website operator. We used an incremental deployment approach through CSP's report-only mode on four websites, collecting over 10M reports. Furthermore, we used semi-automated policy generation through web application crawling on a set of popular websites. We found both that automated methods do not suffice and that significant barriers exist to producing accurate results. Finally, based on our observations, we suggest several improvements to CSP that could help to ease its adoption by the web community.