Black-box web application vulnerability scanners are automated tools that are used to crawl a web application to look for vulnerabilities. These tools are often used in one of two ways. In the first approach, scanners are used as Point-and-Shoot tools where a scanner is only given the root URL of an application and asked to scan the site. Whereas, in the second approach, scanners are first configured to maximize the crawling coverage and vulnerability detection accuracy. Although the performance of leading commercial scanners has been thoroughly studied, very little research has been done to evaluate open-source scanners. This paper presents a feature and performance evaluation of five open-source scanners. We analyze the crawling coverage, vulnerability detection accuracy, scanning speed, report- ing and usability features. The scanners are tested against two well known benchmarks: WIVET and WAVSEP. Additionally, the scanners are tested against a realistic web application called WackoPicko. The chosen benchmarks are composed of a wide range of vulnerabilities and crawling challenges. Each scanner is tested in two modes: default and configured. Lastly, the scanners are compared with the state of the art commercial scanner Burp Suite Professional.
Our results show that being able to properly crawl a web application is a critical task in detecting vulnerabilities. Unfortunately, the majority of the scanners evaluated had difficulty crawling through common web technologies such as dynamically generated JavaScript content and Flash applications. We also identified several classes of vulnerabilities that are not being detected by the scanners. Furthermore, our results show that scanners displayed considerable improvement when run in configured mode.
| Identifer | oai:union.ndltd.org:uottawa.ca/oai:ruor.uottawa.ca:10393/38595 |
| Date | 19 December 2018 |
| Creators | Khalil, Rana Fouad |
| Contributors | Adams, Carlisle |
| Publisher | Université d'Ottawa / University of Ottawa |
| Source Sets | Université d’Ottawa |
| Language | English |
| Detected Language | English |
| Type | Thesis |
| Format | application/pdf |
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