Biblio
The purpose of this paper is to analyze all Cloud based Service Models, Continuous Integration, Deployment and Delivery process and propose an Automated Continuous Testing and testing as a service based TestBot and metrics dashboard which will be integrated with all existing automation, bug logging, build management, configuration and test management tools. Recently cloud is being used by organizations to save time, money and efforts required to setup and maintain infrastructure and platform. Continuous Integration and Delivery is in practice nowadays within Agile methodology to give capability of multiple software releases on daily basis and ensuring all the development, test and Production environments could be synched up quickly. In such an agile environment there is need to ramp up testing tools and processes so that overall regression testing including functional, performance and security testing could be done along with build deployments at real time. To support this phenomenon, we researched on Continuous Testing and worked with industry professionals who are involved in architecting, developing and testing the software products. A lot of research has been done towards automating software testing so that testing of software product could be done quickly and overall testing process could be optimized. As part of this paper we have proposed ACT TestBot tool, metrics dashboard and coined 4S quality metrics term to quantify quality of the software product. ACT testbot and metrics dashboard will be integrated with Continuous Integration tools, Bug reporting tools, test management tools and Data Analytics tools to trigger automation scripts, continuously analyze application logs, open defects automatically and generate metrics reports. Defect pattern report will be created to support root cause analysis and to take preventive action.
Stealing confidential information from a database has become a severe vulnerability issue for web applications. The attacks can be prevented by defining a whitelist of SQL queries issued by web applications and detecting queries not in list. For large-scale web applications, automated generation of the whitelist is conducted because manually defining numerous query patterns is impractical for developers. Conventional methods for automated generation are unable to detect attacks immediately because of the long time required for collecting legitimate queries. Moreover, they require application-specific implementations that reduce the versatility of the methods. As described herein, we propose a method to generate a whitelist automatically using queries issued during web application tests. Our proposed method uses the queries generated during application tests. It is independent of specific applications, which yields improved timeliness against attacks and versatility for multiple applications.
Captchas are designed to be easy for humans but hard for machines. However, most recent research has focused only on making them hard for machines. In this paper, we present what is to the best of our knowledge the first large scale evaluation of captchas from the human perspective, with the goal of assessing how much friction captchas present to the average user. For the purpose of this study we have asked workers from Amazon’s Mechanical Turk and an underground captchabreaking service to solve more than 318 000 captchas issued from the 21 most popular captcha schemes (13 images schemes and 8 audio scheme). Analysis of the resulting data reveals that captchas are often difficult for humans, with audio captchas being particularly problematic. We also find some demographic trends indicating, for example, that non-native speakers of English are slower in general and less accurate on English-centric captcha schemes. Evidence from a week’s worth of eBay captchas (14,000,000 samples) suggests that the solving accuracies found in our study are close to real-world values, and that improving audio captchas should become a priority, as nearly 1% of all captchas are delivered as audio rather than images. Finally our study also reveals that it is more effective for an attacker to use Mechanical Turk to solve captchas than an underground service.
Black-box web application vulnerability scanners are automated tools that probe web applications for security vulnerabilities. In order to assess the current state of the art, we obtained access to eight leading tools and carried out a study of: (i) the class of vulnerabilities tested by these scanners, (ii) their effectiveness against target vulnerabilities, and (iii) the relevance of the target vulnerabilities to vulnerabilities found in the wild. To conduct our study we used a custom web application vulnerable to known and projected vulnerabilities, and previous versions of widely used web applications containing known vulnerabilities. Our results show the promise and effectiveness of automated tools, as a group, and also some limitations. In particular, "stored" forms of Cross Site Scripting (XSS) and SQL Injection (SQLI) vulnerabilities are not currently found by many tools. Because our goal is to assess the potential of future research, not to evaluate specific vendors, we do not report comparative data or make any recommendations about purchase of specific tools.