Today the internet has become primary source of communication among people because it holds limitless space and pool of various web applications and resources. The internet allows us to communicate in a fraction of second with another people who is sitting in the other part of the world. At present, the internet has become part of our daily life and its usage is increasing exponentially, therefore it accumulates a number of web applications on daily basis on Web podium. Most of the web applications exist with few weaknesses and that weaknesses give room to several bad buys (hackers) to interrupt that weak part of code in web applications. Recently, SQL Injection, Cross Site Scripting (XSS) and Cross Site Request Forgery (CSRF) seriously threaten the most of the web applications.  In this study, we have proposed a black box testing method to detect different web vulnerabilities such as SQL Injection, XSS and CSRF and developed a detection tool i.e. Web Vulnerabilities Finder (WVF) for most of these vulnerabilities.  Our proposed method can automatically analyze websites with the aim of finding web vulnerabilities. By applying the tool to some websites, we have found 45 exploitable XSS, SQL Injection 45, Directory Discloser 38 and Local/remote file inclusion 40 vulnerabilities. The experimental results show that our tool can efficiently detect XSS, SQL Injection, Directory Discloser and LFI/RFI vulnerabilities.

Nowadays, spam has become serious issue for computer security, because it becomes a main source for disseminating threats, including viruses, worms and phishing attacks. Currently, a large volume of received emails are spam. Different approaches to combating these unwanted messages, including challenge response model, whitelisting, blacklisting, email signatures and different machine learning methods, are in place to deal with this issue. These solutions are available for end users but due to dynamic nature of Web, there is no 100% secure systems around the world which can handle this problem. In most of the cases spam detectors use machine learning techniques to filter web traffic. This work focuses on systematically analyzing the strength and weakness of current technologies for spam detection and taxonomy of known approaches is introduced.