se the user's service. These are just examples of possible fraud scenarios that have become public. In fact, there are different techniques that scammers can use to reach their goals: Eavesdropping on public transactions to obtain personal data. This can be done by looking over the shoulder of a person performing a © 2009 by Taylor & Francis Group, LLC Towards a Fraud Detection Framework in VoIP Networks transaction or by sniffing on VoIP transactions packets especially if encryption is not used. Using viruses and Trojan horses to get private data stored in computers. Phishing, impersonate a trusted organization or a VoIP provider and ask customers for some private in order to fix a feigned problem. Spam: most spam activities require you to take advantage of the good deals they are offering. If some personal data is given to them, this information will be used for carrying out their attacks. Browsing social networks, for instance web sites of public domains where personal data is posted. Equipment stealing or hacking. 24.2.4 State-of-the-Art Fraud plagues all kinds of business activities ranging from credit card misuse, Internet transactions, telecommunications services and most recently VoIP services. The most obvious approach to prevent such misuse is to reduce the possibility of identity theft and the possibility for a fraudster to misuse the system with false or stolen personal data. Such protection measures include PIN numbers for credit cards or encrypted passwords and are usually stan- dardized by industrial groups. Such prevention measures are outside the scope of this chapter. Instead, we will focus more on the fraud detection issue. That is, we will investigate solutions for identifying fraud as quickly as it happens, i.e., once fraud prevention has failed. When looking at the literature dealing with fraud detection, especially in the credit card and telecommunications areas, we can identify two major approaches, namely rule based and artificial intelligence based detection. 24.2.4.1 Rule Based Detectio