Dynamicity Case
Dynamic IP Addressing is the very basic need of any web based service in current times. Most of the applications rely on dynamic addressing to save costs as single IP address can be re-used time and again by assigning it to multiple users. It also poses lesser security risks. This paper aims at introducing a peculiar method of DAMap with a capability to identify IP addresses that are assigned dynamically for an application and analyze the behavior in context of their dynamicity. The subroutine called DAMap has been applied here to a Gmail user’s login trace for week long duration to identify a significant number of IP addresses working around. The results clearly indicate that dynamic IP addresses are used vastly in the world of Internet. The results also indicate that around 97% of mail servers running on dynamic IP addresses actually send out a large section of spam mails. This property, if captured intelligent, can help in spam mail filtering, identification and prevention of phishing websites and botnet detection.
Introduction
The most common methods used for identification of malicious hosts on Internet are based on tracking of machine hosts connected to Internet using host IP addresses. These methods were devised keeping in mind that application use statically allocated IP addresses for connecting to the clients. Unfortunately, static IP addresses are just a small bite of the ocean set of all IP addresses in the mesh of Internet. In reality, Internet plays around more with dynamic IP addresses now. Therefore, there is a greater need to accurately identify a dynamic IP addresses and its frequency of use through a background procedure that can do the task in an automatic manner.
Understanding the pattern of dynamicity of IP addresses is a taxing task because these are highly volatile addresses and the scale at which they get assigned changes quite rapidly. In past, numerous applications have been developed