No abstract

In order to classify a web page as being benign or malicious, we designed 14 basic and 16 extended features. The basic features that we implemented were selected to represent the essential characteristics of a web page. The system heuristically combines two basic features into one extended feature in order to effectively distinguish benign and malicious pages. The support vector machine can be trained to successfully classify pages by using these features. Because more and more malicious web pages are appearing, and they change so rapidly, classifiers that are trained by old data may misclassify some new pages. To overcome this problem, we selected an adaptive support vector machine (aSVM) as a classifier. The aSVM can learn training data and can quickly learn additional training data based on the support vectors it obtained during its previous learning session. Experimental results verified that the aSVM can classify malicious web pages adaptively.

A peer-to-peer model is very useful in solving the server link bottleneck problem of a client-server model. In this work, we discuss the problems of distributing multimedia content over peer-to-peer network. We focus on two problems in peer-to-peer media content distribution systems. The first is the transmission scheduling of the media data for a multi-source streaming session. We present a sophisticated scheduling scheme, which results in minimum buffering delay. The second problem is on the fast distribution of media content in the peer-to-peer system that is self-growing. We propose a mechanism accelerating the speed at which the system's streaming capacity increases.

No abstract

No abstract