This paper proposes a new method for realizing the web page recommendation system by sharing users' web browse history on an anonymous P2P network. Our scheme creates a user profile, a summary of the user's web browse trends, by analyzing the contents of the web pages browsed. The scheme then provides a P2P network to exchange web browse histories so as to create mutual web page recommendations. The novelty of our method lies in its P2P network formulation; it is formulated in a way so that users having similar user profiles are automatically connected, yet their user profiles are protected from being disclosed to other users. The proposed method intentionally distributes bogus user profiles on the P2P network, while not harming the efficiency of the web browse history sharing process.

This paper presents a network surveillance technique for detecting malicious activities. Based on the hypothesis that unusual conducts like system exploitation will trigger an abnormal network pattern, we try to detect this anomalous network traffic pattern as a sign of malicious, or at least suspicious activities. Capturing and analyzing of a network traffic pattern is implemented with a concept of port profiling, where measures representing various characteristics of connections are monitored and recorded for each port. Though the generation of the port profiles requires the minimum calculation and memory, they exhibit high stability and robustness. Each port profile retains the patterns of the corresponding connections precisely, even if the connections demonstrate multi-modal characteristics. By comparing the pattern exhibited by live traffic with the expected behavior recorded in the profile, intrusive activities like compromising backdoors or invoking trojan programs are successfully detected.