Web Structure Reorganization to Improve Web Navigation Efficiency

The study aims to improve Web navigation efficiency by reorganizing Web structure. Navigation efficiency is defined mathematically for both navigation with / without target destination pages, e.g. for experienced and new users. To help experienced users not to lose their orientation, structure stability is taken into consideration. Stability constraint can also help website designers control the maintaining effort of Web. This study proposes a mathematical programming method to reorganize Web structure in order to achieve better navigation efficiency. Designer can specify the user requirements and how stable the website structure should be. An e-banking example is given to illustrate how the method works in scenarios where user surfs with target destination. This study has the advantage of assessing and improving navigation efficiency and of relieving the designer of tedious chore to modify the structure in transformation

2016-09-01 Concerns

Staff Congress concerns for September 1, 2016

2016-10-03 Meeting Minutes

Staff Congress meeting minutes for October 3, 2016

2016-10-03 Meeting Minutes

Staff Congress meeting minutes for October 3, 2016

Detecting Abnormal Behavior in Web Applications

The rapid advance of web technologies has made the Web an essential part of our daily lives. However, network attacks have exploited vulnerabilities of web applications, and caused substantial damages to Internet users. Detecting network attacks is the first and important step in network security. A major branch in this area is anomaly detection. This dissertation concentrates on detecting abnormal behaviors in web applications by employing the following methodology. For a web application, we conduct a set of measurements to reveal the existence of abnormal behaviors in it. We observe the differences between normal and abnormal behaviors. By applying a variety of methods in information extraction, such as heuristics algorithms, machine learning, and information theory, we extract features useful for building a classification system to detect abnormal behaviors.;In particular, we have studied four detection problems in web security. The first is detecting unauthorized hotlinking behavior that plagues hosting servers on the Internet. We analyze a group of common hotlinking attacks and web resources targeted by them. Then we present an anti-hotlinking framework for protecting materials on hosting servers. The second problem is detecting aggressive behavior of automation on Twitter. Our work determines whether a Twitter user is human, bot or cyborg based on the degree of automation. We observe the differences among the three categories in terms of tweeting behavior, tweet content, and account properties. We propose a classification system that uses the combination of features extracted from an unknown user to determine the likelihood of being a human, bot or cyborg. Furthermore, we shift the detection perspective from automation to spam, and introduce the third problem, namely detecting social spam campaigns on Twitter. Evolved from individual spammers, spam campaigns manipulate and coordinate multiple accounts to spread spam on Twitter, and display some collective characteristics. We design an automatic classification system based on machine learning, and apply multiple features to classifying spam campaigns. Complementary to conventional spam detection methods, our work brings efficiency and robustness. Finally, we extend our detection research into the blogosphere to capture blog bots. In this problem, detecting the human presence is an effective defense against the automatic posting ability of blog bots. We introduce behavioral biometrics, mainly mouse and keyboard dynamics, to distinguish between human and bot. By passively monitoring user browsing activities, this detection method does not require any direct user participation, and improves the user experience