Term weighting has proven to be an effective way to improve the performance of text categorization. Very recently, with the development of user-interactive question answering or community question answering, there has emerged a need to accurately categorize questions into predefined categories. However, as a question is usually a piece of short text, can the existing term-weighting methods perform consistently in question categorization as they do in text categorization? The answer is not clear, since to the best of our knowledge, we have not seen any work related to this problem despite of its significance. In this study, we investigate the popular unsupervised and supervised term-weighting methods for question categorization. At the same time, we propose three new supervised term-weighting methods, namely, qf*icf, iqf*qf*icf, and vrf. Comparisons of them with existing unsupervised and supervised term-weighting methods are made through a series of experiments on question collections of Yahoo! Answers. The experimental results show that iqf*qf*icf achieves the best performance among all term-weighting methods, while qf*icf and vrf are also competitive for question categorization. Meanwhile, tf*OR is proven to be the most significant one among existing methods. In addition, iqf*qf*icf and vrf are also effective for long document categorization.

An approach to identification of the phishing target of a given (suspicious) webpage is proposed by clustering the webpage set consisting of its all associated webpages and the given webpage itself. We first find its associated webpages, and then explore their relationships to the given webpage as their features for clustering. Such relationships include link relationship, ranking relationship, text similarity, and webpage layout similarity relationship. A DBSCAN clustering method is employed to find if there is a cluster around the given webpage. If such cluster exists, we claim the given webpage is a phishing webpage and then find its phishing target (i.e., the legitimate webpage it is attacking) from this cluster. Otherwise, we identify it as a legitimate webpage. Our test dataset consists of 8745 phishing pages (targeting at 76 well-known websites) selected from Phish Tank and preliminary experiments show that the approach can successfully identify 91.44% of their phishing targets. Another dataset of 1000 legitimate webpages is collected to test our method's false alarm rate, which is 3.40%.

Visual spoofing in Unicode-based text is anticipated as a severe web security problem in the near future as more and more Unicode-based web documents will be used. In this paper, to detect whether a suspicious Unicode character in a word is visual spoofing or not, the context of the suspicious character is utilized by employing a Bayesian framework. Specifically, two contexts are taken into consideration: simple context and general context. Simple context of a suspicious character is the word where the character exists while general context consists of all homoglyphs of the character within Universal Character Set (UCS). Three decision rules are designed and used jointly for convicting a suspicious character. Preliminary evaluations and user study show that the proposed approach can detect Unicode-based visual spoofing with high effectiveness and efficiency.