Parallel Strands: A Preliminary Investigation into Mining the Web for Bilingual Text

Parallel corpora are a valuable resource for machine translation, but at present their availability and utility is limited by genre- and domain-specificity, licensing restrictions, and the basic difficulty of locating parallel texts in all but the most dominant of the world's languages. A parallel corpus resource not yet explored is the World Wide Web, which hosts an abundance of pages in parallel translation, offering a potential solution to some of these problems and unique opportunities of its own. This paper presents the necessary first step in that exploration: a method for automatically finding parallel translated documents on the Web. The technique is conceptually simple, fully language independent, and scalable, and preliminary evaluation results indicate that the method may be accurate enough to apply without human intervention.Comment: LaTeX2e, 11 pages, 7 eps figures; uses psfig, llncs.cls, theapa.sty. An Appendix at http://umiacs.umd.edu/~resnik/amta98/amta98_appendix.html contains test dat

An Evasion and Counter-Evasion Study in Malicious Websites Detection

Malicious websites are a major cyber attack vector, and effective detection of them is an important cyber defense task. The main defense paradigm in this regard is that the defender uses some kind of machine learning algorithms to train a detection model, which is then used to classify websites in question. Unlike other settings, the following issue is inherent to the problem of malicious websites detection: the attacker essentially has access to the same data that the defender uses to train its detection models. This 'symmetry' can be exploited by the attacker, at least in principle, to evade the defender's detection models. In this paper, we present a framework for characterizing the evasion and counter-evasion interactions between the attacker and the defender, where the attacker attempts to evade the defender's detection models by taking advantage of this symmetry. Within this framework, we show that an adaptive attacker can make malicious websites evade powerful detection models, but proactive training can be an effective counter-evasion defense mechanism. The framework is geared toward the popular detection model of decision tree, but can be adapted to accommodate other classifiers

Crawling Facebook for Social Network Analysis Purposes

We describe our work in the collection and analysis of massive data describing the connections between participants to online social networks. Alternative approaches to social network data collection are defined and evaluated in practice, against the popular Facebook Web site. Thanks to our ad-hoc, privacy-compliant crawlers, two large samples, comprising millions of connections, have been collected; the data is anonymous and organized as an undirected graph. We describe a set of tools that we developed to analyze specific properties of such social-network graphs, i.e., among others, degree distribution, centrality measures, scaling laws and distribution of friendship.\u