Extracting actionable information from Security Forums
The goal of this work is to systematically extract information from hacker forums, whose information would be in general described as unstructured: the text of a post is not necessarily following any writing rules. By contrast, many security initiatives and commercial entities are harnessing the readily public information, but they seem to focus on structured sources of information. Here, we focus on the problem of analyzing text content in security forums. A key novelty is that we use user profiles and contextual features along with transfer learning approach and also embedding space to help us identify and refine information that we could not get from security forum with trivial analysis. We collect a wealth of data from 5 different security forums. The contribution of our work is twofold; (a) we develop a method to automatically identify through the forums malicious IP addresses (b) we also propose a systematic method to identify and classify user-specified threads of interest into four categories. We further showcase how this information can inform knowledge extraction from the forums. As the cyber-wars are becoming more intense, having early accesses to useful information becomes more imperative to remove the hackers first-move advantage, and our work is a solid step towards this direction.
Bag-of-Embeddings for Text Classification
Words are central to text classification. It has been shown that simple Naive Bayes models with word and bigram features can give highly competitive accuracies when compared to more sophisticated models with part-of-speech, syntax and semantic features. Embeddings offer distributional features about words. We study a conceptually simple classification model by exploiting multiprototype word embeddings based on text classes. The key assumption is that words exhibit different distributional characteristics under different text classes. Based on this assumption, we train multiprototype distributional word representations for different text classes. Given a new document, its text class is predicted by maximizing the probabilities of embedding vectors of its words under the class. In two standard classification benchmark datasets, one is balance and the other is imbalance, our model outperforms state-of-the-art systems, on both accuracy and macro-average F-1 score.
Dawn or dusk of the 5th age of research in educational technology? A literature review on (e-)leadership for technology-enhanced learning in higher education (2013-2017)
The aim of this article is to establish the extent to which the concept of e-leadership has taken off as a lens through which to study leadership for technology-enhanced learning (TEL) in higher education. Building on a previous study conducted in 2013, this article thus covers an exploratory review of the literature for the period 2103-2017. It analyses 49 articles which explore both the specific concept of e-leadership as well as other work dealing more generally with leadership and organisational change for TEL in higher education. The findings show that while none of the empirical studies identified in the literature refer explicitly to e-leadership, there are a number of interesting insights to be found in the theoretical articles. The results also highlight the widely different interpretations and applications of the concept of e-leadership and the consequent need for the definition to be refined. The paper concludes with recommendations for further multidisciplinary research at the intersection of the fields of educational technology and educational management, focusing on values, strategy, organisation and leadership interactions at meso level, the economy and public policy at macro level, and teaching and learning at the micro level, as well as for research in Leadership Development for TEL.