42,378 research outputs found
Automated Big Text Security Classification
In recent years, traditional cybersecurity safeguards have proven ineffective
against insider threats. Famous cases of sensitive information leaks caused by
insiders, including the WikiLeaks release of diplomatic cables and the Edward
Snowden incident, have greatly harmed the U.S. government's relationship with
other governments and with its own citizens. Data Leak Prevention (DLP) is a
solution for detecting and preventing information leaks from within an
organization's network. However, state-of-art DLP detection models are only
able to detect very limited types of sensitive information, and research in the
field has been hindered due to the lack of available sensitive texts. Many
researchers have focused on document-based detection with artificially labeled
"confidential documents" for which security labels are assigned to the entire
document, when in reality only a portion of the document is sensitive. This
type of whole-document based security labeling increases the chances of
preventing authorized users from accessing non-sensitive information within
sensitive documents. In this paper, we introduce Automated Classification
Enabled by Security Similarity (ACESS), a new and innovative detection model
that penetrates the complexity of big text security classification/detection.
To analyze the ACESS system, we constructed a novel dataset, containing
formerly classified paragraphs from diplomatic cables made public by the
WikiLeaks organization. To our knowledge this paper is the first to analyze a
dataset that contains actual formerly sensitive information annotated at
paragraph granularity.Comment: Pre-print of Best Paper Award IEEE Intelligence and Security
Informatics (ISI) 2016 Manuscrip
Neural Cross-Lingual Entity Linking
A major challenge in Entity Linking (EL) is making effective use of
contextual information to disambiguate mentions to Wikipedia that might refer
to different entities in different contexts. The problem exacerbates with
cross-lingual EL which involves linking mentions written in non-English
documents to entries in the English Wikipedia: to compare textual clues across
languages we need to compute similarity between textual fragments across
languages. In this paper, we propose a neural EL model that trains fine-grained
similarities and dissimilarities between the query and candidate document from
multiple perspectives, combined with convolution and tensor networks. Further,
we show that this English-trained system can be applied, in zero-shot learning,
to other languages by making surprisingly effective use of multi-lingual
embeddings. The proposed system has strong empirical evidence yielding
state-of-the-art results in English as well as cross-lingual: Spanish and
Chinese TAC 2015 datasets.Comment: Association for the Advancement of Artificial Intelligence (AAAI),
201
Social Search with Missing Data: Which Ranking Algorithm?
Online social networking tools are extremely popular, but can miss potential discoveries latent in the social 'fabric'. Matchmaking services which can do naive profile matching with old database technology are too brittle in the absence of key data, and even modern ontological markup, though powerful, can be onerous at data-input time. In this paper, we present a system called BuddyFinder which can automatically identify buddies who can best match a user's search requirements specified in a term-based query, even in the absence of stored user-profiles. We deploy and compare five statistical measures, namely, our own CORDER, mutual information (MI), phi-squared, improved MI and Z score, and two TF/IDF based baseline methods to find online users who best match the search requirements based on 'inferred profiles' of these users in the form of scavenged web pages. These measures identify statistically significant relationships between online users and a term-based query. Our user evaluation on two groups of users shows that BuddyFinder can find users highly relevant to search queries, and that CORDER achieved the best average ranking correlations among all seven algorithms and improved the performance of both baseline methods
Visualizing the semantic content of large text databases using text maps
A methodology for generating text map representations of the semantic content of text databases is presented. Text maps provide a graphical metaphor for conceptualizing and visualizing the contents and data interrelationships of large text databases. Described are a set of experiments conducted against the TIPSTER corpora of Wall Street Journal articles. These experiments provide an introduction to current work in the representation and visualization of documents by way of their semantic content
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