CoBAn: A context based model for data leakage prevention

Katz, Gilad; Elovici, Yuval; Shapira, Bracha

doi:10.1016/j.ins.2013.10.005

Cited by 61 publications

(23 citation statements)

References 32 publications

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“…Some of the notable ones are online message sentiment filtering, e-mail sentiment classification, web blog author's attitude analysis etc. Data leakage analysis is an emerging area that can be focused on in security systems (Katz et al 2014). …”

Section: Application Areasmentioning

confidence: 99%

Sentiment analysis and the complex natural language

Khan

Durrani

Ali

et al. 2016

Complex Adapt Syst Model

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There is huge amount of content produced online by amateur authors, covering a large variety of topics. Sentiment analysis (SA) extracts and aggregates users’ sentiments towards a target entity. Machine learning (ML) techniques are frequently used as the natural language data is in abundance and has definite patterns. ML techniques adapt to domain specific solution at high accuracy depending upon the feature set used. The lexicon-based techniques, using external dictionary, are independent of data to prevent overfitting but they miss context too in specialized domains. Corpus-based statistical techniques require large data to stabilize. Complex network based techniques are highly resourceful, preserving order, proximity, context and relationships. Recent applications developed incorporate the platform specific structural information i.e. meta-data. New sub-domains are introduced as influence analysis, bias analysis, and data leakage analysis. The nature of data is also evolving where transcribed customer-agent phone conversation are also used for sentiment analysis. This paper reviews sentiment analysis techniques and highlight the need to address natural language processing (NLP) specific open challenges. Without resolving the complex NLP challenges, ML techniques cannot make considerable advancements. The open issues and challenges in the area are discussed, stressing on the need of standard datasets and evaluation methodology. It also emphasized on the need of better language models that could capture context and proximity.

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Section: Application Areasmentioning

confidence: 99%

Sentiment analysis and the complex natural language

Khan

Durrani

Ali

et al. 2016

Complex Adapt Syst Model

View full text Add to dashboard Cite

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“…Katz et al [6] employed the k-means algorithm on cosine similarity to cluster all the documents in a corpus regardless of their sensitivity level. They then assigned a confidentiality score to each document by calculating the confidential term probability.…”

Section: Related Workmentioning

confidence: 99%

“…A collection of groups of similarity clusters is generated with the same set of similarity features. The entire generation process is outlined in Algorithm 1. for each paragraph do 6 Tokenize Paragraphs; 8 8 Remove Special Characters; 10 10 Remove Stop-Words; 12 12 Convert letters to lower case; B. Similarity-Based Classification Models Fig. 3 describes the overall process required to construct similarity-based classification models from similarity clusters.…”

Section: A Generation Of Collection Of Groups Of Similarity Clustersmentioning

confidence: 99%

Automated big text security classification

Alzhrani

Rudd²,

Boult³

et al. 2016

2016 IEEE Conference on Intelligence and Security Informatics (ISI)

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Abstract-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.

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“…It can consist of one or multiple terms, e.g., "battery life". The aspects and sentiments extracted are used for aspect-based sentiment analysis (Katz et al 2014;Hai et al 2014;Poria et al 2013;Machova and Marhefka 2014;Medhat et al 2014).…”

Section: Big Subjective Datamentioning

confidence: 99%

Lifelong aspect extraction from big data: knowledge engineering

Khan

Durrani

Khalid

et al. 2016

Complex Adapt Syst Model

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Traditional machine learning techniques follow a single shot learning approach. It includes all supervised, semi-supervised, transfer learning, hybrid and unsupervised techniques having a single target domain known prior to analysis. Learning from one task is not carried to the next task, therefore, they cannot scale up to big data having many unknown domains. Lifelong learning models are tailored for big data having a knowledge module that is maintained automatically. The knowledge-base grows with experience where knowledge from previous tasks helps in current task. This paper surveys topic models leading the discussion to knowledge-based topic models and lifelong learning models. The issues and challenges in learning knowledge, its abstraction, retention and transfer are elaborated. The state-of-the art models store word pairs as knowledge having positive or negative co-relations called must-links and cannot-links. The need for innovative ideas from other research fields is stressed to learn more varieties of knowledge to improve accuracy and reveal more semantic structures from within the data.

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CoBAn: A context based model for data leakage prevention

Cited by 61 publications

References 32 publications

Sentiment analysis and the complex natural language

Sentiment analysis and the complex natural language

Automated big text security classification

Lifelong aspect extraction from big data: knowledge engineering

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