A Semantics-Aware Classification Approach for Data Leakage Prevention

Alneyadi, Sultan; Sithirasenan, Elankayer; Muthukkumarasamy, Vallipuram

doi:10.1007/978-3-319-08344-5_27

Cited by 8 publications

(5 citation statements)

References 9 publications

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“…erefore, data is used as the security protection target, and the intricate enterprise data assets are divided into various categories and multiple levels according to the classification and gradation method. According to the type and value of data, different protection strategies are formulated [9,10], and the continuous strengthening and improvement of sensitive data security management have become more prominent and important.…”

Section: Introductionmentioning

confidence: 99%

Application Research of Intelligent Classification Technology in Enterprise Data Classification and Gradation System

Wang

Chang

et al. 2020

Complexity

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Classification and gradation system adopts different security protection schemes for different types of data by implementing classification and gradation management of data, which is an important pretechnical means for data security protection and prevention of data leakage. This paper introduces artificial intelligence classification, machine learning, and other means to learn and train enterprise documents according to the characteristics of enterprise sensitive data. The generated training model can intelligently identify and classify file streams, improving work efficiency and accuracy of classification and gradation. At the same time, the differences, advantages, and disadvantages of K-NN (K-Nearest Neighbors), DT (Decision Tree), and LinearSVC algorithms are compared. The experimental data shows that LinearSVC algorithm is applicable to high-dimensional data, with discrete, sparse data features and large number of features, which is more suitable for classification of sensitive data of enterprises.

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Section: Introductionmentioning

confidence: 99%

Application Research of Intelligent Classification Technology in Enterprise Data Classification and Gradation System

Wang

Chang

et al. 2020

Complexity

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“…We are not the first to explore applying machine learning toward sensitive text classification. However, previous authors, e.g., [6], [7], [8], have discussed the problem and proposed solutions for approaching it. Unfortunately, real sensitive text datasets are generally kept private, so these works were not able to perform realistic evaluations, instead using fictitious "sensitive" texts (e.g., collected from public Twitter feeds).…”

Section: Related Workmentioning

confidence: 99%

Automated U.S diplomatic cables security classification: Topic model pruning vs. classification based on clusters

Alzhrani

Rudd²,

Chow

et al. 2017

2017 IEEE International Symposium on Technologies for Homeland Security (HST)

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The U.S Government has been the target for cyberattacks from all over the world. Just recently, former President Obama accused the Russian government of the leaking emails to Wikileaks and declared that the U.S. might be forced to respond. While Russia denied involvement, it is clear that the U.S. has to take some defensive measures to protect its data infrastructure. Insider threats have been the cause of other sensitive information leaks too, including the infamous Edward Snowden incident. Most of the recent leaks were in the form of text. Due to the nature of text data, security classifications are assigned manually. In an adversarial environment, insiders can leak texts through E-mail, printers, or any untrusted channels. The optimal defense is to automatically detect the unstructured text security class and enforce the appropriate protection mechanism without degrading services or daily tasks. Unfortunately, existing Data Leak Prevention (DLP) systems are not well suited for detecting unstructured texts. In this paper, we compare two recent approaches in the literature for text security classification, evaluating them on actual sensitive text data from the WikiLeaks dataset.

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“…They then assigned a confidentiality score to each document by calculating the confidential term probability. Alneyadi et al [7] used L 1 -norms [8] between ngram category profiles, assigning the document to the category of shortest distance. Hart et al [9] proposed a new training method to overcome the problem of imbalanced data by implementing class-specific classifiers.…”

Section: Related Workmentioning

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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A Semantics-Aware Classification Approach for Data Leakage Prevention

Cited by 8 publications

References 9 publications

Application Research of Intelligent Classification Technology in Enterprise Data Classification and Gradation System

Application Research of Intelligent Classification Technology in Enterprise Data Classification and Gradation System

Automated U.S diplomatic cables security classification: Topic model pruning vs. classification based on clusters

Automated big text security classification

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