Analyzing group communication for preventing data leakage via email

Zilberman, Polina; Katz, Gilad; Elovici, Yuval; Shabtai, Asaf

doi:10.1109/isi.2011.5984047

Cited by 24 publications

(11 citation statements)

References 3 publications

(5 reference statements)

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“…On the preventive methods techniques namely, Policy and Access Rights, Virtualization and Isolation, Cryptographic Approaches and Quantifying are explained and in other side Data Identification, Social and Behavioural, Data mining / text clustering, and Quantifying and Limiting methods are explained in detective side and finally AI based methods have been explained with its advantages over other methods [37].…”

Section: Academic Researchmentioning

confidence: 99%

Data Leakage Prevention System: A Systematic Report

Gowr. P*,

2019

IJRTE

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Technology is growing exponentially and in the fast growing technology more fast processing devices are being introduced. A mobile phone could even handle high processing software and even a small SD card could store the complete data of an organization. In this fast growing it environment maintaining security of data is highly important as a small loss of data might create critical impact in the organization, hence preventing their sensitive data became a greatest challenge. Traditionally organisation implemented methods like framing policies in an organization, implementing Firewall, virtual private network at the endpoints, but still these method started lagging as the technology developed the methodology of data leakage and data theft also attained heights. Hence there was a need for some system which could prevent leakage of data and this could be done with the help of Data Leakage Prevention system (DLPs). The DLPs are capable of detect leakage in Data at any states namely Data in rest, Data in transit and Data in use which increases the need of the DLPs.This paper briefly explains about the carious DLPs available, their limitations, providing the information about the gaps in security providing enough awareness on new developers as well as researchers and professionals for developing a next generation DLP which is capable of preventing data loss.

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Section: Academic Researchmentioning

confidence: 99%

Data Leakage Prevention System: A Systematic Report

Gowr. P*,

2019

IJRTE

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“…However, practically speaking, the HAC is utilized by specifying the clusters in a manual way. For the HAC, common criteria will comprise of a sum of the within-group sums of squares along with the shortest possible distance among sets that triggers in a technique of single-link [11].The HAC is iterative that builds a tree, T, made over a dataset based on the linkage function. The linkage function l: 2X × 2X → R will score the actual merger of two different nodes that correspond to clusters with data points that are stored at the descendant leaves.…”

Section: B Hierarchical Agglomerative Clustering (Hac)mentioning

confidence: 99%

Data Leak Identification in Social Networks using K Means Clustering & Tabu K Means Clustering

Karthik¹,

Tamizhazhagan²,

Narayana³

2019

IJITEE

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The prevention of leakage of data has been defined as a process or solution which identifies data that is confidential, tracks the data in a way in which it moves in and out of its enterprise to prevent any unauthorized data disclosure in an intentional or an unintentional manner. As data that is confidential is able to reside on various computing devices and move through several network access points or different types of social networks such as emails. Leakage of emails has been defined as if the email either deliberately or accidentally goes to an addressee to whom it should not be addressed. Data Leak Prevention (DLP) is the technique or product that tries mitigating threats to data leaks. In this work, the technique of clustering will be combined with the frequency of the term or the inverse document frequency in order to identify the right centroids for analysing the various emails that are communicated among members of an organization. Every member will fit in to various topic clusters and one such topic cluster can also comprise of several members in the organization who have not communicated with each other earlier. At the time when a new email is composed, every addressee will be categorized to be a potential leak recipient or one that is legal. Such classification was based on the emails sent among the sender and the receiver and also on their topic clusters. The work had investigated the technique of K-Means clustering and also proposed a Tabu - K-Means (TABU-KM) technique of clustering to identify points of optimal clustering. The proposed TABU-KM optimizes the K-Means clustering. Experimental results demonstrated that the proposed method achieves higher True Positive Rate (TPR) for known and unknown recipient and lower False Positive Rate (FPR) for known and unknown recipient

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“…Extensions of [1] are described in [2], [10]. An improved approach is presented in [11], [12], which analyzes group communication for preventing information leakage via emails. For an email to be sent, a recipient is approved if the email's content belongs to at least one of the topics common to the sender and the recipient.…”

Section: Related Workmentioning

confidence: 99%

Towards misdirected email detection for preventing information leakage

Liu

Shi

et al. 2014

2014 IEEE Symposium on Computers and Communications (ISCC)

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With the widespread usage of emails, information leakage via misdirected emails becomes a practical and disastrous problem, which should be addressed at all costs. Prior methods have two limitations: privacy issue as relying on email contents to work, and high cost as building too many targeted models. In this paper, we reduce the detection of misdirected emails to a binary classification problem, and build only a universal model to detect misdirected emails. We introduce some representative features that can vividly describe the characteristics of misdirected emails while not infringe users' privacy. Then we design novel algorithms to get these features. The random forest classifier is chosen to perform the detecting task. Experimental results show that our work is able to detect misdirected emails with 89% precision rate and 82% recall rate in average.

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Analyzing group communication for preventing data leakage via email

Cited by 24 publications

References 3 publications

Data Leakage Prevention System: A Systematic Report

Data Leakage Prevention System: A Systematic Report

Data Leak Identification in Social Networks using K Means Clustering & Tabu K Means Clustering

Towards misdirected email detection for preventing information leakage

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