Data leakage prevention system with time stamp

Subhashini, Peneti; Rani, B. Padmaja

doi:10.1109/icices.2016.7518934

Cited by 6 publications

(5 citation statements)

References 2 publications

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“…One important aspect for such a framework should be the consideration and development of a comprehensive dataset. We found that there exist some datasets ( [119], [121], [150], [162]) for evaluating data exfiltration countermeasures, however, these datasets are small in size and limited in quality. These limitations make the datasets unable to reflect the actual strengths and weaknesses of the proposed countermeasure.…”

Section: Discussionmentioning

confidence: 99%

“…Peneti et al [121] present an approach for data leakage prevention based on timestamp. The proposed approach consists of two phases: (1) Learning phase: all documents of an organization are collected to create clusters of data, calculate scores for key terms in the documents and assign a timestamp to each document based on the organization's schedule (2) Detection phase: the outgoing documents' timestamps are compared with the timestamps of the documents in the identified clusters; based on the results of the comparison, a decision is made whether or not the documents are allowed to be sent.…”

Section: Inspecting Unencrypted Trafficmentioning

confidence: 99%

“…Peneti et al [121] In transit Attaches a time stamp with each document and later monitors the time stamp of each outgoing document. If time stamp of outgoing document is old, so it is allowed to leave else transmission is blocked.…”

Section: Malware Attacksmentioning

confidence: 99%

“…Privacy, Encrypted Traffic, and Accuracy) are closely related. In our review, we found a number of studies such as [105], [108], [117], [119], [121], [169] that are set in violation to users' privacy. These countermeasures directly monitor the outgoing network traffic generated by users and scan it for detecting sensitive information.…”

Section: Privacy Encrypted Traffic and Accuracymentioning

confidence: 99%

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Data exfiltration: A review of external attack vectors and countermeasures

Ullah

Edwards

Ramdhany

et al. 2018

Journal of Network and Computer Applications

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Context: One of the main targets of cyber-attacks is data exfiltration, which is the leakage of sensitive or private data to an unauthorized entity. Data exfiltration can be perpetrated by an outsider or an insider of an organization. Given the increasing number of data exfiltration incidents, a large number of data exfiltration countermeasures have been developed. These countermeasures aim to detect, prevent, or investigate exfiltration of sensitive or private data. With the growing interest in data exfiltration, it is important to review data exfiltration attack vectors and countermeasures to support future research in this field. Objective: This paper is aimed at identifying and critically analysing data exfiltration attack vectors and countermeasures for reporting the status of the art and determining gaps for future research. Method: We have followed a structured process for selecting 108 papers from seven publication databases. Thematic analysis method has been applied to analyse the extracted data from the reviewed papers. Results: We have developed a classification of (1) data exfiltration attack vectors used by external attackers and (2) the countermeasures in the face of external attacks. We have mapped the countermeasures to attack vectors. Furthermore, we have explored the applicability of various countermeasures for different states of data (i.e., in use, in transit, or at rest). Conclusion: This review has revealed that (a) most of the state of the art is focussed on preventive and detective countermeasures and significant research is required on developing investigative countermeasures that are equally important; (b) Several data exfiltration countermeasures are not able to respond in real-time, which specifies that research efforts need to be invested to enable them to respond in real-time (c) A number of data exfiltration countermeasures do not take privacy and ethical concerns into consideration, which may become an obstacle in their full adoption (d) Existing research is primarily focussed on protecting data in 'in use' state, therefore, future research needs to be directed towards securing data in 'in rest' and 'in transit' states (e) There is no standard or framework for evaluation of data exfiltration countermeasures. We assert the need for developing such an evaluation framework.

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Section: Discussionmentioning

confidence: 99%

Section: Inspecting Unencrypted Trafficmentioning

confidence: 99%

Section: Malware Attacksmentioning

confidence: 99%

Section: Privacy Encrypted Traffic and Accuracymentioning

confidence: 99%

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Data exfiltration: A review of external attack vectors and countermeasures

Ullah

Edwards

Ramdhany

et al. 2018

Journal of Network and Computer Applications

View full text Add to dashboard Cite

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“…DLPSs use the statistical/ analytical approach, data fingerprinting, regular expressions on context and content of data to identify and avoid unauthorized access to data [10]. DLP approach performs deep content analysis and observes the data access by users to discover improper usage [11][12][13]. DLP systems employ a model using either knowledge of an expert or may train/learn from available past records (Fig.…”

Section: Introductionmentioning

confidence: 99%

A Forecasting-Based DLP Approach for Data Security

Gupta

Kush

2021

Data Analytics and Management

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Sensitive data leakage is the major growing problem being faced by enterprises in this technical era. Data leakage causes severe threats for organization of data safety which badly affects the reputation of organizations. Data leakage is the flow of sensitive data/information from any data holder to an unauthorized destination. Data leak prevention (DLP) is set of techniques that try to alleviate the threats which may hinder data security. DLP unveils guilty user responsible for data leakage and ensures that user without appropriate permission cannot access sensitive data and also provides protection to sensitive data if sensitive data is shared accidentally. In this paper, data leakage prevention (DLP) model is used to restrict/grant data access permission to user, based on the forecast of their access to data. This study provides a DLP solution using data statistical analysis to forecast the data access possibilities of any user in future based on the access to data in the past. The proposed approach makes use of renowned simple piecewise linear function for learning/training to model. The results show that the proposed DLP approach with high level of precision can correctly classify between users even in cases of extreme data access.

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Data Leakage Prevention System for Smart Grid

Jiang

Deng

2020

Communications in Computer and Information Science

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Data leakage prevention system with time stamp

Cited by 6 publications

References 2 publications

Data exfiltration: A review of external attack vectors and countermeasures

Data exfiltration: A review of external attack vectors and countermeasures

A Forecasting-Based DLP Approach for Data Security

Data Leakage Prevention System for Smart Grid

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