Comparative evaluation of algorithms for effective data leakage detection

Kumar, Ajay; Goyal, Ankit; Kumar, Ashwani; Chaudhary, Navneet Kumar; Kamath, S. Sowmya

doi:10.1109/cict.2013.6558085

Cited by 9 publications

(8 citation statements)

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“…In our review, we observed that quite a large number of countermeasures are either not evaluated at all ( [23], [40], [45] , [48] , [49] , [62] , [75] , [88] , [91] , [67] , [92] , [105] , [110], [111], [114], [137], [141], [154], , [160], [174], , [175]) or evaluated weakly ( [60], [70], [100], [109], [132], [138], [147], [158], [161], [165], [171]). We consider an evaluation as weak evaluation when the system is evaluated with a small dataset (e.g.…”

Section: Discussionmentioning

confidence: 99%

“…Kumar et al [171] compare a number of data leakage detection and data allocation strategies for this same 'traitors tracing' problem, though note that the leakage may be accidental. In modelling the problem, they address allocation where only a sample of a total dataset is required.…”

Section: Probabilistic Forensicsmentioning

confidence: 99%

“…Grier [166] In use A method to find out whether a file has been copied or not based on examination of file system. Patel & Singh [167] In use A technique for providing ease in differentiating between copy operation and rest of the operations Kavallaris & Katos [168] In use An approach for preventing data being copied to an un-authorized USB device Papadimitriou & Garcia-Molina [169] In use A model for identification of guilty trusted party who has leaked data Wang et al [170] In use A technique based on probabilistic analysis to identify the insider who leaked data Kumar et al [171] In use An approach for smart data allocation which helps in tracing the data leaker Krishnan et al [172] In use An approach for post-exfiltration analysis to identify what, when and how based on reconstruction of events using logged data from the hypervisor Yu et al [173] In use A technique for identification of the path used for exfiltration of data using statistical analysis Xu et al [174] In use A technique based on probabilistic analysis to identify path and responsible party for data leakage Counter-Intelligence Operation Deng et al [177] In use An approach for tracing the traitor who illegally distribute access credentials of a cloud application Ikegami & Yamauch [175] In use An approach that detects, prevents and tracks the attacker Alexander et al [176] In use A technique for hiding detective and investigative software from the attacker using rootkit Fig. 13 shows the mapping of the attack vectors identified in section 3 onto the countermeasures reported in section 4.3.…”

Section: Probabilistic Forensicsmentioning

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: Probabilistic Forensicsmentioning

confidence: 99%

Section: Probabilistic Forensicsmentioning

confidence: 99%

See 1 more Smart Citation

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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“…To identify the guilty agent, [43], [44], [45] preferred to choose probabilistic approach which implements a variety of data distribution strategies. This improves distributor's chances of identifying leaker.…”

Section: Related Workmentioning

confidence: 99%

Layer-based Privacy and Security Architecture for Cloud Data Sharing

Gupta

Singh

2019

JCOMSS

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The management of data while maintaining its utility and preservation of security scheme is a matter of concern for the cloud owner. In order to minimize the overhead at cloud service provider of applying security over each document and then send it to the client, we propose a layered architecture. This approach maintains security of the sensitive document and privacy of its data sensitivity. To make a balance between data security and utility, the proposed approach categorizes the data according to its sensitivity. Perseverance of various categorization requires different algorithmic schemes. We set up a cloud distributed environment where data is categorized into four levels of sensitivity; public, confidential, secret, top secret and a different approach has been used to preserve the security at each level. At the most sensitive layers i.e. secret and top secret data, we made a provision to detect the faulty node that is responsible for data leakage. Finally, experimental analysis is carried out to analyze the performance of the layered approach. The experimental results show that time taken (in ms) in processing 200 documents of size 20 MB is 437, 2239, 3142, 3900 for public, confidential, secret and top secret data respectively when the documents are distributed among distinct users, which proves the practicality of the proposed approach.

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“…Sensitive data of institutions include intellectual, financial, personal, and other sorts of information, depending on the organization or business' aim [1]. A study carried out by InsightExpress in the US, requested by Cisco [2], reveals that employees' behavior represents the major source of data loss threat mainly related to intellectual property or client's sensitive data.…”

Section: Introductionmentioning

confidence: 99%

Data Leakage Detection Using Dynamic Data Structure and Classification Techniques

Maldonado¹,

Byron²

2015

Inge CUC

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Abstract-Data leakage is a permanent problem in public and private institutions around the world; particularly, identifying the information leakage efficiently. In order to solve this problem, this paper poses an adaptable data structure based on human behavior using all the activities executed within the computer system. When applying this structure, the normal behavior is modeled for each user, so in this way, detects any abnormal behavior in real time. Moreover, this structure enables the application of several classification techniques such as decision trees (C4.5), UCS, and Naive Bayes, these techniques have proven efficient outcomes in intrusion detection. In the testing of this model, a scenario demonstrating the proposal's effectiveness with real information from a government institution was designed so as to establish future lines of work.Keywords-Data Leakage, Data Structure, Decision Tree C4.5, UCS, Naive Bayes Resumen-La fuga de información es un problema que está presente en instituciones públicas y privadas alrededor del mundo. El principal problema que se presenta es identificar de forma eficiente el filtrado de la información. Para solucionar este problema en el presente trabajo desarrolla una estructura de datos adaptable al comportamiento humano, utilizando como base las actividades ejecutadas dentro del sistema informático. Al aplicar esta estructura se modela un comportamiento NORMAL de cada uno de los usuarios y de esta manera detecta cualquier comportamiento ANÓ-MALO en tiempo real. Además, permite la aplicación de varias técnicas de clasificación como los árboles de decisión (C4.5), UCS y Naive Bayes las cuales han demostrado un eficiente resultado en la detección de intrusiones. Para probar este modelo se ha diseñado un escenario que sirve para demostrar la validez de la propuesta con información real de una institución gubernamental y para acreditar líneas futuras de trabajo.Palabras Clave-Fuga de Información, Estructura de Datos,Árbol de decisión C4.5, UCS , Naive Bayes.

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Comparative evaluation of algorithms for effective data leakage detection

Cited by 9 publications

References 5 publications

Data exfiltration: A review of external attack vectors and countermeasures

Data exfiltration: A review of external attack vectors and countermeasures

Layer-based Privacy and Security Architecture for Cloud Data Sharing

Data Leakage Detection Using Dynamic Data Structure and Classification Techniques

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