M-Score: A Misuseability Weight Measure

Harel, Amir; Shabtai, Asaf; Rokach, Lior; Elovici, Yuval

doi:10.1109/tdsc.2012.17

Cited by 38 publications

(39 citation statements)

References 23 publications

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“…To our knowledge, only two solutions [21,22] address the problem of quantifying the severity of data leakages in database environment. In particular, these solutions aim to provide an estimation of the damage that data leakages cause to the organization hosting the data.…”

Section: Computing the Severity Of Alertsmentioning

confidence: 99%

“…In the remainder of this section, we present an overview of the data leakage quantification approach presented in [22]. As shown in [22], this approach is able to discriminate alerts more accurately compared to the approach presented in [21].…”

Section: Computing the Severity Of Alertsmentioning

confidence: 99%

“…In particular, the sensitivity of leaked data is not considered in the calculation of the severity of a leakage. Only two proposals [21,22] use semantic information to compute the severity of data leakages. These proposals measure the severity of leakages in database environment on the basis of the amount and sensitivity of the leaked data.…”

Section: Related Workmentioning

confidence: 99%

“…In particular, to calculate the risk level of data leakages we extend and integrate a behavioral-based anomaly detection solution [3] for assessing the anomaly level of alerts and a data leakage quantification solution [22] for assessing their severity. Compared to the other solution for assessing their severity [21], the solution in [22] provides a more accurate discrimination of alerts based on their severity. On the other hand, the behavioral-based anomaly detection system presented in [3] offers a number of advantages compared to other anomaly detection systems.…”

Section: Related Workmentioning

confidence: 99%

“…In particular, some approaches [3,20] quantify alerts with respect to their anomaly level, allowing security officers to focus on the most abnormal cases. Other approaches [21,22] aim to provide an estimation of the damage caused by data leakages on the basis of the sensitivity and amount of leaked information, allowing security administrators to focus on the most severe data breaches. These approaches, however, only provide a "partial view" on the criticality of alerts, which may lead to a ranking that does not reflect their actual criticality level.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

An anomaly analysis framework for database systems

Vavilis

Egner

Petković

et al. 2015

Computers & Security

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Section: Computing the Severity Of Alertsmentioning

confidence: 99%

Section: Computing the Severity Of Alertsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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An anomaly analysis framework for database systems

Vavilis

Egner

Petković

et al. 2015

Computers & Security

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Form‐based security in mobile health data collection systems

Katarahweire

Mughal

Ngubiri

2021

Security and Privacy

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Mobile Health Data Collection Systems (MHDCS) use electronic forms for data collection in place of paper surveys. Data is entered into the electronic forms by community health workers with smartphones. These systems are used in clinical trials and continuous monitoring of participants. Data collected in MHDCS is diverse with varying sensitivity levels. This therefore calls for the application of different security mechanisms to protect the data. In this paper, a data sensitivity classification model is proposed. It determines the sensitivity levels of form attributes depending on the context and sensitive parameters. The electronic form is the starting point of the data collection process. We augment the form with sensitivity levels and a mapping of security mechanisms to the sensitivity levels is made. Through threat modeling, we identify potential threats and possible mitigations. We demonstrate the feasibility of our approach by prototyping the proposed model into the Open Data Kit tool suite. Tests show that the security mitigations specified during form design are executed once the secure form is loaded on the mobile device during data collection. Our ultimate aim is to facilitate the addition of security requirements into the data collection processes right from the start of the design process.

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An extensive review of data security infrastructure and legislature

Bhavnani

Bardia

Modi

et al. 2021

Security and Privacy

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The last few decades have witnessed technology progress with leaps and bounds leading to the creation and global adoption of different types of digital devices and platforms that can make personalized recommendations to an individual. One of the consequences of the ubiquity of these devices is the daily generation of data in large quantities. This data includes the sensitive data of an individual as well as multinational organizations and therefore, it must be always kept confidential to prevent its theft and misuse by malicious parties. However, the large volumes of data generated make it difficult to create a robust security solution to safeguard the data from different types of cyberattacks. The paper contributes to the data security industry by consolidating numerous data security algorithms that change with the infrastructure that the data is amid and also outlines the regulations surrounding it. This review paper also aims to highlight the benefits and drawbacks of the security algorithms proposed by researchers to encourage further discussion and consideration of the algorithms for their potential implementation in appropriate domains and also inspire them to develop more powerful and robust security algorithms to cover the drawbacks of the existing ones.

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M-Score: A Misuseability Weight Measure

Cited by 38 publications

References 23 publications

An anomaly analysis framework for database systems

An anomaly analysis framework for database systems

Form‐based security in mobile health data collection systems

An extensive review of data security infrastructure and legislature

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