Electronic Health Record Security Based on Ensemble Classification of Keystroke Dynamics

Wesołowski, T; Porwik, Piotr; Doroz, Rafał

doi:10.1080/08839514.2016.1193715

Cited by 18 publications

(10 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The proposed architecture of the classification module for keystroke dynamics based verification assumes the use of four single classifiers in an ensemble: C4.5, Bayesian Network, Support Vector Machine, Random Forest. Those classifiers were chosen because of their high accuracy confirmed in [17]. The aim of the first stage of this research was to determine an optimal values of parameters τ , τ and k * .…”

Section: Resultsmentioning

confidence: 99%

“…The introduced approach is based on the fusion of keystroke dynamics and knuckle analysis. For the purpose of the fusion the biometric user verification methods were chosen that according to the [17] for keystroke based approach and [3] for finger knuckle pattern analysis perform better than other methods described in literature. User activity should be verified continuously, in the background, while a user is performing his everyday tasks.…”

Section: Proposed Biometric User Verification Systemmentioning

confidence: 99%

“…User's profile Φ uid is stored in the database to be used by a classification based intrusion detection module. User profiling method based on keystroke analysis used in this proposed approach is described in details in [17]. The keystroke verification system is based on three ensembles of classifiers EC a , a = 1, ..., 3.…”

Section: Keystroke Based Subsystemmentioning

confidence: 99%

“…The number of attacks is increasing year by year. Only within the years 2014 and 2015 the amount of individuals affected by security breaches, where sensitive personal data such as electronic health records were stolen, increased hundred times [17]. The attacks themselves are becoming more and more sophisticated.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Keystroke Dynamics and Finger Knuckle Imaging Fusion for Continuous User Verification

Wesołowski

Doroz

Wróbel

et al. 2017

Computer Information Systems and Industrial Management

Self Cite

View full text Add to dashboard Cite

Part 2: Biometrics and Pattern Recognition ApplicationsInternational audienceThe paper presents a novel user identity verification method based on fusion of keystroke dynamics and knuckle images analysis. In our solution the verification is performed by an ensemble of classifiers used to verify the identity of an active user. A proposed verification module works on a database which comprises of data representing keystroke dynamics and knuckle images. The usability of the introduced approach was tested experimentally. The obtained results confirm that the proposed fusion method gives better results than the use of a single biometric feature only. For this reason our method can be used for increasing a protection level of computer resources against impostors. The paper presents preliminary research conducted to assess the potential of biometric methods fusion

show abstract

Section: Resultsmentioning

confidence: 99%

Section: Proposed Biometric User Verification Systemmentioning

confidence: 99%

Section: Keystroke Based Subsystemmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Keystroke Dynamics and Finger Knuckle Imaging Fusion for Continuous User Verification

Wesołowski

Doroz

Wróbel

et al. 2017

Computer Information Systems and Industrial Management

Self Cite

View full text Add to dashboard Cite

show abstract

“…Similarity measure compares security practices with other healthcare professionals who have similar security practices. Observed measure is a control approach of obtaining the ground truth whereby some users were observed to conduct their security practices under a supervised, required security practices [39]. But the historical data basically relied on past records with a trust that, the data is reliable enough for training set.…”

Section: Input Data Features Sources Ground Truth Data Format and Nature Of Datamentioning

confidence: 99%

Data-Driven and Artificial Intelligence (AI) Approach for Modelling and Analyzing Healthcare Security Practice: A Systematic Review

Yeng

Nweke

Woldaregay

et al. 2020

Advances in Intelligent Systems and Computing

View full text Add to dashboard Cite

Data breaches in healthcare continue to grow exponentially, calling for a rethinking into better approaches of security measures towards mitigating the menace. Traditional approaches including technological measures, have significantly contributed to mitigating data breaches but what is still lacking is the development of the "human firewall," which is the conscious care security practices of the insiders. As a result, the healthcare security practice analysis, modeling and incentivization project (HSPAMI) is geared towards analyzing healthcare staffs' security practices in various scenarios including big data. The intention is to determine the gap between staffs' security practices and required security practices for incentivization measures. To address the state-of-the art, a systematic review was conducted to pinpoint appropriate AI methods and data sources that can be used for effective studies. Out of about 130 articles, which were initially identified in the context of human-generated healthcare data for security measures in healthcare, 15 articles were found to meet the inclusion and exclusion criteria. A thorough assessment and analysis of the included article reveals that, KNN, Bayesian Network and Decision Trees (C4.5) algorithms were mostly applied on Electronic Health Records (EHR) Logs and Network logs with varying input features of healthcare staffs' security practices. What was found challenging is the performance scores of these algorithms which were not sufficiently outlined in the existing studies.

show abstract

Edge Real-Time Medical Data Segmentation for IoT Devices with Computational and Memory Constrains

Bernaś

Płaczek

Sapek

2017

Computational Collective Intelligence

View full text Add to dashboard Cite

Electronic Health Record Security Based on Ensemble Classification of Keystroke Dynamics

Cited by 18 publications

References 22 publications

Keystroke Dynamics and Finger Knuckle Imaging Fusion for Continuous User Verification

Keystroke Dynamics and Finger Knuckle Imaging Fusion for Continuous User Verification

Data-Driven and Artificial Intelligence (AI) Approach for Modelling and Analyzing Healthcare Security Practice: A Systematic Review

Edge Real-Time Medical Data Segmentation for IoT Devices with Computational and Memory Constrains

Contact Info

Product

Resources

About