Protecting Electronic Health Records in Transit and at Rest

Ulybyshev, Denis; Bare, Christian; Bellisario, Kristen; Kholodilo, Vadim; Northern, Bradley; Solanki, Abhijeet; O’Donnell, Timothy J.

doi:10.1109/cbms49503.2020.00091

Cited by 10 publications

(16 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We also plan to improve the performance of our solution by employing a caching technique and batch processing. To store configurations used in each microservice in our framework, we plan to employ a "PROSPECD" secure data container [43], which supports fine-grained role-based and attribute-based access control. This would also be integrated with a Relational Database Management System.…”

Section: Future Workmentioning

confidence: 99%

VERCASM-CPS: Vulnerability Analysis and Cyber Risk Assessment for Cyber-Physical Systems

et al. 2021

Self Cite

View full text Add to dashboard Cite

Since Cyber-Physical Systems (CPS) are widely used in critical infrastructures, it is essential to protect their assets from cyber attacks to increase the level of security, safety and trustworthiness, prevent failure developments, and minimize losses. It is necessary to analyze the CPS configuration in an automatic mode to detect the most vulnerable CPS components and reconfigure or replace them promptly. In this paper, we present a methodology to determine the most secure CPS configuration by using a public database of cyber vulnerabilities to identify the most secure CPS components. We also integrate the CPS cyber risk analysis with a Controlled Moving Target Defense, which either replaces the vulnerable CPS components or re-configures the CPS to harden it, while the vulnerable components are being replaced. Our solution helps to design a more secure CPS by updating the configuration of existing CPS to make them more resilient against cyber attacks. In this paper, we will compare cyber risk scores for different CPS configurations and show that the Windows® 10 build 20H2 operating system is more secure than Linux Ubuntu® 20.04, while Red Hat® Enterprise® Linux is the most secure in some system configurations.

show abstract

Section: Future Workmentioning

confidence: 99%

VERCASM-CPS: Vulnerability Analysis and Cyber Risk Assessment for Cyber-Physical Systems

et al. 2021

Self Cite

View full text Add to dashboard Cite

show abstract

“…PROSPECD container used to store and transfer blacklisted malicious domain names, is presented in [6]. Compared to the privacy-preserving data dissemination concept as proposed by Lilien and Bhargava [28], it has the following features:…”

Section: Related Workmentioning

confidence: 99%

“…• Detection of several types of data leakages that can be made by authorized entities to unauthorized ones; • Enforcement of access control policies either on a central server or locally on a client's side in a Microsoft Excel ®1 Add-in or in a cross-platform application [6].…”

Section: Related Workmentioning

confidence: 99%

“…A Secure Data Container, proposed in [34] to store device state information, only supports centralized policy enforcement. PROSPECD supports both centralized and local policy enforcement mechanisms [6].…”

Section: Related Workmentioning

confidence: 99%

“…In addition, we devise a secure container to store and transfer the blacklists of malicious domain names in encrypted form as a Protected Spreadsheet Container with Data (PROSPECD), presented in [6]. PROSPECD provides confidentiality and integrity of the blacklists so that they can be used as training data to build a secure model.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Improving DGA-Based Malicious Domain Classifiers for Malware Defense with Adversarial Machine Learning

Yılmaz¹,

Siraj²,

Ulybyshev³

2021

Preprint

Self Cite

View full text Add to dashboard Cite

Domain Generation Algorithms (DGAs) are used by adversaries to establish Command and Control (C&C) server communications during cyber attacks. Blacklists of known/identified C&C domains are often used as one of the defense mechanisms. However, since blacklists are static and generated by signature-based approaches, they can neither keep up nor detect never-seen-before malicious domain names. Due to this shortcoming of blacklist domain checking, machine learning algorithms have been used to address the problem to some extent. However, when training is performed with limited datasets, the algorithms are likely to fail in detecting new DGA variants. To mitigate this weakness, we successfully applied a DGA-based malicious domain classifier using the Long Short-Term Memory (LSTM) method with a novel feature engineering technique. Our model's performance shows a higher level of accuracy compared to a previously reported model from prior research. Additionally, we propose a new method using adversarial machine learning to generate never-before-seen malware-related domain families that can be used to illustrate the shortcomings of machine learning algorithms in this regard. Next, we augment the training dataset with new samples such that it makes training of the machine learning models more effective in detecting never-before-seen malicious domain name variants. Finally, to protect blacklists of malicious domain names from disclosure and tampering, we devise secure data containers that store blacklists and guarantee their protection against adversarial access and modifications.

show abstract

SEMAFORE: Secure Mobile Field Diagnostics for Cyber-Physical Systems

Seyler

Ulybyshev

2023

Lecture Notes in Networks and Systems

View full text Add to dashboard Cite

Protecting Electronic Health Records in Transit and at Rest

Cited by 10 publications

References 10 publications

VERCASM-CPS: Vulnerability Analysis and Cyber Risk Assessment for Cyber-Physical Systems

VERCASM-CPS: Vulnerability Analysis and Cyber Risk Assessment for Cyber-Physical Systems

Improving DGA-Based Malicious Domain Classifiers for Malware Defense with Adversarial Machine Learning

SEMAFORE: Secure Mobile Field Diagnostics for Cyber-Physical Systems

Contact Info

Product

Resources

About