Proposing a modeling framework for minimizing security vulnerabilities in IoT systems in the healthcare domain

Wortman, Paul A.; Tehranipoor, Fatemeh; Karimian, Nima; Chandy, John A.

doi:10.1109/bhi.2017.7897236

Cited by 12 publications

(8 citation statements)

References 11 publications

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“…Approaches which include IoT and security by design are e.g. [9] and [10]. Reference [9] proposes the application of AADL to be able to model all security related information.…”

Section: Background and Related Workmentioning

confidence: 99%

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Design Optimization of IoT Models: Structured Safety and Security Flaw Identification

Rauscher

Bauer

2020

Lecture Notes in Business Information Processing

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More and more devices are being interconnected, thus extending the use of Internet of Things (IoT) systems. However, the larger the networks are the more vulnerable and inscrutable they become. This is a significant challenge especially when IoT is used in safety-and security-critical areas. In these areas, a flawless architecture must be guaranteed already in the design phase. Therefore, a structured possibility is needed to scan models completely for vulnerabilities as early as possible. We developed a pattern recognition framework (PRF) that enables the definition of design patterns and anti-patterns. These patterns are used for a holistic and automated identification of flaws in IoT models during design phase and enable a design optimization.

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“…Approaches which include IoT and security by design are e.g. [9] and [10]. Reference [9] proposes the application of AADL to be able to model all security related information.…”

Section: Background and Related Workmentioning

confidence: 99%

“…[9] and [10]. Reference [9] proposes the application of AADL to be able to model all security related information. Though, their framework doesn't include automated flaw identification possibilities.…”

Section: Background and Related Workmentioning

confidence: 99%

Design Optimization of IoT Models: Structured Safety and Security Flaw Identification

Rauscher

Bauer

2020

Lecture Notes in Business Information Processing

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“…Through our solution one would know that the information produced from an IoT is trustworthy, that the individuals accessing information can be properly identified, and that the exchange of sensitive biological signals across a network is secure. Furthermore, we showed that the hardwarebased solutions are low-cost and feasibly implementable on existing resource constrained IoT systems (sensors, wearables, smart devices) [19].…”

Section: Hardware Security Architecture For Thementioning

confidence: 99%

Towards Implementation of Robust and Low-Cost Security Primitives for Resource-Constrained IoT Devices

Tehranipoor

2018

Preprint

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In recent years, due to the trend in globalization, system integrators have had to deal with integrated circuit (IC)/intellectual property (IP) counterfeiting more than ever. These counterfeit hardware issues counterfeit hardware that have driven the need for more secure chip authentication. High entropy random numbers from physical sources are a critical component in authentication and encryption processes within secure systems [6]. Secure encryption is dependent on sources of truly random numbers for generating keys, and there is a need for an on chip random number generator to achieve adequate security. Furthermore, the Internet of Things (IoT) adopts a large number of these hardware-based security and prevention solutions in order to securely exchange data in resource efficient manner. In this work, we have developed several methodologies of hardware-based random functions in order to address the issues and enhance the security and trust of ICs: a novel DRAM-based intrinsic Physical Unclonable Function (PUF) [13] for system level security and authentication along with analysis of the impact of various environmental conditions, particularly silicon aging; a DRAM remanence based True Random Number Generation (TRNG) to produce random sequences with a very low overhead; a DRAM TRNG model using its startup value behavior for creating random bit streams; an efficient power supply noise based TRNG model for generating an infinite number of random bits which has been evaluated as a cost effective technique; architectures and hardware security solutions for the Internet of Things (IoT) environment. Since IoT devices are heavily resource constrained, our proposed designs can alleviate the concerns of establishing trustworthy and security in an efficient and low-cost manner.

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“…Consequently, the principles of safety/security by design are not fulfilled. [23] and [24] present analyses which are conducted on architecture level. [23] analyzes failure modes and effects by using SysML to evaluate medical devices and to prove the need of analysis in an early design process phase.…”

Section: Related Workmentioning

confidence: 99%

“…[23] analyzes failure modes and effects by using SysML to evaluate medical devices and to prove the need of analysis in an early design process phase. [24] proposes the usage of Architecture Analysis and Design Language (AADL) to be able to model an IoT system with needed details for identifying security vulnerabilities.…”

Section: Related Workmentioning

confidence: 99%

Safety and Security Architecture Analyses Framework for the Internet of Things of Medical Devices

Rauscher

Bauer

2018

2018 IEEE 20th International Conference on E-Health Networking, Applications and Services (Healthcom)

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Internet of Things (IoT) is spreading increasingly in different areas of application. Accordingly, IoT also gets deployed in health care including ambient assisted living, telemedicine or medical smart homes. However, IoT also involves risks. Next to increased security issues also safety concerns are occurring. Deploying health care sensors and utilizing medical data causes a high need for IoT architectures free of vulnerabilities in order to identify weak points as early as possible. To address this, we are developing a safety and security analysis approach including a standardized meta model and an IoT safety and security framework comprising a customizable analysis language.

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Proposing a modeling framework for minimizing security vulnerabilities in IoT systems in the healthcare domain

Cited by 12 publications

References 11 publications

Design Optimization of IoT Models: Structured Safety and Security Flaw Identification

Design Optimization of IoT Models: Structured Safety and Security Flaw Identification

Towards Implementation of Robust and Low-Cost Security Primitives for Resource-Constrained IoT Devices

Safety and Security Architecture Analyses Framework for the Internet of Things of Medical Devices

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