False Data Injection Attacks in Healthcare

Ahmed, Mohiuddin; Ullah, Abu S. S. M. Barkat

doi:10.1007/978-981-13-0292-3_12

Cited by 21 publications

(26 citation statements)

References 21 publications

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“…In this section, we talk about some of the recently proposed FDIA countermeasures (Ahmed and Ullah 2018;He et al 2017;Liu et al 2014;Chaojun et al 2015). The countermeasures are developed mainly for smart grid applications; however, with little efforts, they can be adapted for other domains.…”

Section: Methods and Countermeasures To Defend Against Fdiamentioning

confidence: 99%

“…• Deep learning (Ahmed and Islam 2020) is utilized to learn the FDIA characteristics from the historical data and the learned features are used to identify FDIA. The proposed convolutional deep belief network can detect unobserved FDIA in real-time by exploring the temporal behaviors (Ahmed and Ullah 2018;He et al 2017). • Kullback-leibler distance (KLD) is exploited to distinguish between normal measurements and false data injected measurements.…”

Section: Key Methods For Various Countermeasuresmentioning

confidence: 99%

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False data injection attack (FDIA): an overview and new metrics for fair evaluation of its countermeasure

Ahmed

Pathan

2020

Complex Adapt Syst Model

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Introduction and background The Internet has a great impact on our lives. Based on similar concept of connecting things and objects in a virtual realm, we currently see the emergence of Internet of Everything (IoE). It has also initiated a plethora of Complex Adaptive Systems (CASs) such as wireless sensor networks, edge computing, smart grid and many others which can come together to perform some common task or could be used to attain some particular objective. Technically, a CAS is a system for which the entire system's behavior cannot be fully understood by the full knowledge of the operations and characteristics of individual parts in it. A CAS is often distinguished by its characteristics like self-similarity, self-organization, complexity, and emergence. Today, many types of complex adaptive systems are connected with the regular cyberspace and hence, while we are enjoying great level of technological advancements, we are increasingly becoming more prone to a wide variety of cyber attacks. Manipulation of data within an individual part in this

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Section: Methods and Countermeasures To Defend Against Fdiamentioning

confidence: 99%

Section: Key Methods For Various Countermeasuresmentioning

confidence: 99%

False data injection attack (FDIA): an overview and new metrics for fair evaluation of its countermeasure

Ahmed

Pathan

2020

Complex Adapt Syst Model

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“…Initial system setup covers most of the expenditure of PoF, whereas other studies [37,39,40] cost both during setup and use. Additionally, PoF saves extra cost caused for mishandling of EHRs [10,11].…”

Section: Costmentioning

confidence: 99%

“…US-based identity theft resource center (ITRC) [10] announced businesses (55%) and healthcare industries (23.7%) as the top two identity breaching sectors from 2005 to 2017 (total 1579 breaches in the USA, 2017). False data injection attack in healthcare has been a widely discussed topic in clinical data security and the privacy domain [11].…”

Section: Introductionmentioning

confidence: 99%

Proof-of-Familiarity: A Privacy-Preserved Blockchain Scheme for Collaborative Medical Decision-Making

et al. 2019

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The current healthcare sector is facing difficulty in satisfying the growing issues, expenses, and heavy regulation of quality treatment. Surely, electronic medical records (EMRs) and protected health information (PHI) are highly sensitive, personally identifiable information (PII). However, the sharing of EMRs, enhances overall treatment quality. A distributed ledger (blockchain) technology, embedded with privacy and security by architecture, provides a transparent application developing platform. Privacy, security, and lack of confidence among stakeholders are the main downsides of extensive medical collaboration. This study, therefore, utilizes the transparency, security, and efficiency of blockchain technology to establish a collaborative medical decision-making scheme. This study considers the experience, skill, and collaborative success rate of four key stakeholders (patient, cured patient, doctor, and insurance company) in the healthcare domain to propose a local reference-based consortium blockchain scheme, and an associated consensus gathering algorithm, proof-of-familiarity (PoF). Stakeholders create a transparent and tenable medical decision to increase the interoperability among collaborators through PoF. A prototype of PoF is tested with multichain 2.0, a blockchain implementing framework. Moreover, the privacy of identities, EMRs, and decisions are preserved by two-layer storage, encryption, and a timestamp storing mechanism. Finally, superiority over existing schemes is identified to improve personal data (PII) privacy and patient-centric outcomes research (PCOR).

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“…The issues range from malware attacks that compromise the integrity of systems and confidentiality of patients to distributed denial-of-service (DDoS) which can disrupt facilities ability to provide patient care. In healthcare systems, for instance, cyber-attacks like Ransomware can have ramifications beyond financial loss and breach of privacy [8]. Earlier this year, hackers broke into the databases of Community Health Systems (CHS), one of the largest hospital groups in the United States, and accessed personal health information, name, address and personal data including social security numbers from around 4.5 million patients.…”

Section: Introductionmentioning

confidence: 99%

Privacy-Preserving Data Sharing using Multi-layer Access Control Model in Electronic Health Environment

Chenthara

Ahmed

Whittaker

2019

ICST Transactions on Scalable Information Systems

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Electronic Health Data (EHD) is an emerging health information exchange model that facilitates healthcare providers and patients to efficiently store and share their private healthcare information from any place and at any time as per demand. Generally, Cloud services provide the infrastructure by reducing the cost of storing, processing and updating information with improved efficiency and quality. However, the privacy of Electronic Health Records (EHR) is a significant hurdle while outsourcing private health data in the cloud because there is a higher peril of leaking health information to unauthorized parties. Several existing techniques are able to analyse the security and privacy issues associated with e-healthcare services. These methods are designed for single database, or databases, with an authentication centre and thus cannot adequately protect the data from insider attacks. Therefore, this research study mainly focusses on how to ensure the patient privacy while sharing the sensitive data between same or different organisations as well as healthcare providers in a cloud environment. This paper proposes a multi-layer access control mechanism named MLAC Model to construct a secure and privacy-preserving EHR system that enables patients to share their data with stakeholders. In this paper, we use a Dual layer access control model named Pseudo-Role Attribute based access control (PR-ABAC) mechanism that integrates attributes with roles for the secure sharing of EHR between multiple collaborators. The proposed framework also uses the concept of Provenance to ensure the Integrity of patient data. This work is expected to provide a foundation for developing security solutions against cyber-attacks, and thus contribute to the robustness of healthcare information sharing environments.

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False Data Injection Attacks in Healthcare

Cited by 21 publications

References 21 publications

False data injection attack (FDIA): an overview and new metrics for fair evaluation of its countermeasure

False data injection attack (FDIA): an overview and new metrics for fair evaluation of its countermeasure

Proof-of-Familiarity: A Privacy-Preserved Blockchain Scheme for Collaborative Medical Decision-Making

Privacy-Preserving Data Sharing using Multi-layer Access Control Model in Electronic Health Environment

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