Vulnerability prediction for secure healthcare supply chain service delivery

Abstract

Healthcare organisations are constantly facing sophisticated cyberattacks due to the sensitivity and criticality of patient health care information and wide connectivity of medical devices. Such attacks can pose potential disruptions to critical services delivery. There are number of existing works that focus on using Machine Learning (ML) models for predicting vulnerability and exploitation but most of these works focused on parameterized values to predict severity and exploitability. This paper proposes a novel method that uses ontology axioms to define essential concepts related to the overall healthcare ecosystem and to ensure semantic consistency checking among such concepts. The application of ontology enables the formal specification and description of healthcare ecosystem and the key elements used in vulnerability assessment as a set of concepts. Such specification also strengthens the relationships that exist between healthcare-based and vulnerability assessment concepts, in addition to semantic definition and reasoning of the concepts. Our work also makes use of Machine Learning techniques to predict possible security vulnerabilities in health care supply chain services. The paper demonstrates the applicability of our work by using vulnerability datasets to predict the exploitation. The results show that the conceptualization of healthcare sector cybersecurity using an ontological approach provides mechanisms to better understand the correlation between the healthcare sector and the security domain, while the ML algorithms increase the accuracy of the vulnerability exploitability prediction. Our result shows that using Linear Regression, Decision Tree and Random Forest provided a reasonable result for predicting vulnerability exploitability.

A systematic survey on security and privacy issues of medicine supply chain: Taxonomy, framework, and research challenges

“…Shareeful et al 59 proposed a cybersecurity‐based vulnerability detection in a healthcare supply chain. They used the ontology axioms method for the formal specifications in vulnerability assessment.…”

Neuro-distributed cognitive adaptive optimization for training neural networks in a parallel and asynchronous manner

“…Highly advanced Artificial Intelligence (AI), along with its associated sub-disciplines like machine learn-ing (ML), paved the way for a wide range of real-world applications in sectors such as visual recognition systems, language understanding, automated translation techniques, and robotic innovations [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18]. In recent years, the most prominent sub-field of machine learning (ML), Artificial Neural Networks (ANNs), have been widely and effectively utilized to transform technological advancements and elevate both businesses and daily life towards a new stage of AI sophistication [19][20][21][22][23][24][25].…”

“…1 has K hidden layers with N (i) total neurons in the i-th layer; a(i, j) denote the activation functions of the j-th neuron in the i-th layer; for convenience we assume that the 0-th layer is the input layer (with a(0, j) ≡ x j , where x j denotes the j-th input; and (K + 1)-th layer is the input layer (with a(K + 1, j) ≡ y j , where y j denotes the j-output. Let w = [w (1) ; w (2) ; . .…”

A Machine Learning Approach for the NLP-Based Analysis of Cyber Threats and Vulnerabilities of the Healthcare Ecosystem

“…There is no doubt about the positive impact of digital transformation in the healthcare sector. However, despite these benefits, the adoption of digital technology provides many Cyber Security (CS) challenges that can pose any potential risks within the healthcare system [ 1 ]. This massive technological transformation increases the attack surface where threat actors can exploit possible threats for any potential risk within the Health Care Information Infrastructure (HCII).…”