Cyber threats in the healthcare sector have increased significantly in recent years. Attackers are now using sophisticated techniques to launch multi-phase cyber attacks to compromise the system and leak patient healthcare data. Healthcare organisations need to protect IT infrastructures and understand the threats and possible attack surface for a secure healthcare service delivery. Hence, threat analysis is one of the key activities for tackling the potential risks and ensuring security of a system context. This work presents a threat analysis approach that allows to identify and assess the possible threats within healthcare information infrastructure. The approach considers the existing threat data from widely used repositories and uses Natural Language Processing to identify threats among cyber security news, also evaluating their corresponding level. The preliminary experimental assessment shows promising results, providing a realistic manner to assess the threats, allowing to adopt the proposed approach in real-world contexts.

Cyber Threat Analysis Using Natural Language Processing for a Secure Healthcare System

Stefano Silvestri
2022

Abstract

Cyber threats in the healthcare sector have increased significantly in recent years. Attackers are now using sophisticated techniques to launch multi-phase cyber attacks to compromise the system and leak patient healthcare data. Healthcare organisations need to protect IT infrastructures and understand the threats and possible attack surface for a secure healthcare service delivery. Hence, threat analysis is one of the key activities for tackling the potential risks and ensuring security of a system context. This work presents a threat analysis approach that allows to identify and assess the possible threats within healthcare information infrastructure. The approach considers the existing threat data from widely used repositories and uses Natural Language Processing to identify threats among cyber security news, also evaluating their corresponding level. The preliminary experimental assessment shows promising results, providing a realistic manner to assess the threats, allowing to adopt the proposed approach in real-world contexts.
2022
Istituto di Calcolo e Reti ad Alte Prestazioni - ICAR
Inglese
Proceedings - IEEE Symposium on Computers and Communications
2022 IEEE Symposium on Computers and Communications (ISCC)
1
7
7
978-1-6654-9792-3
https://ieeexplore.ieee.org/document/9912768
IEEE COMPUTER SOC
LOS ALAMITOS, CA
STATI UNITI D'AMERICA
Sì, ma tipo non specificato
30/06/2022-03/07/2022
Rhodes, Greece
Internazionale
Healthcare Ecosystem
Cyber Threat
Deep Learning
Natural Language Processing
Healthcare Information Infrastructure
3
reserved
Islam, Shareeful; Papastergiou, Spyridon; Silvestri, Stefano
273
info:eu-repo/semantics/conferenceObject
04 Contributo in convegno::04.01 Contributo in Atti di convegno
   A Dynamic and Self-Organized Artificial Swarm Intelligence Solution for Security and Privacy Threats in Healthcare ICT Infrastructures
   AI4HEALTHSEC
   H2020
   883273
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.14243/420405
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