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- Publisher Website: 10.1007/978-3-319-69471-9_12
- Scopus: eid_2-s2.0-85034247540
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Conference Paper: Detecting malicious nodes in medical smartphone networks through euclidean distance-based behavioral profiling
Title | Detecting malicious nodes in medical smartphone networks through euclidean distance-based behavioral profiling |
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Authors | |
Keywords | Malicious node Collaborative network Trust computation and management Medical Smartphone Network Insider attack Intrusion detection |
Issue Date | 2017 |
Citation | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2017, v. 10581 LNCS, p. 163-175 How to Cite? |
Abstract | © 2017, Springer International Publishing AG. With the increasing digitization of the healthcare industry, a wide range of medical devices are Internet- and inter-connected. Mobile devices (e.g., smartphones) are one common facility used in the healthcare industry to improve the quality of service and experience for both patients and healthcare personnel. The underlying network architecture to support such devices is also referred to as medical smartphone networks (MSNs). Similar to other networks, MSNs also suffer from various attacks like insider attacks (e.g., leakage of sensitive patient information by a malicious insider). In this work, we focus on MSNs and design a trust-based intrusion detection approach through Euclidean distance-based behavioral profiling to detect malicious devices (or called nodes). In the evaluation, we collaborate with healthcare organizations and implement our approach in a real simulated MSN environment. Experimental results demonstrate that our approach is promising in effectively identifying malicious MSN nodes. |
Persistent Identifier | http://hdl.handle.net/10722/280643 |
ISSN | 2023 SCImago Journal Rankings: 0.606 |
DC Field | Value | Language |
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dc.contributor.author | Meng, Weizhi | - |
dc.contributor.author | Li, Wenjuan | - |
dc.contributor.author | Wang, Yu | - |
dc.contributor.author | Au, Man Ho | - |
dc.date.accessioned | 2020-02-17T14:34:33Z | - |
dc.date.available | 2020-02-17T14:34:33Z | - |
dc.date.issued | 2017 | - |
dc.identifier.citation | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2017, v. 10581 LNCS, p. 163-175 | - |
dc.identifier.issn | 0302-9743 | - |
dc.identifier.uri | http://hdl.handle.net/10722/280643 | - |
dc.description.abstract | © 2017, Springer International Publishing AG. With the increasing digitization of the healthcare industry, a wide range of medical devices are Internet- and inter-connected. Mobile devices (e.g., smartphones) are one common facility used in the healthcare industry to improve the quality of service and experience for both patients and healthcare personnel. The underlying network architecture to support such devices is also referred to as medical smartphone networks (MSNs). Similar to other networks, MSNs also suffer from various attacks like insider attacks (e.g., leakage of sensitive patient information by a malicious insider). In this work, we focus on MSNs and design a trust-based intrusion detection approach through Euclidean distance-based behavioral profiling to detect malicious devices (or called nodes). In the evaluation, we collaborate with healthcare organizations and implement our approach in a real simulated MSN environment. Experimental results demonstrate that our approach is promising in effectively identifying malicious MSN nodes. | - |
dc.language | eng | - |
dc.relation.ispartof | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | - |
dc.subject | Malicious node | - |
dc.subject | Collaborative network | - |
dc.subject | Trust computation and management | - |
dc.subject | Medical Smartphone Network | - |
dc.subject | Insider attack | - |
dc.subject | Intrusion detection | - |
dc.title | Detecting malicious nodes in medical smartphone networks through euclidean distance-based behavioral profiling | - |
dc.type | Conference_Paper | - |
dc.description.nature | link_to_subscribed_fulltext | - |
dc.identifier.doi | 10.1007/978-3-319-69471-9_12 | - |
dc.identifier.scopus | eid_2-s2.0-85034247540 | - |
dc.identifier.volume | 10581 LNCS | - |
dc.identifier.spage | 163 | - |
dc.identifier.epage | 175 | - |
dc.identifier.eissn | 1611-3349 | - |
dc.identifier.issnl | 0302-9743 | - |