Literature DB >> 24473551

MedMon: securing medical devices through wireless monitoring and anomaly detection.

Meng Zhang, Anand Raghunathan, Niraj K Jha.   

Abstract

Rapid advances in personal healthcare systems based on implantable and wearable medical devices promise to greatly improve the quality of diagnosis and treatment for a range of medical conditions. However, the increasing programmability and wireless connectivity of medical devices also open up opportunities for malicious attackers. Unfortunately, implantable/wearable medical devices come with extreme size and power constraints, and unique usage models, making it infeasible to simply borrow conventional security solutions such as cryptography. We propose a general framework for securing medical devices based on wireless channel monitoring and anomaly detection. Our proposal is based on a medical security monitor (MedMon) that snoops on all the radio-frequency wireless communications to/from medical devices and uses multi-layered anomaly detection to identify potentially malicious transactions. Upon detection of a malicious transaction, MedMon takes appropriate response actions, which could range from passive (notifying the user) to active (jamming the packets so that they do not reach the medical device). A key benefit of MedMon is that it is applicable to existing medical devices that are in use by patients, with no hardware or software modifications to them. Consequently, it also leads to zero power overheads on these devices. We demonstrate the feasibility of our proposal by developing a prototype implementation for an insulin delivery system using off-the-shelf components (USRP software-defined radio). We evaluate its effectiveness under several attack scenarios. Our results show that MedMon can detect virtually all naive attacks and a large fraction of more sophisticated attacks, suggesting that it is an effective approach to enhancing the security of medical devices.

Entities:  

Mesh:

Year:  2013        PMID: 24473551     DOI: 10.1109/TBCAS.2013.2245664

Source DB:  PubMed          Journal:  IEEE Trans Biomed Circuits Syst        ISSN: 1932-4545            Impact factor:   3.833


  8 in total

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Journal:  ACM Trans Intell Syst Technol       Date:  2021-02-11       Impact factor: 4.654

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5.  Interpreting Health Events in Big Data Using Qualitative Traditions.

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Journal:  Int J Qual Methods       Date:  2020-12-09

6.  A systematic review of security and privacy issues in the internet of medical things; the role of machine learning approaches.

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Journal:  PeerJ Comput Sci       Date:  2021-03-23

7.  A Hybrid Lightweight System for Early Attack Detection in the IoMT Fog.

Authors:  Shilan S Hameed; Ali Selamat; Liza Abdul Latiff; Shukor A Razak; Ondrej Krejcar; Hamido Fujita; Mohammad Nazir Ahmad Sharif; Sigeru Omatu
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8.  Design and Analysis of a True Random Number Generator Based on GSR Signals for Body Sensor Networks.

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  8 in total

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