Securing Insulin Pump System Using Deep Learning and Gesture Recognition

Ahmad, Usman; Song, Hong; Bilal, Awais; Saleem, Shahzad; Ullah, Asad

doi:10.1109/trustcom/bigdatase.2018.00258

Cited by 15 publications

(14 citation statements)

References 13 publications

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“…In another study made by in [70], The authors propose to secure the insulin pump against attacks that aim to alter the functioning of its system to deliver a lethal dosage of insulin to the patient. To address this issue, the authors propose using Long short-term memory (LSTM), a DL algorithm, to define a threshold value for the insulin dosage delivered to the patient.…”

Section: For Data Collection Levelmentioning

confidence: 99%

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Survey of Machine Learning Based Intrusion Detection Methods for Internet of Medical Things

Si-Ahmed¹,

Al-Garadi²,

Boustia³

2022

Preprint

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Internet of Medical Things (IoMT) represents an application of the Internet of Things, where health professionals perform remote analysis of physiological data collected using sensors that are associated with patients, allowing real-time and permanent monitoring of the patient's health condition and the detection of possible diseases at an early stage. However, the use of wireless communication for data transfer exposes this data to cyberattacks, and the sensitive and private nature of this data may represent a prime interest for attackers. The use of traditional security methods on equipment that is limited in terms of storage and computing capacity is ineffective. In this context, we have performed a comprehensive survey to investigate the use of the intrusion detection system based on machine learning (ML) for IoMT security. We presented the generic three-layer architecture of IoMT, the security requirement of IoMT security. We review the various threats that can affect IoMT security and identify the advantage, disadvantages, methods, and datasets used in each solution based on ML. Then we provide some challenges and limitations of applying ML on each layer of IoMT, which can serve as direction for future study.

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Section: For Data Collection Levelmentioning

confidence: 99%

“…After reviewing the different solutions of security based on ML for data collection level, we notice that the majority of these researches are focused on the security of cardiac implants [7,68] and insulin pump injection system [6,35,70]. There is just one study that is based on the security of deep brain implants [5].…”

Section: For Data Collection Levelmentioning

confidence: 99%

Survey of Machine Learning Based Intrusion Detection Methods for Internet of Medical Things

Si-Ahmed¹,

Al-Garadi²,

Boustia³

2022

Preprint

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“…However, the AES algorithm cannot be applied to CGM since it is only a sensor coupled with a transmitter. Ahmad et al [43] proposed to use a deep learning method to predict the dosage threshold and ask the patient to perform gestures if the insulin to be injected is higher than the threshold. A visible channel based access control scheme, proposed by Zhao et al [44], can authenticate the doctor's USB by transmitting the PIN/key to the IPS via visible light signals.…”

Section: B Ips Securitymentioning

confidence: 99%

Fingerprint Access Control for Wireless Insulin Pump Systems Using Cancelable Delaunay Triangulations

et al. 2019

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An insulin pump is a small wearable medical gadget which can mimic the way a healthy pancreas functions by providing continuous subcutaneous insulin infusion for the patient. However, in the current products, the access to the pump is not securely controlled, rendering the pump insecure to harmful or even lethal attacks, such as those that lead to the privacy breach of the patient and the delivery of abnormal dosage of insulin to the patient. In a conventional symmetric key-based security solution, how to distribute and manage the key is quite challenging, since the patient bearing an insulin pump may visit any hospital or clinic to receive treatment from any qualified doctor. In order to prevent malicious access to the pump, in this paper, we propose a Fingerprint-based Insulin Pump security (FIPsec) scheme which requires an entity to first pass the fingerprint authentication process before it is allowed to access the insulin pump. With this scheme, the request from an adversary to access the pump will be blocked thereby protecting the pump from the possibility of being subjected to serious attacks during the post-request period. In the scheme, a cancelable Delaunay triangle-based fingerprint matching algorithm is proposed for the insulin pump, which has capabilities to resist against nonlinear fingerprint image distortion and the influence of missing or spurious minutiae. In order to evaluate the performance of the proposed fingerprint matching algorithm, we perform comprehensive experiments on FVC2002 DB1 and DB2 fingerprint databases. The results show that the FIPsec scheme can achieve a reasonably low equal error rate and thus becomes a viable solution to thwarting unauthorized access to the insulin pump.INDEX TERMS Biomedical informatics, wearable sensors, implantable biomedical devices, insulin pump system, artificial pancreas, cyber security, authentication, access control.

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“…An attacker who gains access to a device can silently observe the device for months before launching an assault on the device. With the increasing number of cameras in IoT devices, such as toys, smartphones, and wristwatches, the impact of PMA is becoming increasingly significant [42].…”

mentioning

confidence: 99%

“…Malware can be used to impersonate a user. ADPA includes data tampering, while PDPA includes data leakage and reidentification [42].…”

mentioning

confidence: 99%

The Comparison of Cybersecurity Datasets

Alshaibi

Alani

Al-Azzawi

et al. 2022

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Almost all industrial internet of things (IIoT) attacks happen at the data transmission layer according to a majority of the sources. In IIoT, different machine learning (ML) and deep learning (DL) techniques are used for building the intrusion detection system (IDS) and models to detect the attacks in any layer of its architecture. In this regard, minimizing the attacks could be the major objective of cybersecurity, while knowing that they cannot be fully avoided. The number of people resisting the attacks and protection system is less than those who prepare the attacks. Well-reasoned and learning-backed problems must be addressed by the cyber machine, using appropriate methods alongside quality datasets. The purpose of this paper is to describe the development of the cybersecurity datasets used to train the algorithms which are used for building IDS detection models, as well as analyzing and summarizing the different and famous internet of things (IoT) attacks. This is carried out by assessing the outlines of various studies presented in the literature and the many problems with IoT threat detection. Hybrid frameworks have shown good performance and high detection rates compared to standalone machine learning methods in a few experiments. It is the researchers’ recommendation to employ hybrid frameworks to identify IoT attacks for the foreseeable future.

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Securing Insulin Pump System Using Deep Learning and Gesture Recognition

Cited by 15 publications

References 13 publications

Survey of Machine Learning Based Intrusion Detection Methods for Internet of Medical Things

Survey of Machine Learning Based Intrusion Detection Methods for Internet of Medical Things

Fingerprint Access Control for Wireless Insulin Pump Systems Using Cancelable Delaunay Triangulations

The Comparison of Cybersecurity Datasets

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