A review of security challenges, attacks and resolutions for wireless medical devices

Rathore, Heena; Mohamed, Amr; Al-Ali, Abdulla; Du, Xiaojiang; Guizani, Mohsen

doi:10.1109/iwcmc.2017.7986505

Cited by 31 publications

(18 citation statements)

References 27 publications

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“…There are a number of diverse solutions which address the security issues in wireless IMDs [18]. Bio-metric based approaches access the unique physiological characteristics of the human body and provide authentication.…”

Section: Related Work and Backgroundmentioning

confidence: 99%

A Novel Deep Learning Strategy for Classifying Different Attack Patterns for Deep Brain Implants

et al. 2019

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Deep brain stimulators (DBSs), a widely used and comprehensively acknowledged restorative methodology, are a type of implantable medical device which uses electrical stimulation to treat neurological disorders. These devices are widely used to treat diseases such as Parkinson, movement disorder, epilepsy, and psychiatric disorders. Security in such devices plays a vital role since it can directly affect the mental, emotional, and physical state of human bodies. In worst-case situations, it can even lead to the patient's death. An adversary in such devices, for instance, can inhibit the normal functionality of the brain by introducing fake stimulation inside the human brain. Nonetheless, the adversary can impair the motor functions, alter impulse control, induce pain, or even modify the emotional pattern of the patient by giving fake stimulations through DBSs. This paper presents a deep learning methodology to predict different attack stimulations in DBSs. The proposed work uses long short-term memory, a type of recurrent network for forecasting and predicting rest tremor velocity. (A type of characteristic observed to evaluate the intensity of the neurological diseases) The prediction helps in diagnosing fake versus genuine stimulations. The effect of deep brain stimulation was tested on Parkinson tremor patients. The proposed methodology was able to detect different types of emulated attack patterns efficiently and thereby notifying the patient about the possible attack. INDEX TERMS Deep brain stimulators, deep learning, implantable medical devices, machine learning, security.

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Section: Related Work and Backgroundmentioning

confidence: 99%

A Novel Deep Learning Strategy for Classifying Different Attack Patterns for Deep Brain Implants

et al. 2019

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“…mHealth devices that use Bluetooth are vulnerable to an attack called external device mis-bonding (Naveed et al, 2014) which allows external devices to steal data or insert fake data into the original application, thus compromising data confidentiality and integrity. Implantable medical devices are vulnerable to information harvesting, patient-tracking, impersonation and denial of service (Rathore et al, 2017). Elkhodr et al (2011) proposed six features to ensure the authentication of the device, user and environment: secure username, subscriber identification module (SIM) serial number, international mobile identity number, and device longitude and latitude.…”

Section: Security Attacks On the Data Gathering Segmentmentioning

confidence: 99%

Security architecture for a 5G mHealth system

Malila

Mutsvangwa

2019

GHI

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Global health challenges and the proliferation of mobile technologies have been key in the adoption of mHealth for provision of low-cost and equitable healthcare. Evolution of mobile networks to 5G is expected to revolutionise healthcare service delivery due to the stringent performance requirements imposed on 5G. However, because of the open nature of 5G systems, securing patient health information has been identified as a significant barrier to the full adoption of mHealth. In this paper, we propose a security architecture for an mHealth system based on a review of standard principles and guidelines for designing 5G security systems. We present a structured approach for developing and implementing an end-to-end 5G mHealth security system. We propose a security architecture that can be realised using keyless signature infrastructure Blockchain and X-tee technology to secure the communication system including hospital and third-party health data networks, physical layer security for securing the wireless interface in access networks, physical unclonable functions, and a trusted execution environment for securing end-user devices. We propose the adoption of network slicing for isolating health systems from other 5G industry verticals. We define system domains that are used to identify security threats and propose mechanisms to mitigate these threats.

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“…European General Data Protection Regulation 2016/679 [11], FDA [12]). IMD data are also submitted to various security threats the consequences of which can be dramatic medical adverse events [13]. In such a context, one has to consider the following data security requirements as defined by NIST [14]:…”

Section: Introductionmentioning

confidence: 99%

Cryptosystem Conversion, Packing and Matrix Processing of Homomorphically Encrypted Data: Application to IOT Devices

2021

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In this paper, we propose a secure protocol that allows processing encrypted data emitted by an IOT device with low computational capabilities. Its originality is threefold. It first relies on a new fast algorithm which makes possible the conversion of Combined Linear Congruential Generator (CLCG) encrypted IOT data into data homomorphically encrypted with the Damgard-Jurik (D-J) cryptosystem. In second, an original data packing strategy is given so as to reduce communication and computation complexity as well as process several D-J encrypted data at once by means of matrix operations. In third, we introduce a crypto-watermarking based integrity control mechanism. This one combines the lightweight hash function Quark with LSB substitution so as to offer the capability to check the integrity of CLCG encrypted data. We illustrate the deployment of our protocol, in the case an honest-but-curious or malicious third party wants to process encrypted data issued from a real connected knee prosthesis. We theoretically and experimentally demonstrate the performance of our solution. This one can nearly process 500 samples every second. Beyond, our proposal is suited to the general case of IOT.

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A review of security challenges, attacks and resolutions for wireless medical devices

Cited by 31 publications

References 27 publications

A Novel Deep Learning Strategy for Classifying Different Attack Patterns for Deep Brain Implants

A Novel Deep Learning Strategy for Classifying Different Attack Patterns for Deep Brain Implants

Security architecture for a 5G mHealth system

Cryptosystem Conversion, Packing and Matrix Processing of Homomorphically Encrypted Data: Application to IOT Devices

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