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A Novel Framework for Threat Analysis of Machine Learning-Based Smart Healthcare Systems

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A Novel Framework for Threat Analysis of Machine Learning-Based Smart Healthcare Systems A Novel Framework for Threat Analysis of Machine Learning-based Smart Healthcare Systems Nur Imtiazul Haque∗, Mohammad Ashiqur Rahman∗, Md Hasan Shahriar∗ Alvi Ataur Khalil∗ and Selcuk Uluagacy ∗Analytics for Cyber Defense (ACyD) Lab, yCyber-Physical Systems Security Lab Department of Electrical and Computer Engineering Florida International University, Miami, USA f nhaqu004, marahman, mshah068, akhal042, suluagacg@fiu.edu Abstract—Smart healthcare systems (SHSs) are providing fast and triggers implantable medical devices (IMDs) for real-time and efficient disease treatment leveraging wireless body sensor medication and treatment. Currently, healthcare facilities are networks (WBSNs) and implantable medical devices (IMDs)- more efficient, accessible, and personalized as the SHS is based internet of medical things (IoMT). In addition, IoMT-based SHSs are enabling automated medication, allowing communication ameliorating disease diagnostic tools, treatment for patients, among myriad healthcare sensor devices. However, adversaries and healthcare devices, thus improving the quality of lives [6]. can launch various attacks on the communication network and However, an SHS requires processing a lot of historical data to the hardware/firmware to introduce false data or cause data identify anomalous sensor measurements. The data related to unavailability to the automatic medication system endangering healthcare and medication are affluent. They can be utilized to the patient’s life. In this paper, we propose SHChecker, a novel threat analysis framework that integrates machine learning and reveal intricate patterns of dependency between various vital formal analysis capabilities to identify potential attacks and signs of the human body for accurate and precise disease corresponding effects on an IoMT-based SHS. Our framework classification. For faster processing, an SHS integrates the can provide us with all potential attack vectors, each representing concept of IoMT with big data, cloud computing, and artificial a set of sensor measurements to be altered, for an SHS given a intelligence [7]. specific set of attack attributes, allowing us to realize the system’s resiliency, thus the insight to enhance the robustness of the model. With the advent of SHSs, smart medical devices are being We implement SHChecker on a synthetic and a real dataset, which exposed to numerous attack points, susceptible to potential affirms that our framework can reveal potential attack vectors threats. Cyberattacks on the healthcare industry are rapidly in an IoMT system. This is a novel effort to formally analyze growing [8]. SHS devices/IMDs often have vulnerabilities that supervised and unsupervised machine learning models for black- expose significant threats [9]. According to a recent report, 53% box SHS threat analysis. Index Terms—Healthcare systems, internet of medical things, of the healthcare organizations were attacked between October machine learning, cyberattacks, threat analysis, formal model. 2018 and October 2019 [10]. Most popular cyberattacks in the healthcare systems includes hardware Trojan [11], malware (e.g., Medjack [12]), Sybil attacks using either hijacked IoMT I. INTRODUCTION [13] or single malicious node [14], DoS attacks [15], and man- Traditional healthcare systems rely heavily on hospitaliza- in-the-middle (MITM) attacks [16]. At least 20% of the medical tion, specialized consultation, and nursing that require a lot of device manufacturers experienced ransomware or malware at- human interventions. It introduces delayed or incorrect treat- tacks in the last 20 months [17]. So, it is imperative to study ment resulting in increased treatment cost or human mortality. the vulnerabilities of an SHS before deploying it. In the U.S., the aggregated patient treatment cost was almost In this paper, we propose an automated threat analysis $3.81 trillion in 2019 [1], which is projected to be much framework, named SHChecker, for SHSs that uses a machine arXiv:2103.03472v1 [cs.CR] 5 Mar 2021 higher in recent days, especially due to COVID-19 [2]. An learning (ML)-based disease classification model (DCM) to automated healthcare system can certainly reduce this cost and deliver real-time treatment. SHChecker analyzes the underlying the death rate. Contemporary internet of medical things (IoMT) decision-making model of SHS by investigating the possible technology has brought a radical revolution in the healthcare attacks that can be deployed by minimal alteration of sen- domain by enhancing the reliability of remote patient moni- sor values. For safety-critical CPSs like SHSs, failures can toring, increasing efficiency of the medical sensors, and elim- raise the possibility of life-threatening events. That’s why, inating latency between disease detection and medication [3]. to increase reliability, SHSs often employ data validation or A growing number of researches have been conducted toward anomaly detection systems. Hence, we consider a clustering- using IoMT technology for ubiquitous data acquisition, timely based anomaly detection model (ADM) in the SHS due to its processing, and wireless data distribution in the healthcare real-time detection capability [18]. The ADM can learn the field [4], [5]. pattern of sensor measurement relationships by analyzing a The smart healthcare system (SHS) is a modern cyber- massive quantity of data. The proposed SHChecker framework physical system (CPS) that continuously collects data from can assess potential attack vectors of an SHS that uses ML the IoMT sensor network connected to the human body, pro- algorithms, such as decision tree (DT), logistic regression (LR), cesses them accordingly for making required control decisions, or artificial neural network (ANN) for classifying diseases and Sensor Controller Actuator BPM F1 Heart-rate S1 ECG F2 A1 Systolic F3 Blood- ICD S2 Pressure Diastolic F4 Drug Infusion A2 Nasal Pump Sound F5 Insulin Breathing S3 A3 Oral F6 Pump Sound Server Neuro A4 AP stimulator Oxygen F7 Blood- Sweat S4 A5 BPM F1 Oxygen Control Neural- EEG F8 S5 Activity Fig. 1: Sensor behavioral model in SHS K-means or density-based spatial clustering of applications with II. BACKGROUND noise (DBSCAN) clustering algorithms for detecting anomalies. This section provides an overview of the SHSs and some Our work mainly focuses on quantifying the associated threat other related information to motivate the work and facilitate that can be performed by minor alteration in one or more sensor the reader’s comprehension. measurements of an SHS. We verify our model’s efficacy using the University of Queensland Vital Signs (UQVS) dataset and A. IoMT-based SHS our synthetic dataset using several performance metrics [19]. IoMT based SHS is a game-changer for the medical field In summary, our contributions are as follows: concerning consultation accuracy and cost reduction related to human labor. The enormous amount of medical data enables researchers to perform statistical analyses of diseases and • Considering various ML algorithms, we model a real-time medication patterns. So, with the introduction of IoMT in the SHS by deploying a DCM and a corresponding ADM. healthcare field, more attention has been paid to developing • We formally represent the attack model for an SHS using ubiquitous data accessing solutions to acquire and process data a set of flexible attack attributes that specifies an attacker’s from decentralized data sources [21], [22], [23]. In the IoMT capabilities and the attack target. network, data are sent to a remote server to analyze and take • We build SHChecker, a threat analysis framework that can control decisions due to the lack of processing capability of identify potential attack vectors for an ML model-based medical sensors and implantable medical devices (IMDs). SHS. The source code can be found in github [20]. This paper considers an IoMT applied SHS incorporating a • We conduct experiments with SHChecker using real and wireless body sensor network, ML-based control system, and synthetic data to analyze the attack characteristics and IMD-based actuators.Fig. 1 demonstrates an SHS that can de- evaluate the tool’s scalability. liver medicine in real-time with a closed-loop decision control system without requiring any human involvement. In an SHS, The rest of the paper is organized as follows: we provide an patients are continuously monitored by the sensors attached to overview of the SHS and its vulnerabilities along with other their bodies. These sensors deliver their observed measurements necessary background information in Section II-D. We present to the controller using some wireless communication protocols, the SHChecker’s architecture in Section III. In Section III-C2, e.g., WiFi, Bluetooth, Zigbee, etc. we discuss the technical details of the framework, which The controller takes thee measured values, makes decisions includes the machine learning-based models for constraint based on them, and sends the control commands to the IMDs generation and a formal model for threat analysis consider- to deliver the necessary treatment to the patient. For exam- ing adversary’s goals, capabilities, and methodology. We also ple, Dario’s blood glucose monitoring system continuously provide example case studies in section III-C. Moreover, we advertises blood glucose value to the controller (Table I). The discuss our SHS testbed implementation in Section IV. Then, controller checks whether the vital signs of the patient are we evaluate our proposed framework by running experiments within normal ranges [24].
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