Anomaly Detection Scheme for Medical Wireless Sensor Networks

Salem, Osman; Guerassimov, Alexey; Mehaoua, Ahmed; Marcus, Anthony; Furht, Borko

doi:10.1007/978-1-4614-8495-0_8

Cited by 12 publications

(4 citation statements)

References 24 publications

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“…Thus the total cost of the time is O(l) in the process of initialization. At the tth iteration, combining formula (17) with (19), at least increase in R A,t is shown in the following formula (20). Obviously, R M is an upper bound on the radius of the acquired sphere.…”

Section: Analysis Of Computational Complexitymentioning

confidence: 99%

“…In recent years, many studies have been conducted on machine learning and data mining approaches for anomaly detection in WSNs [9,[18][19][20][21][22][23]. Moshtaghi et al [18] proposed an adaptive method that can create elliptical decision boundaries for anomaly detection in WSNs and maintain the decision boundaries without the need for re-training.…”

Section: Introductionmentioning

confidence: 99%

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A Lightweight Anomaly Detection Method Based on SVDD for Wireless Sensor Networks

Chen

2019

Wireless Pers Commun

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Limited resources and harsh deployment environments may cause raw observations collected by sensor nodes to have poor data quality and reliability, which will influence the accuracy of the analysis and decision making in wireless sensor networks (WSNs). Therefore, anomaly detection must be implemented on the data collected by nodes. Support vector data description based on spatiotemporal and attribute correlations (STASVDD) can efficiently detect outliers. A novel optimization method based on STASVDD (N-STAS-VDD) is put forward in this paper. The proposed method considers that outliers can independently occur in each attribute when the collected data vectors are independent and identically distributed in WSNs. The proposed method applies the concept of core-sets to reduce the computational complexity of the quadratic programming problem in STAS-VDD, consequently reducing the energy consumption of resources-constrained WSNs. In addition, comparing the distributed and centralized detection approach of this method, the results show that the distributed approach has better performance because it relieves the communication burden. Extensive experiments were performed on both synthetic and real WSNs datasets. Results revealed that N-STASVDD achieves low time complexity and high detection accuracy.

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Section: Analysis Of Computational Complexitymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Lightweight Anomaly Detection Method Based on SVDD for Wireless Sensor Networks

Chen

2019

Wireless Pers Commun

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“…According to the Grand View Research 1 , IoMT is predicted to reach $534.3 Billion in market size by 2025. It is expected that the U.S. home-based healthcare market 2 will rise by about 7% annually from $103 billion in 2018 to $173 billion by 2026. With 1 https://www.healthcareitnews.com/news/asia-pacific/opportunities-pitfallshealthcare-iot 2 https://store.businessinsider.com/products/the-us-home-healthcare-report recent technological advancements, IoMT has the potential to revolutionize the future of health care industry.…”

Section: Introductionmentioning

confidence: 99%

“…In this paper, we focus on security of RPM including user safety and identify anomalous behavior in RPM applications based on the data collected from smart devices while remotely monitoring the patient at home. Research has developed various methods to secure RPM infrastructure from anomalies, focusing on wireless sensor networks [2]- [4], patient's behavior monitoring [5]- [9], signature and correlation analysis [10], [11]. While there are several anomaly detection models developed for RPM, elderly care, and smart homes, our anomaly detection model is designed to identify anomalies in a unique RPM ecosystem which comprises the intersection of two IoT domains (smart health care and smart home).…”

Section: Introductionmentioning

confidence: 99%

Detecting Anomalous User Behavior in Remote Patient Monitoring

Gupta¹,

Gupta²,

Bhatt³

et al. 2021

Preprint

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The growth in Remote Patient Monitoring (RPM) services using wearable and non-wearable Internet of Medical Things (IoMT) promises to improve the quality of diagnosis and facilitate timely treatment for a gamut of medical conditions. At the same time, the proliferation of IoMT devices increases the potential for malicious activities that can lead to catastrophic results including theft of personal information, data breach, and compromised medical devices, putting human lives at risk. IoMT devices generate tremendous amount of data that reflect user behavior patterns including both personal and day-to-day social activities along with daily routine health monitoring. In this context, there are possibilities of anomalies generated due to various reasons including unexpected user behavior, faulty sensor, or abnormal values from malicious/compromised devices. To address this problem, there is an imminent need to develop a framework for securing the smart health care infrastructure to identify and mitigate anomalies. In this paper, we present an anomaly detection model for RPM utilizing IoMT and smart home devices. We propose Hidden Markov Model (HMM) based anomaly detection that analyzes normal user behavior in the context of RPM comprising both smart home and smart health devices, and identifies anomalous user behavior. We design a testbed with multiple IoMT devices and home sensors to collect data and use the HMM model to train using network and user behavioral data. Proposed HMM based anomaly detection model achieved over 98% accuracy in identifying the anomalies in the context of RPM.

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False Data Injection Attacks in Healthcare

Ahmed

Ullah

2018

Communications in Computer and Information Science

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Anomaly Detection Scheme for Medical Wireless Sensor Networks

Cited by 12 publications

References 24 publications

A Lightweight Anomaly Detection Method Based on SVDD for Wireless Sensor Networks

A Lightweight Anomaly Detection Method Based on SVDD for Wireless Sensor Networks

Detecting Anomalous User Behavior in Remote Patient Monitoring

False Data Injection Attacks in Healthcare

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