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Handbook Of Biometrics

Deep learning models are efficient in learning the features that assist in understanding complex patterns precisely. This study proposed a computerized process of classifying skin disease through deep learning based MobileNet V2 and Long Short Term Memory (LSTM). The MobileNet V2 model proved to be efficient with a better accuracy that can work on lightweight computational devices. The proposed model is efficient in maintaining stateful information for precise predictions. A grey-level co-occurrence matrix is used for assessing the progress of diseased growth. The performance has been compared against other state-of-the-art models such as Fine-Tuned Neural Networks (FTNN), Convolutional Neural Network (CNN), Very Deep Convolutional Networks for Large-Scale Image Recognition developed by Visual Geometry Group (VGG), and convolutional neural network architecture that expanded with few changes. The HAM10000 dataset is used and the proposed method has outperformed other methods with more than 85% accuracy. Its robustness in recognizing the affected region much faster with almost 2× lesser computations than the conventional MobileNet model results in minimal computational efforts. Furthermore, a mobile application is designed for instant and proper action. It helps the patient and dermatologists identify the type of disease from the affected region's image at the initial stage of the skin disease. These findings suggest that the proposed system can help general practitioners efficiently and effectively diagnose skin conditions, thereby reducing further complications and morbidity.

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Classification of Skin Disease Using Deep Learning Neural Networks with MobileNet V2 and LSTM

Recent advancements in the Internet of Health Things (IoHT) have ushered in the wide adoption of IoT devices in our daily health management. For IoHT data to be acceptable by stakeholders, applications that incorporate the IoHT must have a provision for data provenance, in addition to the accuracy, security, integrity, and quality of data. To protect the privacy and security of IoHT data, federated learning (FL) and differential privacy (DP) have been proposed, where private IoHT data can be trained at the owner’s premises. Recent advancements in hardware GPUs even allow the FL process within smartphone or edge devices having the IoHT attached to their edge nodes. Although some of the privacy concerns of IoHT data are addressed by FL, fully decentralized FL is still a challenge due to the lack of training capability at all federated nodes, the scarcity of high-quality training datasets, the provenance of training data, and the authentication required for each FL node. In this paper, we present a lightweight hybrid FL framework in which blockchain smart contracts manage the edge training plan, trust management, and authentication of participating federated nodes, the distribution of global or locally trained models, the reputation of edge nodes and their uploaded datasets or models. The framework also supports the full encryption of a dataset, the model training, and the inferencing process. Each federated edge node performs additive encryption, while the blockchain uses multiplicative encryption to aggregate the updated model parameters. To support the full privacy and anonymization of the IoHT data, the framework supports lightweight DP. This framework was tested with several deep learning applications designed for clinical trials with COVID-19 patients. We present here the detailed design, implementation, and test results, which demonstrate strong potential for wider adoption of IoHT-based health management in a secure way.

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Secure and Provenance Enhanced Internet of Health Things Framework: A Blockchain Managed Federated Learning Approach

IoT-based telemedicine for disease prevention and health promotion: State-of-the-Art

The Internet-of-Things (IoT) network is growing big owing to its utility in smart applications. An IoT network is susceptible to security breaches, in majority due to the resource-constrained nature of IoT. Of the various breaches, the Distributed Denial-of-Service (DDoS) attack can snip off the network service to the users in various ways, such as consumption of server’s resources, saturating link bandwidth, etc. These types of DDoS breaches can turn out to be a catastrophe in critical IoT use cases. This article delves into tackling the DDoS attack triggered by malicious wireless IoT on IoT servers. Our security scheme leverages the cloud and software-defined network (SDN) paradigm to mitigate the DDoS attack on IoT servers. We have proposed a novel mechanism named learning-driven detection mitigation (LEDEM) that detects DDoS using a semisupervised machine-learning algorithm and mitigates DDoS. We tested LEDEM in the testbed and emulated topology, and compared the results with state-of-the-art solutions. We achieved an improved accuracy rate of 96.28% in detecting DDoS attack.

Learning-Driven Detection and Mitigation of DDoS Attack in IoT via SDN-Cloud Architecture

The popularity of smartphones has led to an increasing demand for health apps. As a result, the healthcare industry is embracing mobile technology and the security of mHealth is essential in protecting patient’s user data and WBAN in a clinical setting. Breaches of security can potentially be life-threatening as someone with malicious intentions could misuse mHealth devices and user information. In this article, threats to security for mHealth networks are discussed in a layered approach addressing gaps in this emerging field of research. Suite B and Suite E, which are utilized in many security systems, including in mHealth applications, are also discussed. In this paper, the support for mHealth security will follow two approaches; protecting patient-centric systems and associated link technologies. Therefore this article is focused on the security provisioning of the communication path between the patient terminal (PT; e.g., sensors) and the monitoring devices (e.g., smartphone, data-collector).

A review of security protocols in mHealth Wireless Body Area Networks (WBAN)

There has been an increasing prevalence of ad-hoc networks for various purposes and applications. These include Low Power Wide Area Networks (LPWAN) and Wireless Body Area Networks (WBAN) which have emerging applications in health monitoring as well as user location tracking in emergency settings. Further applications can include real-time actuation of IoT equipment, and activation of emergency alarms through the inference of a user’s situation using sensors and personal devices through a LPWAN. This has potential benefits for military networks and applications regarding the health of soldiers and field personnel during a mission. Due to the wireless nature of ad-hoc network devices, it is crucial to conserve battery power for sensors and equipment which transmit data to a central server. An inference system can be applied to devices to reduce data size for transfer and subsequently reduce battery consumption, however this could result in compromising accuracy. This paper presents a framework for secure automated messaging and data fusion as a solution to address the challenges of requiring data size reduction whilst maintaining a satisfactory accuracy rate. A Multilayer Inference System (MIS) was used to conserve the battery power of devices such as wearables and sensor devices. The results for this system showed a data reduction of 97.9% whilst maintaining satisfactory accuracy against existing single layer inference methods. Authentication accuracy can be further enhanced with additional biometrics and health data information.

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No Soldiers Left Behind: An IoT-Based Low-Power Military Mobile Health System Design

Affordable and expandable low power networks such as 5G and Low Power Wide Area Networks (LPWAN) in the public and private network areas have improved network bandwidth capacities and processing performance. Internet of Things (IoT) technologies are increasing in popularity with numerous applications and devices being developed for smart environments and health-related applications. This raises security concerns in these networks, as many IoT devices handle confidential information such as IP/MAC addresses, which could be used to identify a user's location. As a result, there is vulnerability to data tampering by man-in-the-middle (MITM) attacks, which feature two observable characteristics: (1) there is a measurable delay in the session and (2) has unusual travel times compared to prior normal transactions. To improve the detection of these attacks, this paper proposes a novel scheme using a hybrid routing mechanism, which involves appointing dedicated nodes for enforcing routing between IoT devices and users with minimal intervention and workload to the network. The function of dedicated devices with more computational and battery power can provide three advantages: (1) determine secured paths within the network by avoiding suspicious nodes and networks, (2) provide stable travel times (less fluctuations) for a trusted time server (TTS) to improve the accuracy of estimated travel times, and (3) provide packet inspection for security checks. This proposed solution contributes towards increasing the security of IoT networks by enabling the real-time detection of intruders.

Hybrid Routing for Man-in-the-Middle (MITM) Attack Detection in IoT Networks

Mobile Health (mHealth) is now emerging with Internet of Things (IoT), Cloud and big data along with the prevalence of smart wearable devices and sensors. There is also the emergence of smart environments such as smart homes, cars, highways, cities, factories and grids. Presently, it is difficult to quickly forecast or prevent urgent health situations in real-time as health data are analyzed offline by a physician. Sensors are expected to be overloaded by demands of providing health data from IoT networks and smart environments. This paper proposes to resolve the problems by introducing an inference system so that life-threatening situations can be prevented in advance based on a short and long term health status prediction. This prediction is inferred from personal health information that is built by big data in Cloud. The inference system can also resolve the problem of data overload in sensor nodes by reducing data volume and frequency to reduce workload in sensor nodes. This paper presents a novel idea of tracking down and predicting a personal health status as well as intelligent functionality of inference in sensor nodes to interface IoT networks.

James Jin Kang

Papers

Classification of Skin Disease Using Deep Learning Neural Networks with MobileNet V2 and LSTM

A review of security protocols in mHealth Wireless Body Area Networks (WBAN)

No Soldiers Left Behind: An IoT-Based Low-Power Military Mobile Health System Design

Hybrid Routing for Man-in-the-Middle (MITM) Attack Detection in IoT Networks

Predictive data mining for Converged Internet of Things: A Mobile Health perspective