False image injection prevention using iChain

The advances in information and communication technology are consistently beneficial for the healthcare sector. A trend in the healthcare sector is the progressive shift in how data are acquired and the storage of such data in different facilities, such as in the cloud, due to the efficiency and effectiveness offered. Digital images related to healthcare are sensitive in nature and require maximum security and privacy. A malicious entity can tamper with such stored digital images to mislead healthcare personnel and the consequences of wrong diagnosis are harmful for both parties. A new type of cyber attack, a false image injection attack (FIIA) is introduced in this paper. Existing image tampering detection measures are unable to guarantee tamper-proof medical data in real time. Inspired by the effectiveness of emerging blockchain technology, a security framework, image chain (iChain) is proposed in this paper to ensure the security and privacy of the sensitive healthcare images. The practical challenges associated with the proposed framework and further research that is required are also highlighted

Towards fostering the role of 5G networks in the field of digital health

A typical healthcare system needs further participation with patient monitoring, vital signs sensors and other medical devices. Healthcare moved from a traditional central hospital to scattered patients. Healthcare systems receive help from emerging technology innovations such as fifth generation (5G) communication infrastructure: internet of things (IoT), machine learning (ML), and artificial intelligence (AI). Healthcare providers benefit from IoT capabilities to comfort patients by using smart appliances that improve the healthcare level they receive. These IoT smart healthcare gadgets produce massive data volume. It is crucial to use very high-speed communication networks such as 5G wireless technology with the increased communication bandwidth, data transmission efficiency and reduced communication delay and latency, thus leading to strengthen the precise requirements of healthcare big data utilities. The adaptation of 5G in smart healthcare networks allows increasing number of IoT devices that supplies an augmentation in network performance. This paper reviewed distinctive aspects of internet of medical things (IoMT) and 5G architectures with their future and present sides, which can lead to improve healthcare of patients in the near future

Deep Learning Modalities for Biometric Alteration Detection in 5G Networks-Based Secure Smart Cities

Smart cities and their applications have become attractive research fields birthing numerous technologies. Fifth generation (5G) networks are important components of smart cities, where intelligent access control is deployed for identity authentication, online banking, and cyber security. To assure secure transactions and to protect user’s identities against cybersecurity threats, strong authentication techniques should be used. The prevalence of biometrics, such as fingerprints, in authentication and identification makes the need to safeguard them important across different areas of smart applications. Our study presents a system to detect alterations to biometric modalities to discriminate pristine, adulterated, and fake biometrics in 5G-based smart cities. Specifically, we use deep learning models based on convolutional neural networks (CNN) and a hybrid model that combines CNN with convolutional long-short term memory (ConvLSTM) to compute a three-tier probability that a biometric has been tempered. Simulation-based experiments indicate that the alteration detection accuracy matches those recorded in advanced methods with superior performance in terms of detecting central rotation alteration to fingerprints. This makes the proposed system a veritable solution for different biometric authentication applications in secure smart cities

Generation of artificial facial drug abuse images using Deep De-identified anonymous Dataset augmentation through Genetics Algorithm (3DG-GA)

In biomedical research and artificial intelligence, access to large, well-balanced, and representative datasets is crucial for developing trustworthy applications that can be used in real-world scenarios. However, obtaining such datasets can be challenging, as they are often restricted to hospitals and specialized facilities. To address this issue, the study proposes to generate highly realistic synthetic faces exhibiting drug abuse traits through augmentation. The proposed method, called "3DG-GA", Deep De-identified anonymous Dataset Generation, uses Genetics Algorithm as a strategy for synthetic faces generation. The algorithm includes GAN artificial face generation, forgery detection, and face recognition. Initially, a dataset of 120 images of actual facial drug abuse is used. By preserving, the drug traits, the 3DG-GA provides a dataset containing 3000 synthetic facial drug abuse images. The dataset will be open to the scientific community, which can reproduce our results and benefit from the generated datasets while avoiding legal or ethical restrictions

Measuring trustworthiness of image data in the internet of things environment

Internet of Things (IoT) image sensors generate huge volumes of digital images every day. However, easy availability and usability of photo editing tools, the vulnerability in communication channels and malicious software have made forgery attacks on image sensor data effortless and thus expose IoT systems to cyberattacks. In IoT applications such as smart cities and surveillance systems, the smooth operation depends on sensors’ sharing data with other sensors of identical or different types. Therefore, a sensor must be able to rely on the data it receives from other sensors; in other words, data must be trustworthy. Sensors deployed in IoT applications are usually limited to low processing and battery power, which prohibits the use of complex cryptography and security mechanism and the adoption of universal security standards by IoT device manufacturers. Hence, estimating the trust of the image sensor data is a defensive solution as these data are used for critical decision-making processes. To our knowledge, only one published work has estimated the trustworthiness of digital images applied to forensic applications. However, that study’s method depends on machine learning prediction scores returned by existing forensic models, which limits its usage where underlying forensics models require different approaches (e.g., machine learning predictions, statistical methods, digital signature, perceptual image hash). Multi-type sensor data correlation and context awareness can improve the trust measurement, which is absent in that study’s model. To address these issues, novel techniques are introduced to accurately estimate the trustworthiness of IoT image sensor data with the aid of complementary non-imagery (numeric) data-generating sensors monitoring the same environment. The trust estimation models run in edge devices, relieving sensors from computationally intensive tasks. First, to detect local image forgery (splicing and copy-move attacks), an innovative image forgery detection method is proposed based on Discrete Cosine Transformation (DCT), Local Binary Pattern (LBP) and a new feature extraction method using the mean operator. Using Support Vector Machine (SVM), the proposed method is extensively tested on four well-known publicly available greyscale and colour image forgery datasets and on an IoT-based image forgery dataset that we built. Experimental results reveal the superiority of our proposed method over recent state-of-the-art methods in terms of widely used performance metrics and computational time and demonstrate robustness against low availability of forged training samples. Second, a robust trust estimation framework for IoT image data is proposed, leveraging numeric data-generating sensors deployed in the same area of interest (AoI) in an indoor environment. As low-cost sensors allow many IoT applications to use multiple types of sensors to observe the same AoI, the complementary numeric data of one sensor can be exploited to measure the trust value of another image sensor’s data. A theoretical model is developed using Shannon’s entropy to derive the uncertainty associated with an observed event and Dempster-Shafer theory (DST) for decision fusion. The proposed model’s efficacy in estimating the trust score of image sensor data is analysed by observing a fire event using IoT image and temperature sensor data in an indoor residential setup under different scenarios. The proposed model produces highly accurate trust scores in all scenarios with authentic and forged image data. Finally, as the outdoor environment varies dynamically due to different natural factors (e.g., lighting condition variations in day and night, presence of different objects, smoke, fog, rain, shadow in the scene), a novel trust framework is proposed that is suitable for the outdoor environments with these contextual variations. A transfer learning approach is adopted to derive the decision about an observation from image sensor data, while also a statistical approach is used to derive the decision about the same observation from numeric data generated from other sensors deployed in the same AoI. These decisions are then fused using CertainLogic and compared with DST-based fusion. A testbed was set up using Raspberry Pi microprocessor, image sensor, temperature sensor, edge device, LoRa nodes, LoRaWAN gateway and servers to evaluate the proposed techniques. The results show that CertainLogic is more suitable for measuring the trustworthiness of image sensor data in an outdoor environment.Doctor of Philosoph

Statistical Review of Health Monitoring Models for Real-Time Hospital Scenarios

Health Monitoring System Models (HMSMs) need speed, efficiency, and security to work. Cascading components ensure data collection, storage, communication, retrieval, and privacy in these models. Researchers propose many methods to design such models, varying in scalability, multidomain efficiency, flexibility, usage and deployment, computational complexity, cost of deployment, security level, feature usability, and other performance metrics. Thus, HMSM designers struggle to find the best models for their application-specific deployments. They must test and validate different models, which increases design time and cost, affecting deployment feasibility. This article discusses secure HMSMs' application-specific advantages, feature-specific limitations, context-specific nuances, and deployment-specific future research scopes to reduce model selection ambiguity. The models based on the Internet of Things (IoT), Machine Learning Models (MLMs), Blockchain Models, Hashing Methods, Encryption Methods, Distributed Computing Configurations, and Bioinspired Models have better Quality of Service (QoS) and security than their counterparts. Researchers can find application-specific models. This article compares the above models in deployment cost, attack mitigation performance, scalability, computational complexity, and monitoring applicability. This comparative analysis helps readers choose HMSMs for context-specific application deployments. This article also devises performance measuring metrics called Health Monitoring Model Metrics (HM3) to compare the performance of various models based on accuracy, precision, delay, scalability, computational complexity, energy consumption, and security

IoT Health Devices: Exploring Security Risks in the Connected Landscape

The concept of the Internet of Things (IoT) spans decades, and the same can be said for its inclusion in healthcare. The IoT is an attractive target in medicine; it offers considerable potential in expanding care. However, the application of the IoT in healthcare is fraught with an array of challenges, and also, through it, numerous vulnerabilities that translate to wider attack surfaces and deeper degrees of damage possible to both consumers and their confidence within health systems, as a result of patient-specific data being available to access. Further, when IoT health devices (IoTHDs) are developed, a diverse range of attacks are possible. To understand the risks in this new landscape, it is important to understand the architecture of IoTHDs, operations, and the social dynamics that may govern their interactions. This paper aims to document and create a map regarding IoTHDs, lay the groundwork for better understanding security risks in emerging IoTHD modalities through a multi-layer approach, and suggest means for improved governance and interaction. We also discuss technological innovations expected to set the stage for novel exploits leading into the middle and latter parts of the 21st century