28 research outputs found
Cyber Physical System Based Smart Healthcare System with Federated Deep Learning Architectures with Data Analytics
Data shared between hospitals and patients using mobile and wearable Internet of Medical Things (IoMT) devices raises privacy concerns due to the methods used in training. the development of the Internet of Medical Things (IoMT) and related technologies and the most current advances in these areas The Internet of Medical Things and other recent technological advancements have transformed the traditional healthcare system into a smart one. improvement in computing power and the spread of information have transformed the healthcare system into a high-tech, data-driven operation. On the other hand, mobile and wearable IoMT devices present privacy concerns regarding the data transmitted between hospitals and end users because of the way in which artificial intelligence is trained (AI-centralized). In terms of machine learning (AI-centralized). Devices connected to the IoMT network transmit highly confidential information that could be intercepted by adversaries. Due to the portability of electronic health record data for clinical research made possible by medical cyber-physical systems, the rate at which new scientific discoveries can be made has increased. While AI helps improve medical informatics, the current methods of centralised data training and insecure data storage management risk exposing private medical information to unapproved foreign organisations. New avenues for protecting users' privacy in IoMT without requiring access to their data have been opened by the federated learning (FL) distributive AI paradigm. FL safeguards user privacy by concealing all but gradients during training. DeepFed is a novel Federated Deep Learning approach presented in this research for the purpose of detecting cyber threats to intelligent healthcare CPSs
Blockchain-assisted Undisclosed IIoT Vulnerabilities Trusted Sharing Protection with Dynamic Token
With the large-scale deployment of industrial internet of things (IIoT)
devices, the number of vulnerabilities that threaten IIoT security is also
growing dramatically, including a mass of undisclosed IIoT vulnerabilities that
lack mitigation measures. Coordination Vulnerabilities Disclosure (CVD) is one
of the most popular vulnerabilities sharing solutions, in which some security
workers (SWs) can develop undisclosed vulnerabilities patches together.
However, CVD assumes that sharing participants (SWs) are all honest, and thus
offering chances for dishonest SWs to leak undisclosed IIoT vulnerabilities. To
combat such threats, we propose an Undisclosed IIoT Vulnerabilities Trusted
Sharing Protection (UIV-TSP) scheme with dynamic token. In this article, a
dynamic token is an implicit access credential for an SW to acquire an
undisclosed vulnerability information, which is only held by the system and
constantly updated as the SW access. Meanwhile, the latest updated token can be
stealthily sneaked into the acquired information as the traceability token.
Once the undisclosed vulnerability information leaves the SW host, the embedded
self-destruct program will be automatically triggered to prevent leaks since
the destination MAC address in the traceability token has changed. To quickly
distinguish dishonest SWs, trust mechanism is adopted to evaluate the trust
value of SWs. Moreover, we design a blockchain-assisted continuous logs storage
method to achieve the tamper-proofing of dynamic token and the transparency of
undisclosed IIoT vulnerabilities sharing. The simulation results indicate that
our proposed scheme is resilient to suppress dishonest SWs and protect the IoT
undisclosed vulnerabilities effectively.Comment: 10 pages,12 figure
Strengthening Smart Contracts: An AI-Driven Security Exploration
Smart contracts are automated agreements in which the conditions between the purchaser and the vendor are encoded directly into lines of code allowing them to execute automatically Smart contracts have emerged as a ground-breaking technology facilitating the decentralized and trustless execution of agreements on blockchain platforms However the widespread adoption of smart contracts exposes them to various security threats leading to substantial financial losses and reputational harm Artificial Intelligence has the capability to aid in the detection and reduction of vulnerabilities thereby enhancing the overall strength and resilience of smart contracts This integration can create highly secure and transparent systems that reduce the risk of fraud corruption and other malicious activities thereby increasing trust and confidence in these systems and improving overall security This research paper delves into the innovative applications of Artificial Intelligence techniques to enhance the security of smart contracts Investigating the potential of AI in detecting vulnerabilities identifying potential attacks and offering automated solutions for safer smart contracts will significantly contribute to the development and flawless execution of this emerging technolog
Enhancing Confidentiality and Privacy Preservation in e-Health to Enhanced Security
Electronic health (e-health) system use is growing, which has improved healthcare services significantly but has created questions about the privacy and security of sensitive medical data. This research suggests a novel strategy to overcome these difficulties and strengthen the security of e-health systems while maintaining the privacy and confidentiality of patient data by utilising machine learning techniques. The security layers of e-health systems are strengthened by the comprehensive framework we propose in this paper, which incorporates cutting-edge machine learning algorithms. The suggested framework includes data encryption, access control, and anomaly detection as its three main elements. First, to prevent unauthorised access during transmission and storage, patient data is secured using cutting-edge encryption technologies. Second, to make sure that only authorised staff can access sensitive medical records, access control mechanisms are strengthened using machine learning models that examine user behaviour patterns. This research's inclusion of machine learning-based anomaly detection is its most inventive feature. The technology may identify variations from typical data access and usage patterns, thereby quickly spotting potential security breaches or unauthorised activity, by training models on past e-health data. This proactive strategy improves the system's capacity to successfully address new threats. Extensive experiments were carried out employing a broad dataset made up of real-world e-health scenarios to verify the efficacy of the suggested approach. The findings showed a marked improvement in the protection of confidentiality and privacy, along with a considerable decline in security breaches and unauthorised access events
Digital Twins and Blockchain for IoT Management
Security and privacy are primary concerns in IoT management. Security
breaches in IoT resources, such as smart sensors, can leak sensitive data and
compromise the privacy of individuals. Effective IoT management requires a
comprehensive approach to prioritize access security and data privacy
protection. Digital twins create virtual representations of IoT resources.
Blockchain adds decentralization, transparency, and reliability to IoT systems.
This research integrates digital twins and blockchain to manage access to IoT
data streaming. Digital twins are used to encapsulate data access and view
configurations. Access is enabled on digital twins, not on IoT resources
directly. Trust structures programmed as smart contracts are the ones that
manage access to digital twins. Consequently, IoT resources are not exposed to
third parties, and access security breaches can be prevented. Blockchain has
been used to validate digital twins and store their configuration. The research
presented in this paper enables multitenant access and customization of data
streaming views and abstracts the complexity of data access management. This
approach provides access and configuration security and data privacy
protection.Comment: Reference: Mayra, Samaniego and Ralph, Deters. 2023. Digital Twins
and Blockchain for IoT Management. In The 5th ACM International Symposium on
Blockchain and Secure Critical Infrastructure (BSCI '23), July 10-14, 2023,
Melbourne, VIC, Australia. ACM, New York, NY, USA, 11 pages.
https://doi.org/10.1145/3594556.359461
SCEI: A Smart-Contract Driven Edge Intelligence Framework for IoT Systems
Federated learning (FL) utilizes edge computing devices to collaboratively
train a shared model while each device can fully control its local data access.
Generally, FL techniques focus on learning model on independent and identically
distributed (iid) dataset and cannot achieve satisfiable performance on non-iid
datasets (e.g. learning a multi-class classifier but each client only has a
single class dataset). Some personalized approaches have been proposed to
mitigate non-iid issues. However, such approaches cannot handle underlying data
distribution shift, namely data distribution skew, which is quite common in
real scenarios (e.g. recommendation systems learn user behaviors which change
over time). In this work, we provide a solution to the challenge by leveraging
smart-contract with federated learning to build optimized, personalized deep
learning models. Specifically, our approach utilizes smart contract to reach
consensus among distributed trainers on the optimal weights of personalized
models. We conduct experiments across multiple models (CNN and MLP) and
multiple datasets (MNIST and CIFAR-10). The experimental results demonstrate
that our personalized learning models can achieve better accuracy and faster
convergence compared to classic federated and personalized learning. Compared
with the model given by baseline FedAvg algorithm, the average accuracy of our
personalized learning models is improved by 2% to 20%, and the convergence rate
is about 2 faster. Moreover, we also illustrate that our approach is
secure against recent attack on distributed learning.Comment: 12 pages, 9 figure