21 research outputs found
Proposition of Augmenting V2X Roadside Unit to Enhance Cooperative Awareness of Heterogeneously Connected Road Users
Intelligent transportation and autonomous mobility solutions rely on
cooperative awareness developed by exchanging proximity and mobility data among
road users. To maintain pervasive awareness on roads, all vehicles and
vulnerable road users must be identified, either cooperatively, where road
users equipped with wireless capabilities of Vehicle-to-Everything (V2X) radios
can communicate with one another, or passively, where users without V2X
capabilities are detected by means other than V2X communications. This
necessitates the establishment of a communications channel among all
V2X-enabled road users, regardless of whether their underlying V2X technology
is compatible or not. At the same time, for cooperative awareness to realize
its full potential, non-V2X-enabled road users must also be communicated with
where possible or, leastwise, be identified passively. However, the question is
whether current V2X technologies can provide such a welcoming heterogeneous
road environment for all parties, including varying V2X-enabled and
non-V2X-enabled road users? This paper investigates the roles of a
propositional concept named Augmenting V2X Roadside Unit (A-RSU) in enabling
heterogeneous vehicular networks to support and benefit from pervasive
cooperative awareness. To this end, this paper explores the efficacy of A-RSU
in establishing pervasive cooperative awareness and investigates the
capabilities of the available communication networks using secondary data. The
primary findings suggest that A-RSU is a viable solution for accommodating all
types of road users regardless of their V2X capabilities.Comment: 13 page
Blockchain-Enabled Federated Learning Approach for Vehicular Networks
Data from interconnected vehicles may contain sensitive information such as
location, driving behavior, personal identifiers, etc. Without adequate
safeguards, sharing this data jeopardizes data privacy and system security. The
current centralized data-sharing paradigm in these systems raises particular
concerns about data privacy. Recognizing these challenges, the shift towards
decentralized interactions in technology, as echoed by the principles of
Industry 5.0, becomes paramount. This work is closely aligned with these
principles, emphasizing decentralized, human-centric, and secure technological
interactions in an interconnected vehicular ecosystem. To embody this, we
propose a practical approach that merges two emerging technologies: Federated
Learning (FL) and Blockchain. The integration of these technologies enables the
creation of a decentralized vehicular network. In this setting, vehicles can
learn from each other without compromising privacy while also ensuring data
integrity and accountability. Initial experiments show that compared to
conventional decentralized federated learning techniques, our proposed approach
significantly enhances the performance and security of vehicular networks. The
system's accuracy stands at 91.92\%. While this may appear to be low in
comparison to state-of-the-art federated learning models, our work is
noteworthy because, unlike others, it was achieved in a malicious vehicle
setting. Despite the challenging environment, our method maintains high
accuracy, making it a competent solution for preserving data privacy in
vehicular networks.Comment: 7 page
Migrating to Post-Quantum Cryptography: a Framework Using Security Dependency Analysis
Quantum computing is emerging as an unprecedented threat to the current state
of widely used cryptographic systems. Cryptographic methods that have been
considered secure for decades will likely be broken, with enormous impact on
the security of sensitive data and communications in enterprises worldwide. A
plan to migrate to quantum-resistant cryptographic systems is required.
However, migrating an enterprise system to ensure a quantum-safe state is a
complex process. Enterprises will require systematic guidance to perform this
migration to remain resilient in a post-quantum era, as many organisations do
not have staff with the expertise to manage this process unaided. This paper
presents a comprehensive framework designed to aid enterprises in their
migration. The framework articulates key steps and technical considerations in
the cryptographic migration process. It makes use of existing organisational
inventories and provides a roadmap for prioritising the replacement of
cryptosystems in a post-quantum context. The framework enables the efficient
identification of cryptographic objects, and can be integrated with other
frameworks in enterprise settings to minimise operational disruption during
migration. Practical case studies are included to demonstrate the utility and
efficacy of the proposed framework using graph theoretic techniques to
determine and evaluate cryptographic dependencies.Comment: 21 Page
HARDC : A novel ECG-based heartbeat classification method to detect arrhythmia using hierarchical attention based dual structured RNN with dilated CNN
In this paper have developed a novel hybrid hierarchical attention-based
bidirectional recurrent neural network with dilated CNN (HARDC) method for
arrhythmia classification. This solves problems that arise when traditional
dilated convolutional neural network (CNN) models disregard the correlation
between contexts and gradient dispersion. The proposed HARDC fully exploits the
dilated CNN and bidirectional recurrent neural network unit (BiGRU-BiLSTM)
architecture to generate fusion features. As a result of incorporating both
local and global feature information and an attention mechanism, the model's
performance for prediction is improved.By combining the fusion features with a
dilated CNN and a hierarchical attention mechanism, the trained HARDC model
showed significantly improved classification results and interpretability of
feature extraction on the PhysioNet 2017 challenge dataset. Sequential Z-Score
normalization, filtering, denoising, and segmentation are used to prepare the
raw data for analysis. CGAN (Conditional Generative Adversarial Network) is
then used to generate synthetic signals from the processed data. The
experimental results demonstrate that the proposed HARDC model significantly
outperforms other existing models, achieving an accuracy of 99.60\%, F1 score
of 98.21\%, a precision of 97.66\%, and recall of 99.60\% using MIT-BIH
generated ECG. In addition, this approach substantially reduces run time when
using dilated CNN compared to normal convolution. Overall, this hybrid model
demonstrates an innovative and cost-effective strategy for ECG signal
compression and high-performance ECG recognition. Our results indicate that an
automated and highly computed method to classify multiple types of arrhythmia
signals holds considerable promise.Comment: 23 page
A Dependable Hybrid Machine Learning Model for Network Intrusion Detection
Network intrusion detection systems (NIDSs) play an important role in
computer network security. There are several detection mechanisms where
anomaly-based automated detection outperforms others significantly. Amid the
sophistication and growing number of attacks, dealing with large amounts of
data is a recognized issue in the development of anomaly-based NIDS. However,
do current models meet the needs of today's networks in terms of required
accuracy and dependability? In this research, we propose a new hybrid model
that combines machine learning and deep learning to increase detection rates
while securing dependability. Our proposed method ensures efficient
pre-processing by combining SMOTE for data balancing and XGBoost for feature
selection. We compared our developed method to various machine learning and
deep learning algorithms to find a more efficient algorithm to implement in the
pipeline. Furthermore, we chose the most effective model for network intrusion
based on a set of benchmarked performance analysis criteria. Our method
produces excellent results when tested on two datasets, KDDCUP'99 and
CIC-MalMem-2022, with an accuracy of 99.99% and 100% for KDDCUP'99 and
CIC-MalMem-2022, respectively, and no overfitting or Type-1 and Type-2 issues.Comment: Accepted in the Journal of Information Security and Applications
(Scopus, Web of Science (SCIE) Journal, Quartile: Q1, Site Score: 7.6, Impact
Factor: 4.96) on 7 December 202
GNSS time synchronisation in co-operative vehicular networks
This thesis is a comprehensive study of time synchronisation issues in vehicular communication networks. It reviews the requirements of time synchronization in cooperative vehicular networks and examines the feasibility of Global Navigation Satellite System (GNSS) timing techniques for synchronising the networks. Results from experiments show that GNSS time synchronisation methods can replace existing time synchronisation function (TSF) based synchronisation in vehicular networks by offering high precision and high accuracy