1,541 research outputs found
Authentication enhancement in command and control networks: (a study in Vehicular Ad-Hoc Networks)
Intelligent transportation systems contribute to improved traffic safety by facilitating real time communication between vehicles. By using wireless channels for communication, vehicular networks are susceptible to a wide range of attacks, such as impersonation, modification, and replay. In this context, securing data exchange between intercommunicating terminals, e.g., vehicle-to-everything (V2X) communication, constitutes a technological challenge that needs to be addressed. Hence, message authentication is crucial to safeguard vehicular ad-hoc networks (VANETs) from malicious attacks. The current state-of-the-art for authentication in VANETs relies on conventional cryptographic primitives, introducing significant computation and communication overheads. In this challenging scenario, physical (PHY)-layer authentication has gained popularity, which involves leveraging the inherent characteristics of wireless channels and the hardware imperfections to discriminate between wireless devices. However, PHY-layerbased authentication cannot be an alternative to crypto-based methods as the initial legitimacy detection must be conducted using cryptographic methods to extract the communicating terminal secret features. Nevertheless, it can be a promising complementary solution for the reauthentication problem in VANETs, introducing what is known as “cross-layer authentication.” This thesis focuses on designing efficient cross-layer authentication schemes for VANETs, reducing the communication and computation overheads associated with transmitting and verifying a crypto-based signature for each transmission. The following provides an overview of the proposed methodologies employed in various contributions presented in this thesis.
1. The first cross-layer authentication scheme: A four-step process represents this approach: initial crypto-based authentication, shared key extraction, re-authentication via a PHY challenge-response algorithm, and adaptive adjustments based on channel conditions. Simulation results validate its efficacy, especially in low signal-to-noise ratio (SNR) scenarios while proving its resilience against active and passive attacks.
2. The second cross-layer authentication scheme: Leveraging the spatially and temporally correlated wireless channel features, this scheme extracts high entropy shared keys that can be used to create dynamic PHY-layer signatures for authentication. A 3-Dimensional (3D) scattering Doppler emulator is designed to investigate the scheme’s performance at different speeds of a moving vehicle and SNRs. Theoretical and hardware implementation analyses prove the scheme’s capability to support high detection probability for an acceptable false alarm value ≤ 0.1 at SNR ≥ 0 dB and speed ≤ 45 m/s.
3. The third proposal: Reconfigurable intelligent surfaces (RIS) integration for improved authentication: Focusing on enhancing PHY-layer re-authentication, this proposal explores integrating RIS technology to improve SNR directed at designated vehicles. Theoretical analysis and practical implementation of the proposed scheme are conducted using a 1-bit RIS, consisting of 64 × 64 reflective units. Experimental results show a significant improvement in the Pd, increasing from 0.82 to 0.96 at SNR = − 6 dB for multicarrier communications.
4. The fourth proposal: RIS-enhanced vehicular communication security: Tailored for challenging SNR in non-line-of-sight (NLoS) scenarios, this proposal optimises key extraction and defends against denial-of-service (DoS) attacks through selective signal strengthening. Hardware implementation studies prove its effectiveness, showcasing improved key extraction performance and resilience against potential threats.
5. The fifth cross-layer authentication scheme: Integrating PKI-based initial legitimacy detection and blockchain-based reconciliation techniques, this scheme ensures secure data exchange. Rigorous security analyses and performance evaluations using network simulators and computation metrics showcase its effectiveness, ensuring its resistance against common attacks and time efficiency in message verification.
6. The final proposal: Group key distribution: Employing smart contract-based blockchain technology alongside PKI-based authentication, this proposal distributes group session keys securely. Its lightweight symmetric key cryptography-based method maintains privacy in VANETs, validated via Ethereum’s main network (MainNet) and comprehensive computation and communication evaluations.
The analysis shows that the proposed methods yield a noteworthy reduction, approximately ranging from 70% to 99%, in both computation and communication overheads, as compared to the conventional approaches. This reduction pertains to the verification and transmission of 1000 messages in total
Information Encoding for Flow Watermarking and Binding Keys to Biometric Data
Due to the current level of telecommunications development, fifth-generation (5G) communication systems are expected to provide higher data rates, lower latency, and improved scalability. To ensure the security and reliability of data traffic generated from wireless sources, 5G networks must be designed to support security protocols and reliable communication applications. The operations of coding and processing of information during the transmission of both binary and non-binary data in nonstandard communication channels are described. A subclass of linear binary codes is considered, which are both Varshamov-Tenengolz codes and are used for channels with insertions and deletions of symbols. The use of these codes is compared with Hidden Markov Model (HMM)-based systems for detecting intrusions in networks using flow watermarking, which provide high true positive rate in both cases. The principles of using Bose-Chadhuri-Hocquenhgem (BCH) codes, non-binary Reed-Solomon codes, and turbo codes, as well as concatenated code structures to ensure noise immunity when reproducing information in Helper-Data Systems are considered. Examples of biometric systems organization based on the use of these codes, operating on the basis of the Fuzzy Commitment Scheme (FCS) and providing FRR < 1% for authentication, are given
The Appropriation of Value from Knowledge: Three Essays on Technological Discontinuities, Market Entry, and Patent Strategy
Knowledge accumulation and protection are critical considerations of the firm. How does the capability to appropriate value from knowledge affect firm strategies in the industries? To answer this question, I develop a new theory and evidence to argue that appropriate value from knowledge is a central consideration in firms’ capabilities and decisions to deal with technological changes and intellectual property issues. In particular, I examine the relatedness of products and markets, the strategic uses of patents, and how firms can successfully adapt to concerns regarding technological changes and intellectual property leakage. Throughout my three dissertation chapters, I find evidence that the capability to appropriate value from knowledge can affect how firms behave in consistent and essential ways. These findings provide important implications for knowledge-based views of the firm and strategy-based recommendations in terms of the management of knowledge assets
Fuzzy Logic Weighted Averaging Algorithm for Malaysian Banknotes Reader Featuring Counterfeit Detection
This paper proposed a novel fuzzy logic weighted averaging (FLWA) algorithm in image processing techniques to detect counterfeit Malaysian banknotes. Image acquisition techniques on banknote position detection and re-adjustment, image pre-processing techniques, feature extraction methods on Malaysian banknotes’ watermarks are also covered in the paper. The FLWA Algorithm has the advantage of a much simpler model since it is a human guidance learning algorithm that does not require enrolment process to get the specific weights for each security feature. Each security feature is treated with equal weight. The experimental results also shown that FLWA model also outperform the MobileNet model and VGG16 model in Malaysian banknotes’ counterfeit detection. It has a distinct advantage over earlier or current banknote counterfeit detection techniques in that it adopted the known watermarks features, with known machine learning techniques to identify real Malaysian banknotes and detect those counterfeit Malaysian banknotes
From Zero to Hero: Detecting Leaked Data through Synthetic Data Injection and Model Querying
Safeguarding the Intellectual Property (IP) of data has become critically
important as machine learning applications continue to proliferate, and their
success heavily relies on the quality of training data. While various
mechanisms exist to secure data during storage, transmission, and consumption,
fewer studies have been developed to detect whether they are already leaked for
model training without authorization. This issue is particularly challenging
due to the absence of information and control over the training process
conducted by potential attackers.
In this paper, we concentrate on the domain of tabular data and introduce a
novel methodology, Local Distribution Shifting Synthesis (\textsc{LDSS}), to
detect leaked data that are used to train classification models. The core
concept behind \textsc{LDSS} involves injecting a small volume of synthetic
data--characterized by local shifts in class distribution--into the owner's
dataset. This enables the effective identification of models trained on leaked
data through model querying alone, as the synthetic data injection results in a
pronounced disparity in the predictions of models trained on leaked and
modified datasets. \textsc{LDSS} is \emph{model-oblivious} and hence compatible
with a diverse range of classification models, such as Naive Bayes, Decision
Tree, and Random Forest. We have conducted extensive experiments on seven types
of classification models across five real-world datasets. The comprehensive
results affirm the reliability, robustness, fidelity, security, and efficiency
of \textsc{LDSS}.Comment: 13 pages, 11 figures, and 4 table
The 2023 wearable photoplethysmography roadmap
Photoplethysmography is a key sensing technology which is used in wearable devices such as smartwatches and fitness trackers. Currently, photoplethysmography sensors are used to monitor physiological parameters including heart rate and heart rhythm, and to track activities like sleep and exercise. Yet, wearable photoplethysmography has potential to provide much more information on health and wellbeing, which could inform clinical decision making. This Roadmap outlines directions for research and development to realise the full potential of wearable photoplethysmography. Experts discuss key topics within the areas of sensor design, signal processing, clinical applications, and research directions. Their perspectives provide valuable guidance to researchers developing wearable photoplethysmography technology
Cybersecurity: Past, Present and Future
The digital transformation has created a new digital space known as
cyberspace. This new cyberspace has improved the workings of businesses,
organizations, governments, society as a whole, and day to day life of an
individual. With these improvements come new challenges, and one of the main
challenges is security. The security of the new cyberspace is called
cybersecurity. Cyberspace has created new technologies and environments such as
cloud computing, smart devices, IoTs, and several others. To keep pace with
these advancements in cyber technologies there is a need to expand research and
develop new cybersecurity methods and tools to secure these domains and
environments. This book is an effort to introduce the reader to the field of
cybersecurity, highlight current issues and challenges, and provide future
directions to mitigate or resolve them. The main specializations of
cybersecurity covered in this book are software security, hardware security,
the evolution of malware, biometrics, cyber intelligence, and cyber forensics.
We must learn from the past, evolve our present and improve the future. Based
on this objective, the book covers the past, present, and future of these main
specializations of cybersecurity. The book also examines the upcoming areas of
research in cyber intelligence, such as hybrid augmented and explainable
artificial intelligence (AI). Human and AI collaboration can significantly
increase the performance of a cybersecurity system. Interpreting and explaining
machine learning models, i.e., explainable AI is an emerging field of study and
has a lot of potentials to improve the role of AI in cybersecurity.Comment: Author's copy of the book published under ISBN: 978-620-4-74421-
Improving Classification in Single and Multi-View Images
Image classification is a sub-field of computer vision that focuses on identifying objects within digital images. In order to improve image classification we must address the following areas of improvement: 1) Single and Multi-View data quality using data pre-processing techniques. 2) Enhancing deep feature learning to extract alternative representation of the data. 3) Improving decision or prediction of labels. This dissertation presents a series of four published papers that explore different improvements of image classification. In our first paper, we explore the Siamese network architecture to create a Convolution Neural Network based similarity metric. We learn the priority features that differentiate two given input images. The metric proposed achieves state-of-the-art Fβ measure. In our second paper, we explore multi-view data classification. We investigate the application of Generative Adversarial Networks GANs on Multi-view data image classification and few-shot learning. Experimental results show that our method outperforms state-of-the-art research. In our third paper, we take on the challenge of improving ResNet backbone model. For this task, we focus on improving channel attention mechanisms. We utilize Discrete Wavelet Transform compression to address the channel representation problem. Experimental results on ImageNet shows that our method outperforms baseline SENet-34 and SOTA FcaNet-34 at no extra computational cost. In our fourth paper, we investigate further the potential of orthogonalization of filters for extraction of diverse information for channel attention. We prove that using only random constant orthogonal filters is sufficient enough to achieve good channel attention. We test our proposed method using ImageNet, Places365, and Birds datasets for image classification, MS-COCO for object detection, and instance segmentation tasks. Our method outperforms FcaNet, and WaveNet and achieves the state-of-the-art results
A Tutorial on Coding Methods for DNA-based Molecular Communications and Storage
Exponential increase of data has motivated advances of data storage
technologies. As a promising storage media, DeoxyriboNucleic Acid (DNA) storage
provides a much higher data density and superior durability, compared with
state-of-the-art media. In this paper, we provide a tutorial on DNA storage and
its role in molecular communications. Firstly, we introduce fundamentals of
DNA-based molecular communications and storage (MCS), discussing the basic
process of performing DNA storage in MCS. Furthermore, we provide tutorials on
how conventional coding schemes that are used in wireless communications can be
applied to DNA-based MCS, along with numerical results. Finally, promising
research directions on DNA-based data storage in molecular communications are
introduced and discussed in this paper
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