Ahmed Al-Dubai

Sarah Al-Shareeda, Yasar Celik, Bilge Bilgili et al.

Creating a Digital Twin (DT) for Healthcare Intelligent Transportation Systems (HITS) is a hot research trend focusing on enhancing HITS management, particularly in emergencies where ambulance vehicles must arrive at the crash scene on time and track their real-time location is crucial to the medical authorities. Despite the claim of real-time representation, a temporal misalignment persists between the physical and virtual domains, leading to discrepancies in the ambulance's location representation. This study proposes integrating AI predictive models, specifically Support Vector Regression (SVR) and Deep Neural Networks (DNN), within a constructed mock DT data pipeline framework to anticipate the medical vehicle's next location in the virtual world. These models align virtual representations with their physical counterparts, i.e., metaphorically offsetting the synchronization delay between the two worlds. Trained meticulously on a historical geospatial dataset, SVR and DNN exhibit exceptional prediction accuracy in MATLAB and Python environments. Through various testing scenarios, we visually demonstrate the efficacy of our methodology, showcasing SVR and DNN's key role in significantly reducing the witnessed gap within the HITS's DT. This transformative approach enhances real-time synchronization in emergency HITS by approximately 88% to 93%.

Awais Aziz Shah, Ahsan Adeel, Jawad Ahmad et al.

Multimodal hearing aids (HAs) aim to deliver more intelligible audio in noisy environments by contextually sensing and processing data in the form of not only audio but also visual information (e.g. lip reading). Machine learning techniques can play a pivotal role for the contextually processing of multimodal data. However, since the computational power of HA devices is low, therefore this data must be processed either on the edge or cloud which, in turn, poses privacy concerns for sensitive user data. Existing literature proposes several techniques for data encryption but their computational complexity is a major bottleneck to meet strict latency requirements for development of future multi-modal hearing aids. To overcome this problem, this paper proposes a novel real-time audio/visual data encryption scheme based on chaos-based encryption using the Tangent-Delay Ellipse Reflecting Cavity-Map System (TD-ERCS) map and Non-linear Chaotic (NCA) Algorithm. The results achieved against different security parameters, including Correlation Coefficient, Unified Averaged Changed Intensity (UACI), Key Sensitivity Analysis, Number of Changing Pixel Rate (NPCR), Mean-Square Error (MSE), Peak Signal to Noise Ratio (PSNR), Entropy test, and Chi-test, indicate that the newly proposed scheme is more lightweight due to its lower execution time as compared to existing schemes and more secure due to increased key-space against modern brute-force attacks.

Nilupulee A. Gunathilake, Ahmed Al-Dubai, William J. Buchanan et al.

Lightweight cryptography is a novel diversion from conventional cryptography that targets internet-of-things (IoT) platform due to resource constraints. In comparison, it offers smaller cryptographic primitives such as shorter key sizes, block sizes and lesser energy drainage. The main focus can be seen in algorithm developments in this emerging subject. Thus, verification is carried out based upon theoretical (mathematical) proofs mostly. Among the few available side-channel analysis studies found in literature, the highest percentage is taken by power attacks. PRESENT is a promising lightweight block cipher to be included in IoT devices in the near future. Thus, the emphasis of this paper is on lightweight cryptology, and our investigation shows unavailability of a correlation electromagnetic analysis (CEMA) of it. Hence, in an effort to fill in this research gap, we opted to investigate the capabilities of CEMA against the PRESENT algorithm. This work aims to determine the probability of secret key leakage with a minimum number of electromagnetic (EM) waveforms possible. The process initially started from a simple EM analysis (SEMA) and gradually enhanced up to a CEMA. This paper presents our methodology in attack modelling, current results that indicate a probability of leaking seven bytes of the key and upcoming plans for optimisation. In addition, introductions to lightweight cryptanalysis and theories of EMA are also included.

Mwrwan Abubakar, Pádraig McCarron, Zakwan Jaroucheh et al.

The COVID-19 pandemic has recently emerged as a worldwide health emergency that necessitates coordinated international measures. To contain the virus's spread, governments and health organisations raced to develop vaccines that would lower Covid-19 morbidity, relieve pressure on healthcare systems, and allow economies to open. As a way forward after the COVID-19 vaccination, the Vaccination certificate has been adopted to help the authorities formulate policies by controlling cross-border travelling. To resolve significant privacy concerns and remove the need for relying on third parties to maintain trust and control the user's data, in this paper, we leverage blockchain technologies in developing a secure and verifiable vaccination certificate. Our approach has the advantage of utilising a hybrid architecture that implements different advanced technologies, such as smart contracts, interPlanetary File System (IPFS), and Self-sovereign Identity (SSI). We will rely on verifiable credentials paired with smart contracts to implement on-chain access control decisions and provide on-chain verification and validation of the user and issuer DIDs. The usability of this approach was further analysed, particularly concerning performance and security. Our analysis proved that our approach satisfies vaccination certificate security requirements.

Nilupulee A. Gunathilake, Ahmed Al-Dubai, William J. Buchanan et al.

Side-channel attacks are an unpredictable risk factor in cryptography. Therefore, continuous observations of physical leakages are essential to minimise vulnerabilities associated with cryptographic functions. Lightweight cryptography is a novel approach in progress towards internet-of-things (IoT) security. Thus, it would provide sufficient data and privacy protection in such a constrained ecosystem. IoT devices are resource-limited in terms of data rates (in kbps), power maintainability (battery) as well as hardware and software footprints (physical size, internal memory, RAM/ROM). Due to the difficulty in handling conventional cryptographic algorithms, lightweight ciphers consist of small key sizes, block sizes and few operational rounds. Unlike in the past, affordability to perform side-channel attacks using inexpensive electronic circuitries is becoming a reality. Hence, cryptanalysis of physical leakage in these emerging ciphers is crucial. Among existing studies, power analysis seems to have enough attention in research, whereas other aspects such as electromagnetic, timing, cache and optical attacks continue to be appropriately evaluated to play a role in forensic analysis. As a result, we started analysing electromagnetic emission leakage of an ultra-lightweight block cipher, PRESENT. According to the literature, PRESENT promises to be adequate for IoT devices, and there still seems not to exist any work regarding correlation electromagnetic analysis (CEMA) of it. Firstly, we conducted simple electromagnetic analysis in both time and frequency domains and then proceeded towards CEMA attack modelling. This paper provides a summary of the related literature (IoT, lightweight cryptography, side-channel attacks and EMA), our methodology, current outcomes and future plans for the optimised results.