Journal of Informatics and Web Engineering

Editorial Preview

Su-Cheng Haw — 2024-10-14

Effective from Volume 3, JIWE has transitioned to a triannual publication release effort. Specifically, releases occur each February, June, and October. This particular October 2024 release contains 12 papers within the regular section that covers a broad range of application concerning Machine Learning (ML), Artificial Intelligence (AI), Data Mining (DM), Software Engineering, Recommender Systems, Cybersecurity, Healthcare, and other key areas in web engineering. Additionally, this edition presents a captivating collection of 7 papers curated by our Thematic Editor, Dr. Ji-Jian Chin, under the theme "Pervasive Computing." In his editorial, Dr. Ji-Jian Chin highlights cutting-edge research on the integration of computing into everyday environments. Moreover, these papers are aligned to some of the United Nations Sustainable Development Goals (SDGs), particularly SDG 3 (Good Health and Well-being) through advancements in healthcare technologies, SDG 9 (Industry, Innovation, and Infrastructure) through software and systems innovation research, and SDG 16 (Peace, Justice, and Strong Institutions) through contributions to privacy and cybersecurity research.

MobiTest – A Software for Mobile-Based Testing

Ayesha Anees Zaveri — 2024-10-14

MobiTest is an application that serves as a valuable tool in the fast-growing field of software testing. Efficiency is crucial in this industry, where testers, quality assurance teams, and others must meticulously test each application, avoiding the need to repeat the entire cycle to identify bugs. This application is a breeze thanks to its intuitive features and educational content. Thanks to continuous integration, testers can easily keep up with the fast-paced development cycle and start automating tasks as soon as the user interface development is completed. This saves valuable time and ensures a smoother and more efficient process. During the development of this application, a need arose for manual testing, which unfortunately resulted in the inefficient use of resources. MobiTest was designed to overcome these limitations by providing the ability to generate generic test scripts for any application as needed. It can efficiently and adaptively handle intricate tasks according to predefined parameters. This application thoroughly examines every possible detail, allowing the hacker to exploit the system.

Enhancing Cybersecurity Awareness through Gamification: Design an Interactive Cybersecurity Learning Platform for Multimedia University Students

Adlil Khaliq Abdul Razack — 2024-10-14

Cybersecurity has emerged as a critical imperative in contemporary digital landscapes, necessitating heightened awareness and proficiency across all demographic segments. Accordingly, this research has been meticulously crafted to delve into the complexities of cybersecurity awareness, with a specific focus on university students. The study embarks on an exhaustive analysis encompassing the evaluation of cybersecurity awareness levels within the targeted groups, the identification of prevailing issues and practices, and an exploration of novel methodologies, notably gamification, to fortify cybersecurity knowledge and skills among diverse user cohorts. Central to this investigation is the efficacy of gamified learning environments tailored expressly for augmenting cybersecurity awareness among university students. Through a comprehensive examination of existing platforms, methodological frameworks, and user interactions, this research outlines critical trends, challenges, and latent opportunities within the cybersecurity awareness domain, with a specific emphasis on gamification's transformative potential. The study not only identifies key areas for improvement but also proposes innovative solutions rooted in gamified learning paradigms, with the overarching goal of fostering engaging, effective, and sustainable cybersecurity awareness initiatives among students. Drawing upon a synthesis of theoretical constructs, empirical insights, and pragmatic recommendations, this research significantly contributes to the evolving discourse on cybersecurity education. By underscoring the transformative efficacy of gamification as a pivotal tool in cybersecurity awareness initiatives, this study overlays the way for substantial advancements in cybersecurity education paradigms, offering a roadmap for enhancing cybersecurity awareness levels among university students and beyond.

Treatment Recommendation using BERT Personalization

J Jayapradha — 2024-10-14

This research work develops a new framework that combines patient feedback with evidence-based best practices across disease states to improve drug recommendations. It employs BERT as its free-text processing engine to deal with sentiment judgment and classification. The functionality of the system, named `PharmaBERT`, includes acceptance of drug review data as a comprehensive input, drug categorization when dealing with a wide range of treatments and fine-tuning the BERT-based model for gaining positive or negative sentiment towards specific medications. PharmaBERT categorizes various drugs and fine-tunes the BERT structure to perceive lots of possible sentiments for specific medications. Consequently, PharmaBERT brings all its training and optimization capabilities together and through this, the system reaches a higher accuracy of up to 91% thus showcasing the potency of the model in capturing patient sentiments. While being a BERT spin-off, PharmaBERT utilizes its own set of experienced techniques to comprehend and sense the health-related text input given by the patient, doctor, or pharmacist. It uses transfer learning, that is, it learns from language representations to adapt quickly to the intricacies of drug reviewing. Through PharmaBERT, healthcare professionals may expand their diagnoses with insights from patient feedback to constitute more neutral decisions.

Performance Evaluation on E-Commerce Recommender System based on KNN, SVD, CoClustering and Ensemble Approaches

Wan-Er Kong — 2024-10-14

E-commerce recommender systems (RS) nowadays are essential for promoting products. These systems are expected to offer personalized recommendations for users based on the user preference. This can be achieved by employing cutting-edge technology such as artificial intelligence (AI) and machine learning (ML). Tailored recommendations for users can boost user experience in using the application and hence increase income as well as the reputation of a company. The purpose of this study is to investigate popular ML methods for e-commerce recommendation and study the potential of ensemble methods to combine the strengths of individual approaches. These recommendations are derived from a multitude of factors, including users' prior purchases, browsing history, demographic information, and others. To forecast the interests and preferences of users, several techniques are chosen to be investigated in this study, which include Singular Value Decomposition (SVD), k-Nearest Neighbor Baseline (KNN Baseline) and CoClustering. In addition, several evaluation metrics including the fraction of concordant pairs (FCP), mean absolute error (MAE), root mean square error (RMSE) and normalized discounted cumulative gain (NDCG) will be used to assess how well different techniques work. To provide a better understanding, the outcomes produced in this study will be incorporated into a graphical user interface (GUI).

Review on Automated Storage and Retrieval System for Warehouse

Alex Low Kai Jie — 2024-10-14

The swift expansion of e-commerce and supply chain operations has significantly enhanced the efficiency of warehouse management systems, establishing them as vital components in augmenting organizations' competitiveness. This paper delves into warehouse sorting systems to enhance the sorting process, reduce error rates, and simplify internal warehouse procedures. It aims to develop a scalable, adaptable, and efficient warehouse sorting system that can maximize sorting efficiency while effectively responding to changing market demands through the use of advanced automation technologies. The study provides an in-depth review of the literature that has explored the Automated Storage and Retrieval System (ASRS) within the context of warehouse operations. It offers a comprehensive introduction to the operational systems of warehouses, detailing each type of ASRS along with the technologies that can improve the efficiency and accuracy of these systems. Moreover, the paper thoroughly investigates and classifies the ASRS design decision problem and compares multiple types of ASRS. The analysis aims to delineate the distinctions among various ASRS configurations, assessing their scalability, adaptability, and their impact on operational efficacy in warehouse environments Through this comparative review, the paper emphasizes the potential enhancements in sorting processes that modern ASRS can provide, ensuring that warehouse operations can rapidly adapt to market changes and demands. The goal is to highlight best practices and technological innovations that can lead to more responsive and efficient warehouse management systems. This exploration contributes to a better understanding of how cutting-edge automation and adaptable system designs can significantly influence the efficiency of warehouse sorting processes.

Development of Virtual Reality-Based Left Brain System

Kok An Teo — 2024-10-14

This paper proposes a virtual reality based Left Brain System named BrainUp World to improve left brain thinking. The left-brain system was employed with mobile virtual reality technology, hand motion tracking and haptic feedback system. The implementation of these systems is to enhance the experience and sense of embodiment. BrainUp World includes virtual reality-based brain training games to improve a person’s attention level, reasoning skill, auditory cognition, arithmetic skill, sequencing skill and memory. The hand tracking system was utilized with an IR camera to capture the hand orientation and return the gesture data to the VR application. A low-cost and lightweight haptic glove was invented which provides the sense of touch using vibrations while interacting with VR contents. An experimental study was conducted to assess the efficacy of BrainUp World compared to traditional PC-based training approaches. Participants were randomly assigned to either the VR-based or PC-based training group and underwent 6 different games to test a person’s attention level, reasoning skill, auditory cognition, arithmetic skill, sequencing skill, and memory. The results revealed a statistically significant improvement in left brain function in the VR-based training group compared to the PC-based group, with a T-score growth of 4.73. Analysis using ANOVA confirmed the significance of this difference (p-Value = 0.04). Notably, the study also identified age-related differences in thinking fluency, highlighting the importance of personalized cognitive training interventions. In conclusion, BrainUp World demonstrates the potential of VR technology in promoting left brain development, as evidenced by empirical findings from the conducted study. By offering immersive and interactive cognitive training experiences, VR-based systems hold promise for enhancing various aspects of cognitive function associated with left hemisphere dominance. Further research in this area is encouraged to explore the full potential of VR-based interventions for cognitive enhancement.

Comparative Analysis of Linear and Nonlinear sEMG Methods for Detecting Muscle Fatigue During Dynamic Biceps Curls

Tang Ming — 2024-10-14

Muscle fatigue, a key concern in sports science, rehabilitation, and occupational health, influences performance, injury risk, and provides insights into muscle functionality and endurance. Surface electromyography (sEMG) has emerged as a vital tool for non-invasively tracking muscle electrical activity and gauging health. As its application for muscle fatigue assessment grows, identifying the most accurate analytical methods is essential. Current sEMG analyses employ both linear and nonlinear metrics to measure fatigue onset and progression, yet research is ongoing to determine which method is most effective in the context of dynamic contractions. The study was aimed to evaluate the efficacy of established linear and nonlinear methods in measuring muscle fatigue caused by dynamic contractions through surface electromyography (sEMG) signals. A group of twelve healthy individuals completed biceps curls at a consistent pace of one repetition per four seconds, which constituted 75% of their 10-repetition maximum. Concurrently, sEMG signals were captured from the biceps brachii muscle at 1000 Hz. To assess the sEMG signals during the initial, middle, and final sets of 10 repetitions, three linear metrics—mean frequency, median frequency, and spectral moment ratio (SMR)—along with two nonlinear approaches, namely sample entropy and detrended fluctuation analysis (DFA), were utilized. The study's outcomes indicated notable shifts in the SMR values and the two DFA-derived scaling exponents across the exercise sets. These results indicated that SMR, sample entropy, and DFA are effective in gauging muscle fatigue, with sample entropy and DFA demonstrating heightened sensitivity to the fatigue levels when compared to the linear metrics.

Pi Class – A Revolutionary Step Forward in Hybrid Class Management System

Qasim Hassan — 2024-10-14

The COVID-19 pandemic has caused widespread disruptions globally, resulting in a state of health emergency with numerous deaths reported and an overall implementation of quarantine and isolation. Strict lockdowns have been implemented to curb the spread of the virus, requiring social distancing and limiting physical interactions. These measures had far-reaching impacts on all aspects of life, including education. Education was one of the most affected sectors, with difficulty delivering quality education in schools, colleges, and universities. It was hard to provide quality education, a basic need of humanity. In this research paper, we propose an adequate solution to overcome the difficulties of maintaining educational quality during and after the COVID-19 pandemic and its multiple variants. The proposed solution is a hybrid model for an autonomous lecture recording system that facilitates students to attend physical classes and attend lectures virtually. The solution proposed is a cost-effective and convenient way for students to access lectures. The application involves hardware and software components that record and preserve lectures' audio and visual aspects. The system will allow lectures to be delivered directly to the students' devices. The major modules of the project include Python scripting, model training, UI/UX design, and app development.

Comparative Evaluation of Machine Learning Models for Mobile Phone Price Prediction: Assessing Accuracy, Robustness, and Generalization Performance

Saima Anwar Lashari — 2024-10-14

These days, mobile phones are the most commonly purchased goods. Thousands of new models with improved features, designs, and specifications are released yearly. An autonomous mobile price prediction system is required to assist customers in determining whether or not they can afford these devices. Many machine learning models exhibit varying performance degrees based on their architecture and learning properties. Ten widely used classifiers were assessed in this study: Logistic Regression (LR), Random Forest (RF), K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Extreme Gradient Boosting (XGBoost), Decision Tree (DT), Naïve Bayes (NB), Linear Discriminant Analysis (LDA), AdaBoost, and Light Gradient Boosting (LGB). The F1-score, recall, accuracy, and precision of these models were evaluated. According to the findings, the results indicated that LR, with its use of the Elastic Net parameter, outperformed the others with 96% accuracy, 97% precision, 94% recall, and 96% F1-score. Other models like XGBoost, LGB, and SVM also showed strong performance, whereas KNN had the poorest performance. The study highlights the importance of selecting the appropriate model for accurate mobile price prediction. Among all the machine learning used in this paper, the LR classifier outperforms the other state-of-the-art models because of the elastic Net parameter used for mobile phone price prediction.

Development of Robot Feature for Stunting Analysis Using Long-Short Term Memory (LSTM) Algorithm

Muhammad Rahadian Abdurrahman — 2024-10-14

Stunting prevalence in Indonesia persists as a significant challenge necessitating concerted efforts from all stakeholders. We developed robot for stunting analysis using deep learning algorithm. It aligns with the Sustainable Development Goal (SDG) agenda, specifically targeting SDG 3, which focuses on ensuring good health and well-being for all. Long Short-Term Memory (LSTM) is a type of Recurrent Neural Network (RNN) designed to overcome the vanishing gradient problem in traditional RNNs. In general, either LSTM can be used in analysis. This study aims to classify stunting based on age and height using LSTM. The LSTM model was trained with 50 epochs using datasets collected from the health office and robots. The evaluation results show training accuracy of 96.65% and training validation of 96.61%, with precision, recall and f1-score varying with relevance to the f1-score and support value. This research illustrates the potential for using data classification methods in stunting diagnosis. However, it is necessary to adjust parameters and increase the amount of training data to improve model performance. With good convergence at epoch 50, these results show the model's ability to classify stunting based on age and height. However, further validation and testing on larger datasets is needed to thoroughly test the reliability and generalization of the model. This research can contribute to the development of deep learning regarding robots as a means of testing stunting. This research provides initial evidence of the potential of stunting classification methods using robots. However, parameter adjustments and increasing the amount of training data need to be done to improve the overall model performance.

Investigation on Understanding the Numeracy Capacity of Intellectual Disabled Students using Enabling Technology Tools: Web Application, AR and UI/UX

Vishvjit Thakar — 2024-10-14

The population of individuals with intellectual disabilities (ID) is increasing, necessitating assistance with a wide range of daily activities. Acquiring and assessing numeracy and communication skills are critical for this demographic, requiring tools and techniques tailored to their specific needs. Effective educational tools must employ multi-modal and multi-sensory approaches to cater to diverse learning styles and incorporate assistive technological solutions. Despite the availability of numerous tools, there is a need to enhance their utility and effectiveness. This study aims to identify and refine the requirements for an innovative educational tool that employs Two-dimensional (2D) and Augmented Reality (AR) technologies. To achieve this, we conducted semi-structured interviews and surveys with teachers working with students with ID, gaining insights into the current solutions, advantages, and limitations. Additionally, we used physical props as design probes in a co-design methodology to better understand and elicit the true needs of individuals with ID. The findings from this research will inform the development of a 2D/AR tool designed to make learning mathematics more engaging and effective for individuals with ID, contributing to the advancement of inclusive education practices. Enabling Technology plays a significant role in the numeracy ability among people with ID. Generative AI and Explainable AI shall further improve learning ability in the years to come.

DDoS Attack Detection with Machine Learning

Wei-Wu Tay — 2024-10-14

Nowadays, Distributed Denial of Service (DDoS) attacks are a major issue in internet security. These attacks target servers or network infrastructure. Similar to an unanticipated traffic jam on highway (lagging/crash) that prevent normal traffic reach to destination. DDoS may prevent users to access any system services. Researchers and scientists have developed numerous methods and algorithms to improve the performance of DDoS detection. In this paper, a DDoS detection method utilizing machine learning is proposed. There are three type of supervised machine learning classification methods which are K-Nearest Neighbor, Multilayer Perceptron and Random Forest, are applied in the proposed work to assess the accuracy of the model in training and testing processes. RF classification provides robustness and interpretability, MLP offers deep learning capabilities for complex patterns, and K-NN delivers simplicity and adaptability for instance-based learning. Together, these methods can contribute to a comprehensive DDoS attack detection system using machine learning. There are two types of classification setups: binary and multi-class classification. Binary classification involves identifying traffic as either a DDoS attack or normal using the NSL-KDD dataset. Multi-class classification, on the other hand, distinguishes between various types of DDoS attacks (such as DoS, Probe, U2R, and Sybil) and normal traffic using the NSL-KDD dataset. Feature engineering is also involved in this experiment to convert the categorical features into numerical values for detecting DDoS attack. Our model's performance was effective compared to other machine learning methods. RF achieved the highest accuracy rates: 99.35% in binary classification and 97.71% in multi-class classification. K-NN followed with 99.15% in binary and 97.35% in multi-class classification, while MLP achieved 90.63% in binary and 84.33% in multi-class classification.

Editorial: Artificial Intelligence and Cybersecurity in Pervasive Computing

Ji-Jian Chin — 2024-10-14

Pervasive computing, or ubiquitous computing, is rapidly increasing in capacity and capabilities. With the Internet of Things (IoT) becoming an integral part of daily life and the growing availability of edge computing resources, automation guided by data is advancing applications in healthcare, manufacturing, automotive, and other areas. It's natural that pervasive computing will intersect with artificial intelligence (AI) and cybersecurity. AI can improve detection, prediction, and anticipative responses to human needs, while cybersecurity addresses topics like misuse prevention, ethics, policies, and governance. This issue features seven articles on these intersections, including four AI articles exploring natural language processing and computer vision, and three cybersecurity articles covering cryptography, medical devices, and maritime security.

Sibling Discrimination Using Linear Fusion on Deep Learning Face Recognition Models

Rita Goel — 2024-10-14

Facial recognition technology has revolutionised human identification, providing a non-invasive alternative to traditional biometric methods like signatures and voice recognition. The integration of deep learning has significantly enhanced the accuracy and adaptability of these systems, now widely used in criminal identification, access control, and security. Initial research focused on recognising full-frontal facial features, but recent advancements have tackled the challenge of identifying partially visible faces, a scenario that often reduces recognition accuracy. This study aims to identify siblings based on facial features, particularly in cases where only partial features like eyes, nose, or mouth are visible. Utilising advanced deep learning models such as VGG19, VGG16, VGGFace, and FaceNet, the research introduces a framework to differentiate between sibling images effectively. To boost discrimination accuracy, the framework employs a linear fusion technique that merges insights from all the models used. The methodology involves preprocessing image pairs, extracting embeddings with pre-trained models, and integrating information through linear fusion. Evaluation metrics, including confusion matrix analysis, assess the framework's robustness and precision. Custom datasets of cropped sibling facial areas form the experimental basis, testing the models under various conditions like different facial poses and cropped regions. Model selection emphasises accuracy and extensive training on large datasets to ensure reliable performance in distinguishing subtle facial differences. Experimental results show that combining multiple models' outputs using linear fusion improves the accuracy and realism of sibling discrimination based on facial features. Findings indicate a minimum accuracy of 96% across different facial regions. Although this is slightly lower than the accuracy achieved by a single model like VGG16 with full-frontal poses, the fusion approach provides a more realistic outcome by incorporating insights from all four models. This underscores the potential of advanced deep learning techniques in enhancing facial recognition systems for practical applications.

HybridEval: An Improved Novel Hybrid Metric for Evaluation of Text Summarization

Raheem Sarwar — 2024-10-14

The present work re-evaluates the evaluation method for text summarization tasks. Two state-of-the-art assessment measures e.g., Recall-Oriented Understudy for Gisting Evaluation (ROUGE) and Bilingual Evaluation Understudy (BLEU) are discussed along with their limitations before presenting a novel evaluation metric. The evaluation scores are significantly different because of the length and vocabulary of the sentences, this suggests that the primary restriction is its inability to preserve the semantics and meaning of the sentences and consistent weight distribution over the whole sentence. To address this, the present work organizes the phrases into six different groups and to evaluate “text summarization” problems, a new hybrid approach (HybridEval) is proposed. Our approach uses a weighted sum of cosine scores from InferSent’s SentEval algorithms combined with original scores, achieving high accuracy. HybridEval outperforms existing state-of-the-art models by 10-15% in evaluation scores.

Intelligent Abstractive Summarization of Scholarly Publications with Transfer Learning

Farooq Zaman — 2024-10-14

Intelligent abstractive text summarization of scholarly publications refers to machine-generated summaries that capture the essential ideas of an article while maintaining semantic coherence and grammatical accuracy. As information continues to grow at an overwhelming rate, text summarization has emerged as a critical area of research. In the past, summarization of scientific publications predominantly relied on extractive methods. These approaches involve selecting key sentences or phrases directly from the original document to create a summary or generate a suitable title. Although extractive methods preserve the original wording, they often lack the ability to produce a coherent, concise, and fluent summary, especially when dealing with complex or lengthy texts. In contrast, abstractive summarization represents a more sophisticated approach. Rather than extracting content from the source, abstractive models generate summaries using new language, often incorporating words and phrases not found in the original text. This allows for more natural, human-like summaries that better capture the key ideas in a fluid and cohesive manner. This study introduces two advanced models for generating titles from the abstracts of scientific articles. The first model employs a Gated Recurrent Unit (GRU) encoder coupled with a greedy-search decoder, while the second utilizes a Transformer model, known for its capacity to handle long-range dependencies in text. The findings demonstrate that both models outperform the baseline Long Short-Term Memory (LSTM) model in terms of efficiency and fluency. Specifically, the GRU model achieved a ROUGE-1 score of 0.2336, and the Transformer model scored 0.2881, significantly higher than the baseline LSTM model, which reported a ROUGE-1 score of 0.1033. These results underscore the potential of abstractive models to enhance the quality and accuracy of summarization in academic and scholarly contexts, offering more intuitive and meaningful summaries.

Cyber-Securing Medical Devices Using Machine Learning: A Case Study of Pacemaker

Suliat Toyosi Jimoh — 2024-10-14

This study aims to enhance the cybersecurity framework of pacemaker devices by identifying vulnerabilities and recommending effective strategies. The objectives are to pinpoint cybersecurity weaknesses, utilize machine learning to predict security breaches, and propose countermeasures based on analytical trends. The literature review highlights the transformation of pacemaker technology from basic, fixed-rate devices to sophisticated systems with wireless capabilities, which, while improving patient care, also introduce significant cybersecurity risks. These risks include unauthorized entry, data breaches, and life-threatening device malfunctions. The methodology in this study utilizes a quantitative research approach using the WUSTL-EHMS-2020 dataset, which includes network traffic features, patients' biometric features, and attack label. The step-by-step method of machine learning prediction includes data collection, data preprocessing, feature engineering, and models’ training using Support Vector Machines (SVM) and Gradient Boosting Machines (GBM). The implementation results used evaluation metrics like accuracy, precision, recall, and F1 score to show that GBM model outperformed the SVM model. The GBM model achieved higher accuracy of 95.1% compared to 92.5% for SVM, greater precision of 99.6% compared to 96.7% for SVM, better recall of 94.9% compared to 42.7% for SVM, and a higher F1 score of 76.3% compared to 59.0% for SVM, making GBM model more effective in predicting cybersecurity threats. This study concludes that GBM is an effective machine learning model for enhancing pacemaker cybersecurity by analyzing network traffic and biometric data patterns. Future recommendations for improving the pacemaker cybersecurity include implementing GBM model for threat predictions, integration with existing security measures, and regular model updates and retraining.

Towards Analysable Chaos-based Cryptosystems: Constructing Difference Distribution Tables for Chaotic Maps

Je Sen Teh — 2024-10-14

Chaos-based cryptography has yet to achieve practical, real-world applications despite extensive research. A major challenge is the difficulty in analysing the security of these cryptosystems, which often appear ad hoc in design. Unlike conventional cryptography, evaluating the security margins of chaos-based encryption against attacks such as differential cryptanalysis is complex. This paper introduces a straightforward approach of using chaotic maps in cryptographic algorithms in a way that facilitates cryptanalysis. We demonstrate how a chaos-based substitution function can be constructed using fixed-point representation, enabling the application of conventional cryptanalysis tools such as the difference distribution table. As a proof-of-concept, we apply our method to the logistic map, showing that differential properties vary based on the initial state and number of iterations. Our findings demonstrate the feasibility of designing analysable chaos-based cryptographic components with well-understood security margins.

Conditional Deployable Biometrics: Matching Periocular and Face in Various Settings

Jihyeon Kim — 2024-10-14

In this paper, we introduce the concept of Conditional Deployable Biometrics (CDB), designed to deliver consistent performance across various biometric matching scenarios, including intra-modal, multimodal, and cross-modal applications. The CDB framework provides a versatile and deployable biometric authentication system that ensures reliable matching regardless of the biometric modality being used. To realize this framework, we have developed CDB-Net, a specialized deep neural network tailored for handling both periocular and face biometric modalities. CDB-Net is engineered to handle the unique challenges associated with these different modalities while maintaining high accuracy and robustness. Our extensive experimentation with CDB-Net across five diverse and challenging in-the-wild datasets illustrates its effectiveness in adhering to the CDB paradigm. These datasets encompass a wide range of real-world conditions, further validating the model’s capability to manage variations and complexities inherent in biometric data. The results confirm that CDB-Net not only meets but exceeds expectations in terms of performance, demonstrating its potential for practical deployment in various biometric authentication scenarios.

In Search of Suitable Methods for Cost-Benefit Analysis of Cyber Risk Mitigation in Offshore Wind: A Survey

Yvonne Hwei-Syn Kam — 2024-10-14

In recent years, notable incidents have highlighted the vulnerability of wind energy infrastructure, making cybersecurity crucial for the offshore wind industry. However, justifying the costs of cybersecurity measures is essential. A cost benefit analysis (CBA) is commonly utilised to support decision-making for risk mitigation. With a cost benefit analysis, risk mitigation strategies that strike an optimal balance between the costs of mitigation measures and the resulting risk reduction can be identified. This survey of literature was carried out to identify the existing proposed solutions for cost benefit analysis on cyber risk mitigation measures for offshore wind cyber physical systems. After narrowing the area scope, a systematic search across Scopus and Web of Science, yielded 18 articles, of which six met the selection criteria. It was found that the there was a lack of cost benefit analysis of cybersecurity solutions for, or set in, the area of offshore wind directly. From the analysis of the surveyed works, suggestions on future directions were given. The existing literature found lacks detailed cost modelling for offshore wind, beyond general breakdowns encompassing capital, maintenance, and labour/installation expenses, risk and scenario loss. Some of the literature used contextual factors such as compatibility and effectiveness of mitigation measures, effects on OT performance, geographical location, geopolitical context, and installed rated power which could be adapted to suit offshore wind. Since offshore operations contribute significantly to costs, cost modelling and consideration of other relevant factors pertaining to this area would be beneficial if explored. As an emerging area, in the future we expect this research to be a basis and a methodology that can be expanded with a larger data set from other publications in the field. Thus, it represents an opportunity to advance knowledge in offshore wind cyber-physical systems.