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Easy to Check Algebraic Characterizations of Dynamical Properties for Linear CA and Additive CA over a Finite Abelian Group
No full textPart 1: Invited PapersInternational audienceWe focus on how the dynamical properties of any Linear CA over (Z/mZ)n are hidden inside the characteristic polynomial of its defining matrix, namely, a polynomial of degree n in the indeterminate t and with Laurent polynomials over Z/mZ as coefficients. In particular, as far as Linear CA over (Z/mZ)n are concerned, we review the mostly recent algebraic decidable characterizations of the following properties: injectivity, surjectivity, sensitivity to the initial conditions, equicontinuity, topological transitivity, and positive expansivity. These characterizations are easy to check, i.e., related decision algorithms can be designed is such a way that exponential terms in their computational complexity are avoided as much as possible. In particular, gcd operations are involved, while the prime factor decomposition of m is bypassed. Finally, we recall how such characterizations regarding Linear CA over (Z/mZ)n can be exploited to decide the above mentioned dynamical properties for the whole class of Additive CA over a finite abelian group
Formal Techniques for Distributed Objects, Components, and Systems: 44th IFIP WG 6.1 International Conference, FORTE 2024, Held as Part of the 19th International Federated Conference on Distributed Computing Techniques, DisCoTec 2024
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A Framework for Integrating Gamification in Information Security Awareness Programmes for Higher Education Students
No full textPart 1: Cybersecurity Training and EducationInternational audienceIn the context of higher education, students are often identified as targets for security attacks due to their seeming lack of security awareness. While institutions employ technological solutions to safeguard students when engaging with official systems, this does not extend to students’ own devices and cannot influence how, and if, they employ good security practices. A possible mitigation strategy is security awareness training. Literature, however, is divided about the effectiveness of such programmes due to the complacency and irreverence that many people have toward security, possibly due to occurrences of security fatigue and risk homeostasis. Gamification has been shown to be an effective method of enhancing traditional training in such a way as to enhance engagement and promote retention of concepts. There is, however, a dearth of research that investigates the application of gamification in information security awareness training for higher education students. This research, therefore, contributes a framework for the gamification of security awareness training in this context. The framework was developed through identifying gamification mechanics from literature. Possible implementations of these mechanics were presented to higher education students by means of an online self-reporting questionnaire to measure their perception in these mechanics when applied to security training. Feedback from 196 students were incorporated into the development of the resulting framework. Furthermore, the framework was influenced by the Knowledge, Attitude, and Behaviour-model that often underpins research into the human aspects of information security. The resulting framework can contribute to the practical aspects of incorporating gamification in information security awareness training for higher education students
GPT-Enabled Cybersecurity Training: A Tailored Approach for Effective Awareness
No full textPart 1: Cybersecurity Training and EducationInternational audienceThis study explores the limitations of traditional Cybersecurity Awareness and Training (CSAT) programs and proposes an innovative solution using Generative Pre-Trained Transformers (GPT) to address these shortcomings. Traditional approaches lack personalization and adaptability to individual learning styles. To overcome these challenges, the study integrates GPT models to deliver highly tailored and dynamic cybersecurity learning experiences. Leveraging natural language processing capabilities, the proposed approach personalizes training modules based on individual trainee profiles, helping to ensure engagement and effectiveness. An experiment using a GPT model to provide a real-time and adaptive CSAT experience through generating customized training content. The findings have demonstrated a significant improvement over traditional programs, addressing issues of engagement, dynamicity, and relevance. GPT-powered CSAT programs offer a scalable and effective solution to enhance cybersecurity awareness, providing personalized training content that better prepares individuals to mitigate cybersecurity risks in their specific roles within the organization
Using Breach and Attack Demonstrations to Explain Spear Phishing Attacks to Young Adults
No full textPart 1: Cybersecurity Training and EducationInternational audiencePhishing attacks continue to thrive despite continued efforts to inform citizens about their dangers and how to enact protective behaviours. Demonstrations have been shown to help enhance student learning in various disciplines, yet these have not been explored in a security context with lay individuals. We designed and delivered a Breach and Attack Demonstration (BAD) of spear phishing to 10 lay younger adults (18–24) to explore their perceptions of this method as an awareness tool and to capture any long-lasting impressions. Based on semi-structured interviews and survey responses 6 months after the demonstrations, we found that participants were surprised at how simple spear phishing attacks were to enact and this impression persevered 6 months following the BAD. We discuss the benefits and drawbacks of using BADs as an interactive awareness tool, concluding with recommendations for the design of such demonstrations for lay individuals
Integrating Industry 5.0 Competencies: A Learning Factory Based Framework
No full textPart 4: Evolving Workforce Skills and Competencies for Industry 5.0International audienceIndustries across the world are witnessing profound technological paradigm changes, necessitating a new set of engineering skills and capabilities for the future workforce. This paper investigates the needed engineering competencies required to bridge the skill gap related to Industry 5.0. Our work analyses current educational frameworks on the topic and proposes a new concept based on the exploitation of learning factories. The paper showcases the conception, execution, and evaluation of a summer school program dedicated to such an objective. It discusses the structure, methodologies, and outcomes of this course, demonstrating the effectiveness of the proposed didactic framework. The findings offer valuable insights for educators and industry professionals alike in preparing the future workforce for the challenges and opportunities of Industry 5.0
Diabetic Foot Ulcer Classification Using Deep Learning Approach
No full textPart 1: SDG 3 Good Health and Well-BeingInternational audiencePodiatrist diagnosis and lesion localization are used as current testing approaches for Diabetic Foot ulcers (DFU). Current systems for automation concentrate on either categorization or division. One of the most common metabolic conditions, Diabetes (also called diabetes mellitus), is brought on by the pancreas incapacity to generate sufficient anti hyper-glycemic hormone to control glycemia levels. It may cause “Diabetic Peripheral Neuropathy,” a collection of nerve diseases that can develop if the blood glucose level is left uncontrolled and unmanaged. Blood glucose levels may be efficiently maintained by recognising diabetes initially through constant surveillance and screenings. Using image processing methods such as data stretching with Deep Learning, image reduction and improvement, image division, and feature extraction, this paper offers a thorough approach for analysing thermal scans of the diabetic foot. By using these techniques, doctors may assess and track a patient's condition with fewer examinations. In this work, EfficientNetB3 architecture is compared with other Deep Learning models for accuracy in DFU identification. To create a powerful deep learning model, we gathered an extensive collection of 1,775 DFU photos. The basis of belief for this data gathering was created by two medical experts using annotator software to define the DFU region of interest. It took 48 ms to figure out a single image and 57.2 MB to validate the model. EfficientnetB3 got an average mean accuracy of 93%. This work demonstrates the effectiveness of Deep Learning for DFU localization in real-time, which could have improved with the use of a larger data set
Mental Stress Assessment in Working Environment for an Individual Using Wearable Sensor of EEG and Pulse Signal Measured with Help of Deep Learning Algorithm
No full textPart 1: SDG 3 Good Health and Well-BeingInternational audienceThanks to artificial intelligence (AI), machines will eventually have the same emotional impact as humans. Deep emotions, not just words, are used by this “affective computing” to engage with humans. Bypassing societal masks, sensors like the EEG and GSR are able to sense our genuine feelings. However, creating robots that are actually sensitive to our emotions is challenging. To build genuinely emotionally intelligent machines, we need to improve the methods we use to choose, arrange, and evaluate these signals.This paper propose a method for assessing mental stress in the workplace in real-time using deep learning algorithms in conjunction with wearing EEG and GSR. The major goal is to make computers smarter emotionally so that they can change the way people interact with computers (HCI). Our proposed system can discreetly track a worker’s emotional and psychological well-being as they go about their daily tasks. By combining electroencephalogram (EEG) and pulse signal analysis, our system is able to record not only the mental but also the physiological reactions to mental stress
Useful but for Someone Else - An Explorative Study on Cybersecurity Training Acceptance
No full textPart 1: Awareness and EducationInternational audienceInsecure user behavior is the most common cause of cybersecurity incidents. Insecure behavior includes failing to detect phishing, insecure password management, and more. The problem has been known for decades, and state-of-the-art mitigation methods include security education, training, and awareness (SETA). A common problem with SETA is, however, that users do not seem to adopt it to a high enough extent. When users are not adopting SETA, its intended benefit is lost. Previous research argues for personalized SETA and suggests that different user groups have different SETA needs and preferences. The characteristics of those groups are, however, unknown. To that end, this research draws on an existing dataset to identify how different populations perceive different SETA methods. A quantitative analysis shows that users in different demographic groups have different SETA preferences, with age being the most impactful demographic. A qualitative analysis reveals further factors that impact user adoption of SETA, with cost and ease of use being important factors for further research
Assessing Cyber Security Support for Small and Medium-Sized Enterprises
No full textPart 1: Management and RiskInternational audienceSmall and Medium-Sized Enterprises (SMEs) share many of the same cyber security needs and challenges as larger organisations, but often have significantly less knowledge and capability to deal with them. One of the fundamental issues can be where to find information in the first instance, to explain the nature of cyber threats and the subsequent actions that SMEs should be taking. In many cases, the natural route for interested or concerned SMEs is to seek and refer to related guidance that can be found online. However, this in itself can be a challenge considering the volume and variety of sources that can be located as a consequence. This paper investigates and analyses the situation, based upon a sample of over 30 UK-based guidance sources, and an assessment of their coverage, completeness and clarity. The results reveal that there is indeed a significant diversity in the materials that SMEs may be presented with, and this in turn could lead to inconsistent and potentially ill-informed decision-making. Additionally, in many cases, there will be a limit to how far the online support will take them, with the potential that questions remain unresolved, and SMEs could be more confused as a result of their efforts