Recent advances in extended reality (XR) have enabled seamless access to immersive services. However, user authentication in these environments typically relies on conventional methods, such as PIN entry, which remains vulnerable to unauthorized use. This paper investigates gaze behavior as an implicit second authentication factor in XR. Using a Meta Quest Pro headset, participants performed legitimate and impostor login attempts while their gaze data were recorded. Temporal, spatial, and oculomotor metrics revealed distinctive and reproducible gaze dynamics between user roles. Tree-based machine learning models, particularly XGBoost, reliably distinguished legitimate from impostor sessions under user-independent validation (AUC =.84). Calibrating model thresholds further enabled adaptive balancing between security and usability. These findings demonstrate that gaze dynamics can unobtrusively enhance PIN authentication, introducing an adaptive layer that aligns authentication sensitivity with situational risk and user needs in immersive environments.

Over the past few years, the transition from centralized to distributed industrial control systems (ICS) has introduced new challenges related to coordination, communication reliability, and cybersecurity. These challenges include conditions such as deadlocks and livelocks, which adversaries can exploit to compromise ICS safety and availability. To ensure secure and resilient operations in distributed ICS, run-time monitoring must go beyond detection to include responsive recovery. In this paper, we extend the ASM2S framework, a model-based inline security monitoring approach, by integrating recovery capabilities directly into the monitoring loop. Our approach uses formal specifications to allow system behavior, threat conditions, and recovery actions to be explicitly defined and evaluated at run-time. We demonstrate the approach using a water distribution system use case. Our work enhances the run-time assurance of distributed ICS by enabling automatic detection and recovery from security violations, offering a robust foundation for self-healing critical infrastructure.

Industrial Control Systems (ICS) increasingly face cybersecurity threats due to their distributed architecture and critical role in infrastructure operations. We adopt inline security monitoring as a practical run-time verification strategy to address these risks. However, authoring formal specifications remains time-consuming and error-prone, requiring deep domain expertise. In this paper, we explore how large language models (LLMs) can support the synthesis of inline security monitors by generating Java Modeling Language (JML) specifications for distributed ICS applications. We use a water distribution system (WDS) as our testbed and FormalBench to generate prompts to guide the GPT-4o model in producing JML annotations. We then evaluate these outputs using the FormalBench framework. Our findings show that LLMs capture key security properties and generate context-aware assertions with minimal intervention, taking a first step toward automating the specification process and enhancing the security and resilience of distributed ICS environments.

The increasing complexity and decentralization of Industrial Control Systems (ICS) have expanded the attack surface for cyber-security threats, particularly in critical infrastructure domains. Inline monitoring using formal annotations like the Java Modeling Language (JML) offers a lightweight yet precise method to detect behavioral anomalies. However, the manual creation of such specifications is resource-intensive and requires domain expertise. This paper explores generative artificial intelligence (AI), specifically large language models (LLMs), to automate the synthesis of inline formal security monitors. We benchmark three state-of-the-art LLMs (GPT-4o, DeepSeek-V3, and Gemini 2.5 Flash) on their ability to generate JML annotations for ICS software drawn from the ASM2S water distribution system. Our evaluation across five dimensions (syntax, semantics, property coverage, alarm semantics, and effort savings) reveals distinct trade-offs. GPT-4o demonstrates strong syntactic and structural alignment with ASM2S, while DeepSeek-V3 offers richer behavioral modeling. Gemini 2.5 Flash showcases conceptual depth but introduces non-verifiable constructs. These findings demonstrate the potential of LLMs as co-pilots in secure-by-design ICS development and underscore the need for syntax-aware fine-tuning and interactive verification workflows.

In the decentralized Industrial Control Systems (ICS) era, water distribution systems (WDS) are critical in ensuring water safe and reliable delivery. However, their growing complexity, connectivity, and distributed nature expose them to cybersecurity risks. Renowned WDS software, like EPANET, lacks features to address such risks, which, however, can be addressed by complementary solutions, like ASM2S. In this paper, we compare the capabilities offered by these two tools and make a first step towards exploring their combination, aiming to equip WDS tools with enhanced hydraulic, water quality, and cybersecurity modeling and monitoring characteristics.

In the decentralized Industrial Control Systems (ICS) domain, water distribution systems (WDS) are critical in ensuring water safe and reliable delivery. However, their growing complexity, connectivity, and distributed nature expose them to cybersecurity risks. Run-time inline monitoring can address such risks. This paper focuses on implementing run-time inline security monitoring for deadlocks in WDS, providing examples and simulation results. The results indicate the run-time detection of deadlocks, enhancing WDS’s overall reliability and efficiency.

Over the past few years, there has been a noticeable transition from centralized Industrial Control Systems (ICS) to distributed systems. However, the challenges of distributed systems (e.g., communication delays and packet loss) can give rise to undesired situations like deadlocks, which malicious actors may target. To address such conditions, implementing run-time security monitoring can ensure these systems’ reliable and secure operation. In this paper, we introduce a novel approach for run-time security monitoring for distributed ICS, extending previous works that focus on autonomous and centralized systems. Our approach makes it possible to specify physical and cyber resources and their changing limitations within a distributed environment. By doing so, our approach offers a valuable contribution to ICS security by tackling limitations introduced by distributed ICS. Furthermore, it provides an efficient mechanism for monitoring the security of these systems in real time.

AVI-CH is the 14th workshop in the series of PATCH workshops, since 2007 and the 4th in a row at AVI. It is the meeting place for researchers and practitioners focusing on the application of advanced information and communication technology (ICT) in cultural heritage with a specific focus on user interfaces, visualization and interaction. This year, eight papers were submitted by researchers from Greece, Italy and Israel. All were accepted.

Graphical user authentication (GUA) is a common alternative to text-based user authentication, where people are required to draw graphical passwords on background images. Such schemes are theoretically considered remarkably secure because they offer a large password space. However, people tend to create their passwords on salient image areas introducing high password predictability. Aiming to help people use the password space more effectively, we propose a gameful password creation process. In this paper, we present GamePass, a gamified mechanism that integrates the GUA password creation process. We provide the first evidence that it is possible to nudge people towards better password choices by gamifying the process. GamePass randomly guides participants’ attention to areas other than the salient areas of authentication images, makes the password creation process more fun, and people are more engaged. Gamifying the password creation process enables users to interact better and make less predictable graphical password choices instead of being forced to use a strict password policy.

In multiplayer collaborative games, players need to coordinate their actions and synchronize their efforts effectively to succeed as a team; thus, individual differences can impact teamwork and gameplay. This article investigates the effects of cognitive styles on teams engaged in collaborative gaming activities. Fifty-four individuals took part in a mixed-methods user study; they were classified as field-dependent (FD) or independent (FI) based on a field-dependent–independent (FD-I) cognitive-style-elicitation instrument. Three groups of teams were formed, based on the cognitive style of each team member: FD-FD, FD-FI, and FI-FI. We examined collaborative gameplay in terms of team performance, cognitive load, communication, and player experience. The analysis revealed that FD-I cognitive style affected the performance and mental load of teams. We expect the findings to provide useful insights on understanding how cognitive styles influence collaborative gameplay.

AVI2CH is a meeting place for researchers and practitioners focusing on the application of advanced information and communication technology in cultural heritage (CH) with a specific focus on user interfaces, visualization and interaction. It builds on a series of PATCH workshops, since 2007 including three at AVI and also a series of European workshops on cultural informatics. Eleven papers range from novel interfaces in museums to wider community engagement; all share a common mission to ensure that the latest digital technology helps preserve the past in ways that enrich the lives of current and future generations

For the past 20 years, researchers have investigated the use of eye tracking in security applications. We present a holistic view on gaze-based security applications. In particular, we canvassed the literature and classify the utility of gaze in security applications into a) authentication, b) privacy protection, and c) gaze monitoring during security critical tasks. This allows us to chart several research directions, most importantly 1) conducting field studies of implicit and explicit gaze-based authentication due to recent advances in eye tracking, 2) research on gaze-based privacy protection and gaze monitoring in security critical tasks which are under-investigated yet very promising areas, and 3) understanding the privacy implications of pervasive eye tracking. We discuss the most promising opportunities and most pressing challenges of eye tracking for security that will shape research in gaze-based security applications for the next decade.

ACM PATCH 2020, organized in conjunction with the 28th International Conference on User Modeling, Adaptation and Personalization, is the latest event of the PATCH series, started in 2007 and held within the UMAP and IUI Conference series. We summarize the main ideas addressed in the papers accepted for publication in the workshop proceedings and for presentation at the event.

Cultural Heritage (CH) is a challenging domain of application for novel Information and Communication Technologies (ICT), where visualization plays a major role in enhancing visitors' experience, either onsite or online. Technology-supported natural human-computer interaction is a key factor in enabling access to CH assets. Advances in ICT ease visitors to access collections online and better experience CH onsite. The range of visualization devices - from tiny smart watch screens and wall-size large situated public displays to the latest generation of immersive head-mounted displays - together with the increasing availability of real-time 3D rendering technologies for online and mobile devices and, recently, Internet of Things (IoT) approaches, require exploring how they can be applied successfully in CH. Following the successful workshop at AVI 2016 and the large numbers of recent events and projects focusing on CH and, considering that 2018 has been declared the European Year of Cultural Heritage, the goal of the workshop is to bring together researchers and practitioners interested in presenting and discussing the potential use of state-of-the-art advanced visual interfaces in enhancing our daily CH experience.

Nowadays, technological advances introduce new visualization and user interaction possibilities. Focusing on the user authentication domain, graphical passwords are considered a better fit for interaction environments which lack a physical keyboard. Nonetheless, the current graphical user authentication schemes are deployed in conventional layouts, which introduce security vulnerabilities associated with the strength of the user selected passwords. Aiming to investigate the effectiveness of advanced visualization layouts in selecting stronger passwords, this paper reports a between-subject study, comparing two different design layouts a two-dimensional and a three dimensional. Results provide evidence that advanced visualization techniques provide a more suitable framework for deploying graphical user authentication schemes and underpin the need for considering such techniques for providing assistive and/or adaptive mechanisms to users aiming to assist them to create stronger graphical passwords.

Visual attention, search, processing and comprehension are important cognitive tasks during a graphical password composition activity. Aiming to shed light on whether individual differences on visual behavior affect the strength of the created passwords, we conducted an eye-tracking study (N=36), and adopted an accredited cognitive style theory to interpret the results. The analysis revealed that users with different cognitive styles followed different patterns of visual behavior which affected the strength of the created passwords. Motivated, by the results of the first study, we introduced adaptive characteristics to the user authentication mechanism, aiming to assist specific cognitive style user groups to create more secure passwords, and conducted a second study with a new sample (N=40) to test the adaptive characteristics. Results strengthen our assumptions that adaptive mechanisms based on users' differences in cognitive and visual behavior uncover a new perspective for improving the password's strength within graphical user authentication realms.

In this paper, we introduce a two-step method for estimating the strength of user-created graphical passwords based on the eye-gaze behaviour during password composition. First, the individuals' gaze patterns, represented by the unique fixations on each area of interest (AOI) and the total fixation duration per AOI, are calculated. Second, the gaze-based entropy of the individual is calculated. To investigate whether the proposed metric is a credible predictor of the password strength, we conducted two feasibility studies. Results revealed a strong positive correlation between the strength of the created passwords and the gaze-based entropy. Hence, we argue that the proposed gaze-based metric allows for unobtrusive prediction of the strength of the password a user is going to create and enables intervention to the password composition for helping users create stronger passwords.

Evidence suggests that individual cognitive differences affect users' memorability, visual behavior, and graphical passwords' security. Such knowledge denotes the added value of personalizing graphical password schemes towards the unique cognitive characteristics of the users. However, real-time and accurate cognition-based predictive user models are necessary to reach such a breakthrough. In this paper, we present the results of such an attempt, where an in-lab eye-tracking study was conducted with 36 participants who completed a recall-based graphical password composition task. We adopted a credible cognitive style theory, and investigated a variety of eye-tracking metrics to predict participants' cognitive styles. Results' analysis reveals that inferring individual cognitive differences in real-time during graphical password composition is feasible within a few seconds and that specific eye-tracking metrics correlate stronger with certain cognitive style groups. The findings further support the vision of incorporating real-time adaptive mechanisms in graphical password schemes for the benefit of service providers and end-users.

Recent research provides evidence that individual differences in human cognitive styles affect user performance and experience in diverse application domains. However, state-of-the-art elicitation methods of cognitive styles require researchers to apply explicit, in-lab, and time-consuming "paper-and-pencil" techniques, rendering real-time integration of cognitive styles? elicitation impractical in interactive system design. Aiming to elaborate an implicit elicitation method of cognitive styles, this paper reports two feasibility studies based on an eye-tracking multifactorial model. In both studies, participants performed visual activities of varying characteristics, and the eye-tracking analysis revealed quantitative differences on visual behavior among individuals with different cognitive styles. Based on these differences, a series of classification experiments were conducted, and the results revealed that gaze-based implicit elicitation of cognitive styles in real-time is feasible, which could be used by interactive systems to adapt to the users' cognitive needs and preferences, to better assist them, and improve their performance and experience.