Patching zero-day vulnerabilities: an empirical analysis

The rapid expansion of Internet of Things (IoT) devices has reshaped various sectors by improving connectivity, efficiency, and convenience. Central to this transformation are LoRa (Long Range) and LoRaWAN (Long Range Wide Area Network) technologies, which provide reliable, low-power, long-range communication critical for IoT applications. As these systems evolve, examining security vulnerabilities and forensic challenges becomes increasingly essential. This paper explores digital forensics within IoT environments, focusing on the methodologies and tools required to secure and maintain the integrity of IoT deployments. By analyzing artifacts and log data from SenseCAP devices, the study offers insights into device operations and user interactions. Additionally, frequency analysis conducted via Software-Defined Radio (SDR) confirmed LoRa communication within expected frequency bands. The findings highlight the importance of robust forensic investigations to protect IoT ecosystems from cyber threats. Through an extensive literature review and empirical analysis, this paper contributes to advancing IoT device forensics, proposing strategies to address emerging challenges and enhance the resilience of IoT infrastructures in an increasingly interconnected world.

This research employs an advanced Graph Attention Network (GAT) model to assess user influence in e-commerce social networks. As social networks become increasingly embedded in online shopping environments, it is essential for businesses to comprehend how user relationships, behaviors, and interactions shape purchasing behavior in order to refine marketing tactics. Conventional influence analysis techniques are inadequate for handling the intricate and varied nature of these networks, which include diverse user engagements and transaction records. This research proposes a modified GAT model tailored to e-commerce environments, which incorporates advanced attention mechanisms to better capture the significance of various user interactions, product categories, and demographic attributes. The study first constructs a graph-based representation of the e-commerce social network by integrating transactional, demographic, and social connection data. It then applies the enhanced GAT model to quantify user influence, emphasizing the importance of contextual interactions and varying user behaviors. Experimental results, validated using real-world e-commerce data, show that the modified GAT model outperforms traditional influence metrics in terms of accuracy, offering businesses actionable insights into identifying key influencers within their networks. Key contributions of the research include the development of a comprehensive graph-based framework for influence analysis, the introduction of domain-specific modifications to GAT, and the empirical validation of the proposed model’s effectiveness. These developments allow for more precise detection of key influencers, supporting e-commerce platforms in enhancing marketing efficiency, strengthening customer interaction, and boosting sales outcomes. By introducing a new method of influence analysis specifically designed for e-commerce social networks, this study addresses important gaps in existing research and provides a foundation for future investigations in the field.

Purpose The continuous influx of software vulnerabilities poses a significant challenge to organizations, necessitating effective resource allocation for threat mitigation. A key factor in this process is assessing the severity of vulnerabilities to prioritize which issues require immediate attention. This paper aims to automate the prediction of common vulnerability scoring system (CVSS) metrics from textual descriptions of vulnerabilities, reducing the reliance on manual expert analysis. Design/methodology/approach This study applies machine learning and natural language processing techniques, particularly convolutional neural networks (CNNs), to predict CVSS base metrics such as attack vectors, attack complexity and required privileges. The CNN models are trained on vulnerability descriptions and evaluated for their accuracy in predicting these metrics, which are then used to compute overall severity base scores. Findings The CNN models demonstrated high accuracy in predicting CVSS base metrics from textual descriptions. The predicted severity base scores closely align with those provided by human experts, showing the model’s potential to streamline the vulnerability assessment process. Practical implications Automating CVSS metric prediction could significantly reduce the time and effort required for vulnerability severity assessment. This would enable security teams to quickly identify and prioritize critical vulnerabilities, improving response times in cybersecurity management. Originality/value This research provides an innovative approach to vulnerability management by automating CVSS metric prediction, reducing the need for manual expert analysis and therefore accelerating security assessments.

Summary form only given, as follows. The tutorial will present an integrated set of techniques for performing competent and comprehensive Business Process Modeling. Concepts have been drawn from a number of sources, including Stakeholder modeling Enterprise modeling based upon value chains and objects developed at the GMD in Germany Event models from James Odell and the corresponding UML activity diagrams Simulation techniques for multi-server queuing network analysis. These have been synthesised and integrated in a manner which is comprehensive, non-redundant and practical for commercial developers. The dynamic models reflecting the business process are built against the background of a static business object model represented using a standard UML Class Diagram. The approach is architecture and repository based. Resultant models can be easily expanded into system level event models reflecting the full impact of operations on the underlying business objects. If desired, this can extend to model technical events, such as user interaction with a GUI. The resultant models are easily mapped to technical design architectures which are GUI, Client Server and Internet capable. The tutorial will cover the concepts, but not the detail involved in this mapping. A paper will be issued to attendees which covers the mapping in detail.

Welcome to the 24th ACM International Conference on User Modeling, Adaptation, and Personalization (UMAP 2016) in Halifax, Canada, July 13-16, 2016. UMAP is the premier international conference for researchers and practitioners working on systems that adapt to individual users or to groups of users. UMAP is the successor of the biennial User Modeling (UM) and Adaptive Hypermedia and Adaptive Web-based Systems (AH) conferences that were merged in 2009. It has traditionally been organized under the auspices of User Modeling Inc. This year (2016) UMAP became an ACM conference, sponsored by ACM SIG CHI and SIG WEB. The conference spans a wide scope of topics related to user modeling, adaptation, and personalization. UMAP 2016 is focused on bringing together cutting-edge research from user interaction and modeling, adaptive technologies, and delivery platforms. It includes high-quality peer-reviewed papers featuring substantive new research in one of five research areas, each chaired by leaders in the field: User Modeling for Recommender Systems (chairs: Alexander Felfernig & Pasquale Lops)Adaptive & Personalized Educational Systems (chairs: Antonija Mitrovic & Kalina Yacef)Personalization in the Social Web & Crowdsourcing Era (chairs: Alessandro Bozzon & Harith Alani)Adaptive, Intelligent, & Multimodal User Interfaces (chairs: Julien Epps & Hatice Gunes)Architectures, Techniques, & Methodologies for UMAP (chairs: Stephan Weibelzahl & Mihaela Cocea) This year we received 123 submissions. In keeping with UMAPs rigorous standards, each paper was carefully reviewed by members of the Program Committee (PC) while the Area Chairs (ACs) coordinated the reviews and provided recommendations to the Program Chairs. The international Program Committee (PC) consisted of 132 members who were assisted by 49 subreviewers. These were leading researchers as well as highly promising young researchers. Papers were assigned to at least 4 members of the PC and to 1 AC member based on their expertise, interests, and other factors. Each paper received at least 3 reviews, and 95% received 4 reviews. After the initial reviews were submitted, the designated AC facilitated discussion amongst reviewers in order to resolve differences and correct misunderstandings. The AC then provided a summative meta-review and a recommendation to the Program Chairs. The final decisions were based on these recommendations, the meta-reviews, and reviewer scores. We accepted 21 long papers (23.9% acceptance rate) and 13 short papers (27.6% acceptance rate) for oral presentation and an additional 17 extended abstracts for poster presentation and inclusion in the proceedings. The program also features posters, demos, and late breaking results, which collectively showcase the wide spectrum of novel ideas and latest results in user modeling, adaptation and personalization. We also invited three distinguished keynote speakers, each illustrating significant issues and prospective directions for the field. Hossein Derakhshan (shared keynote speaker with Hypertext 2016) is an Iranian-Canadian blogger who was imprisoned in Tehran from November 2008 to November 2014. He has been called the "father of Persian blogging" and has helped promote podcasting in Iran. His talk, "Killing the Hyperlink, Killing the Web: the Shift from Library-Internet to Television-Internet," reflects his views on the Internet today.Lada Adamic leads the Product Science group within Facebook's Data Science Team. She is also an adjunct associate professor at the University of Michigan's School of Information and Center for the Study of Complex Systems. Her talk "The Life and Times of Information in Networks" focuses on cascades of information-sharing and resharing within social media.Sandra Carberry was one of the founders of the User Modeling research area at the first workshop in Maria Laach in 1986. As appropriate for the 30th anniversary, her talk "User Modeling: The Past, The Present and The Future" discusses how the field evolved, insights into where the field is headed, and the hottest topics for exploration. The conference includes a doctoral consortium that provides an opportunity for doctoral students to explore and develop their research interests under the guidance of distinguished scholars. This track received 18 submissions of which nine were accepted as full papers and eight as posters. A set of seven workshops and two tutorials round out the program. (Workshop) IFUP: Workshop on Multi-dimension Information Fusion for Modeling and Personalisation (half-day) organized by Robin Burke (DePaul University, USA), Feida Zhu (Singapore Management University, Singapore), Neil Yorke-Smith (American University of Beirut, Lebanon), and Guibing Guo (Northeastern University, China)(Workshop) INRA: News Recommendation and Analytics (half-day) organized by Jon Atle Gulla (Norwegian University of Science and Technology Trondheim, Norway), Luc Martens (Minds-UGent-WiCa Ghent, Belgium), Özlem Özgöbek (Norwegian University of Science and Technology Trondheim, Norway), and Nafiseh Shabib (TNS Gallup, Oslo, Norway)(Workshop) SOAP: Workshop on Surprise, Opposition, and Obstruction in Adaptive and Personalized Systems (half-day) organized by Peter Knees (Johannes Kepler University Linz, Austria), Kristina Andersen (Studio for Electro Instrumental Music, Amsterdam, the Netherlands), Alan Said (Recorded Future, Gothenburg, Sweden), and Marko Tkalcic (Free University of Bozen-Bolzano, Italy)(Workshop) HAAPIE: Human Aspects in Adaptive and Personalised Interactive Environments (half-day) organized by Panagiotis Germanakos (SAP SE, Germany), Marios Belk (Department of Computer Science, University of Cyprus), George Samaras (Department of Computer Science, University of Cyprus), and Vania Dimitrova (University of Leeds, UK)(Workshop) EvalUMAP: Towards comparative evaluation in the user modelling, adaptation and personalization space (full-day) organized by Owen Conlan (Trinity College Dublin, Ireland), Liadh Kelly (Trinity College Dublin, Ireland), Kevin Koidl (Trinity College Dublin, Ireland), Séamus Lawless (Trinity College Dublin, Ireland), Killian Levacher (Trinity College Dublin, Ireland), and Athanasios Staikopoulos (Trinity College Dublin, Ireland)(Workshop) FuturePD: The future of personal data: envisioning new personalized services enabled by Quantified Self technologies (half-day) organized by Amon Rapp (University of Torino, Italy), Federica Cena (University of Torino, Italy), Judy Kay (University of Sidney, Australia), Bob Kummerfeld (University of Sydney, Australia), Frank Hopfgartner (University Gardens Glasgow, UK), Jakob Eg Larsen (Technical University of Denmark, Denmark), and Elise van den Hoven (University of Technology Sydney, Australia). (Workshop) PALE: Personalization Approaches in Learning Environments (full-day) organized by Milos Kravcik (RWTH Aachen University, Germany), Olga C. Santos (UNED, Spain), Jesus G. Boticario (UNED, Spain), and Maria Bielikova (FIIT STUBA, Slovakia)(Tutorial) Semantics-Aware Techniques for Social Media Analysis, User Modeling, and Recommender Systems (half-day) by Pasquale Lops and Cataldo Musto (University of Bari Aldo Moro, Italy)(Tutorial) Games, Gamification and Personalization (half-day) by Amon Rapp (University of Torino, Italy).

Usability evaluation is one of the essential aspects of software production. This evaluation should be done during the entire life cycle of a software application, from pre-production to production and post-production. However, the collection and evaluation of usability data can be a very challenging, time-consuming, and expensive task to be conducted manually, particularly for certain types of products and working conditions. These challenges may include the need to recruit participants fully engage and motivate them during evaluation, and factor in environmental conditions. Other challenges may include collecting data in real-world environments, especially when the users are geographically dispersed, minimizing evaluator and participant bias, and analyzing complex data sets, particularly when the volume of data is large. This research explores an alternative approach to automate the whole process of usability evaluation by utilizing data mining and machine learning techniques on data recorded in log files. The objective of this dissertation is to extract the value of usability-related metrics from user interactions and estimate usability measures in a quantitative manner. The use of log files for usability evaluation offers several advantages over traditional methods of evaluation, such as collecting data objectively without the need for subjective interpretation of the evaluator in some cases, creating a comprehensive view of user interactions with the possibility of identifying user behavioral patterns and trends that appear in large data sets, and collecting data from real-world scenarios instead of data from simulated scenarios. Other advantages include reducing evaluation costs, enabling remote data collection from anywhere in the world, which leads to the identification of location-dependent usability problems, and facilitating continuous monitoring over time, which leads to the identification of time-dependent usability issues. In this dissertation, Chapter II provides a comprehensive categorization, comparison, and summary of the pertinent usability evaluation techniques that utilize log files as input data in both academic and industrial research. Each method is examined carefully, and its respective strengths and weaknesses are highlighted to provide a systematic understanding of the advantages and limitations of each technique. Chapter III assesses the originality of the research questions and proposed solutions. Given the ongoing development and refinement of log file analysis tools and techniques by proficient teams in advanced corporations, and the substantial research already conducted in web usage mining, this dissertation compares its solution with prior works in these two domains. The similarities and disparities between them are evaluated to determine the uniqueness and value of

Healthcare 4.0, which is the integration of digital technologies in healthcare, promises to bring about revolutionary advancements but also introduces significant cybersecurity challenges. This research seeks to address the growing concerns by investigating security threats in healthcare 4.0 systems. The study uses a multifaceted methodology that includes a comprehensive literature review and empirical analysis using advanced algorithms such as Random Forest. Using visualization techniques, data distribution analysis, and intrusion detection experiments, the research identifies common vulnerabilities and patterns in healthcare 4.0 environments. The findings highlight the need for proactive measures and strong policies to protect patient data integrity, safeguard medical infrastructure, and ensure continuous provision of health care services. This study calls for a holistic approach to cyber security with an emphasis on collaborative efforts toward strengthening Healthcare 4.0 systems against emerging threats.

Patterns of user interaction behavior in online social networks have attracted increasing academic interest, since the quantitative understanding of user behavior is helpful to uncover the origins of many socioeconomic phenomena. In order to gain insights into user interaction behavior characteristics in microblogging, we have crawled Sina Microblog data and obtained user profiles and statuses. Six features of users are studied in our experiments, which are followers and friends, statuses and favorites, retweets and comments. Empirical analysis reveals some common statistical features of user behavior in microblogging. First, all of these properties follow different power-law distributions. Especially, followers and statuses exhibit a double-power-law distribution and friends follow a specific interval power law distribution. Second, there exists a strong positive correlation between statuses and followers, retweets and comments, but the relation between followers and friends is weak. Whats more, there exists a moderate correlation between the number of friends and statuses, retweets and followers, and comments and followers. Third, only a small number of statuses have extreme dissemination and concern. From a quantitative view, the result shows that the strong effect of celebrity cannot be ignored in Sina Microblogs. We believe this paper has added rich empirical materials to our knowledge and understanding of user behavior in microblogging.

The extensive adoption of cyber-physical systems (CPS) in daily life has led to an increase in cybersecurity vulnerabilities in both physical and digital domains. Traditional rule-based and signature defenses are inadequate for safeguarding against emerging technologies and zero-day threats. This paper introduces a methodology for threat detection in CPS that is driven by behavioral analysis and utilizes machine learning, illustrated through a case study of a Smart Home System. User interaction and environmental signals undergo normalization via rule-based deviation detection (including after-hours access, rapid toggling, and multi-location/IP flags). These normalized signals are subsequently combined with temporal and contextual features and input into an XGBoost classifier, which includes probability calibration for the classification of anomaly severity (Low vs. High). This study employs STRIDE (Spoofing, Tampering, Repudiation, Information Disclosure, Denial of Service, and Elevation of Privilege) for systematic threat analysis, achieving a balance between detection accuracy and privacy by relying on behavioral cues rather than extensive personal information. In a held-out test set, the classification model demonstrated an accuracy of 0.91, ROC-AUC of 0.94, PR-AUC (High) of 0.88, precision (High) of 0.84, recall (High) of 0.86, F1 (High) of 0.85, and a Brier score of 0.12, indicating robust discrimination and well-calibrated probabilities. The proposed approach is systematic and reproducible, enabling rapid, transparent, and flexible mitigation while enhancing the resilience of CPS against emerging attack vectors in practical applications.

An exceptional feature of the development of modern operating systems based on the Linux kernel is their leading use in cloud technologies, mobile devices and the Internet of things, which is accompanied by the emergence of more and more security threats at the kernel level. In order to improve the security of existing and future Linux distributions, it is necessary to analyze the existing approaches and tools for automated vulnerability detection and to conduct experimental security testing of some current versions of the kernel. The research is based on fuzzing - a software testing technique, which consists in the automated detection of implementation errors by sending deliberately incorrect data to the input of the fuzzer and analyzing the program's response at its output. Using the Syzkaller software tool, which implements a code coverage approach, vulnerabilities of the Linux kernel level were identified in stable versions used in modern distributions. The direction of this research is relevant and requires further development in order to detect zero-day vulnerabilities in new versions of the kernel, which is an important and necessary link in increasing the security of the Linux operating system family.

Identifying when customers want an organisation's products and services has been high on the list of priorities for a significant length of time. Many data are bought and sold to identify key times in a customer's lifecycle. The mantra of marketing is that the right message needs to be communicated to the right person, at the right time. Yet in this situation, the techniques of survival analysis, widely used in non-marketing contexts to identify the time to occurrence of critical events, is hardly used. The authors outline how survival analysis has been used in a number of case studies and illustrate the dangers of not using the insights contained within survival analysis when trying to understand time-based issues. Customer lifetime analysis uses customer information like the typical time between purchases to predict the potential value of a customer. Simple means and summary statistics can be seriously biased and misleading for this type of data. Survival analysis is a good, sound and flexible tool to analyse length of time-to-next-purchase and cancellation data. As a result the output from survival analysis can be fed into various forms of customer lifetime analysis to provide more reliable and accurate conclusions. This can lead to a better understanding of customer lifecycles and value and, as a result, more efficient use of the available budgets.

Advanced graph embedding for intelligent heating, ventilation, and air conditioning optimization: An ensemble learning-based recommender system

Chapter 10 - General design topics

Flood disasters in North Sumatra Province show an increasing trend, both in terms of the frequency of occurrences and the magnitude of their impacts, thereby necessitating adaptive and responsive crisis communication strategies that address community needs. In this context, Instagram has become an important medium for disseminating disaster-related information to the public. This study aims to analyze the role of Instagram in the dissemination of flood disaster information in North Sumatra through a case study of the official accounts @bpbdprovinsisumut and @infobmkgsumut, using the perspectives of contemporary communication theory, crisis communication theory, and agenda-setting theory. The research employs a descriptive qualitative approach with content analysis techniques applied to posts and user interactions during the disaster period. The findings indicate a complementary division of roles: the @infobmkgsumut account is more dominant in the pre-disaster phase by delivering early warning information based on meteorological data, while the @bpbdprovinsisumut account focuses on the emergency response and post-disaster phases through evacuation information and the coordination of aid. The use of visual elements such as infographics and field documentation has proven effective in simplifying technical messages so they are easily understood by the public. However, the communication pattern applied remains predominantly one-way and institutional in nature, with limited emphasis on empathy and the narratives of victims’ experiences. In addition, the management of two-way interactions in the comment sections has not been optimally utilized as a channel for reporting residents’ emergency conditions. This study concludes that Instagram has evolved into a dynamic social space in disaster management, yet strengthening human-centered message framing strategies is still necessary.

In this work we propose intelligent, automated content analysis techniques for different media to extract knowledge from the multimedia content. Information derived from different sources/modalities will be analyzed and fused, in terms of spatiotemporal, personal and even social contextual information. In order to achieve this goal, semantic analysis will be applied to the content items, taking into account the content itself (e.g., text, images and video), as well as existing personal, social and contextual information (e.g., semantic and machine-processable metadata and tags). The above process exploits the so-called “Media Intelligence” towards the ultimate goal of identifying “Collective Intelligence”, emerging from the collaboration and competition among people, empowering innovative services and user interactions. The utilization of “Media Intelligence” constitutes a departure from traditional methods for information sharing, since semantic multimedia analysis has to fuse information from both the content itself and the social context, while at the same time the social dynamics have to be taken into account. Such intelligence provides added-value to the available multimedia content and renders existing procedures and research efforts more efficient.KeywordsCollective intelligenceMedia intelligenceSocial media