Abstract
Tampered multimedia content is increasingly being used in a broad range of cybercrime activities. The spread of fake news, misinformation, digital kidnapping, and ransomware-related crimes are among the most recurrent crimes in which manipulated digital photos are being used as an attacking vector. One of the linchpins of accurately detecting manipulated multimedia content is the use of machine learning and deep learning algorithms. This work proposed a dataset of photos and videos suitable for digital forensics, which has been used to benchmark Support Vector Machines (SVM) and Convolution Neural Networks algorithms (CNN). An SVM-based module for the Autopsy digital forensics open-source application has also been developed. This was evaluated as a very capable and useful forensic tool, winning second place on the OSDFCon international Autopsy modules competition.

Abstract
Since its first introduction in late 90s, the use of marketplaces has continued to grow, today virtually everything from physical assets to services can be purchased on digital marketplaces, real estate is not an exception. Some marketplaces allow acclaimed asset owners to advertise their products, to which the services gets commission/percentage from proceeds of sale/lease. Despite the success recorded in the use of the marketplaces, they are not without limitations which include identity and property fraud, impersonation and the use of centralised technology with trusted parties that are prone to single point of failures (SPOF). Being one of the most valuable assets, real estate has been a target for marketplace fraud as impersonators take pictures of properties they do not own, upload them on marketplace with promising prices that lures innocent or naive buyers. This paper addresses these issues by proposing a self sovereign identity (SSI) and smart contract based framework for identity verification and verified transaction management on secure digital marketplaces. First, the use of SSI technology enable methods for acquiring verified credential (VC) that are verifiable on a decentralised blockchain registry to identify both real estate owner(s) and real estate property. Second, the smart contracts are used to negotiate the secure transfer of real estate property deeds on the marketplace. To assess the viability of our proposal we define an application scenario and compare our work with other approaches.

Abstract
In medical organizations, healthcare providers need to have fast access to patients' medical information in order to make accurate diagnoses as well as to provide appropriate treatments. Efficient healthcare is thus highly dependent on doctors being provided with access to patients' medical information at the right time and place. However it frequently happens that critical pieces of pertinent information end up not being used because they are located in information systems that do not inter-operate in a timely manner. Unfortunately the standard operational mode for many healthcare applications, and even healthcare institutions, is to be managed and operated as isolated islands that do not share information in an efficient manner. There are many reasons that contribute to this grim state of affairs, but what interests us the most is the lack of enforceable security policies for systems interoperability and data exchange and the existence of many heterogeneous legacy systems that are almost impossible to directly include into any reasonable secure interoperable workflow. In this paper we propose a RBAC mobile agent access control model supported by a specially managed public key infrastructure for mobile agent's strong authentication and access control. Our aim is to create the right means for doctors to be provided with timely accurate information, which would be otherwise inaccessible, by the means of strongly authenticated mobile agents capable of securely bridging otherwise isolated institutional eHealth domains and legacy applications. © 2013 IEEE.

Abstract
New governmental legislation introduced e-prescription as mandatory in the Portuguese health system. This changes consequences were not properly considered, which caused security problems related to patient and prescriber's data, such as digital identity fraud or access to prescriptions history to build clinical profiles. In order to evaluate the e-prescribing software users awareness to those risks, a survey took place, and the results revealed ignorance of certain obligations and procedures of the e-prescribing process. A significant part of doctors are not conscious about where the patient's data is stored neither about the risks related with prescription's information. © 2013 IEEE.

Abstract
Background Intrusion Tolerant Systems (ITS) aim to maintain system security despite adversarial presence by limiting the impact of successful attacks. Current ITS risk managers rely heavily on public databases like NVD and Exploit DB, which suffer from long delays in vulnerability evaluation, reducing system responsiveness.Objective This work extends the HAL 9000 Risk Manager to integrate additional real-time threat intelligence sources and employ machine learning techniques to automatically predict and reassess vulnerability risk scores, addressing limitations of existing solutions.Methods A custom-built scraper collects diverse cybersecurity data from multiple Open Source Intelligence (OSINT) platforms, such as NVD, CVE, AlienVault OTX, and OSV. HAL 9000 uses machine learning models for CVE score prediction, vulnerability clustering through scalable algorithms, and reassessment incorporating exploit likelihood and patch availability to dynamically evaluate system configurations.Results Integration of newly scraped data significantly enhances the risk management capabilities, enabling faster detection and mitigation of emerging vulnerabilities with improved resilience and security. Experiments show HAL 9000 provides lower risk and more resilient configurations compared to prior methods while maintaining scalability and automation.Conclusions The proposed enhancements position HAL 9000 as a next-generation autonomous Risk Manager capable of effectively incorporating diverse intelligence sources and machine learning to improve ITS security posture in dynamic threat environments. Future work includes expanding data sources, addressing misinformation risks, and real-world deployments.

Abstract
Vehicle cybersecurity has emerged as a critical concern, driven by innovation in the automotive industry, e.g., autonomous, electric, or connected vehicles. Current efforts to address these challenges are constrained by the limited computational resources of vehicles and the reliance on connected infrastructures. This motivated the foundation of Vehicle Security Operations Centers (VSOCs) that extend IT-based Security Operations Centers (SOCs) to cover the entire automotive ecosystem, both the in-vehicle and off-vehicle scopes. Security Orchestration, Automation, and Response (SOAR) tools are considered key for implementing an effective cybersecurity solution. However, existing state-of-the-art solutions depend on infrastructure networks such as 4G, 5G, and WiFi, which often face scalability and congestion issues. To address these limitations, we propose a novel SOAR architecture EVSOAR that leverages the EV charging stations for connectivity and computing to enhance vehicle cybersecurity. Our EV-specific SOAR architecture enables real-time analysis and automated responses to cybersecurity threats closer to the EV, reducing cellular latency, bandwidth, and interference limitations. Our experimental results demonstrate a significant improvement in latency, stability, and scalability through the infrastructure and the capacity to deploy computationally intensive applications that are otherwise infeasible within the resource constraints of individual vehicles.