Virtual Reality (VR) is commonly utilized to examine driver interactions with vulnerable road users (VRUs) in an effective and secure manner. Recent studies, however, have highlighted issues in VR simulations, particularly concerning the authenticity of state-of-the-art pedestrian agent behaviors. These inaccuracies can compromise the perceived realism of the situation, potentially leading to unrepresentative driver reactions. This paper aims to show-case enhancements in pedestrian agent models and evaluate their subsequent advantages. To this end, real pedestrian movements, captured via motion-capture technology, were compared with outputs from a contemporary pedestrian agent model within a VR driving simulator experiment. The findings underpin the advantages of using motion-captured pedestrians to enhance social presence. Additionally, participant feedback emphasized that certain elements, such as head movements, explicit gestures, and subtle cues like hesitation before entering the road, were crucial in distinguishing realistic from unrealistic agents. These insights contribute significantly to refining the focus for systematic advancements in (pedestrian) agent models in VR environments. Such improvements are pivotal in augmenting the users’ sense of presence and the behavioral accuracy of the simulations.

Driving automation is being pushed towards widespread adoption, with significant progress being made continuously. Once the automated vehicle takes over the driving task, the question arises as to how people want to be driven by automation. In order to gain insights into this, a driving simulator study was conducted, in which N = 49 participants experienced an automated urban drive where pedestrians crossed or attempted to cross the road in front of the automated vehicle at various points. The driving style of the automated vehicle was manipulated (aggressive/defensive), while participants rated their desire for control, trust in automation, and acceptance. The results show that there is no general preference for one driving style over the other. Rather, the preferred behavior of the automation depended on the respective traffic scenario, with drivers preferring defensive driving in some crossing situations and aggressive driving in other situations. The present study indicates that, generally, defensive driving behavior is not necessarily the solution preferred by the user. Instead, a more nuanced approach based on the traffic scenario is recommended.

The representation of Internet traffic as connection graphs augments anomaly detection systems by providing insight on the structural connection properties, i.e., who-talks-to-whom. However, these graphs are extremely large and one has to decide in advance on which aspect to focus. In the context of malware detection, this is difficult as malware often mimics legitimate traffic. In this paper, we present a statistical approach for extracting the typical traffic destinations for a set of monitored hosts, and derive a reduced graph that contains only connections that are anomalous for that host. This graph can then be analyzed efficiently. Our system is designed to scale to thousands of monitored hosts. We evaluate our approach using a data set from a real network, and show that we can reliably detect injected malware activity.

Abstract In this paper, we discuss the security threats of next‐generation telecommunication core networks induced by the adoption of Internet technology. The 3GPP IP multimedia subsystem (IMS) is based on the Internet protocol (IP) and hence inherits many security problems from the Internet world. We provide a review of the most prominent vulnerabilities, covering both fundamental functions like routing and name resolution as well as message tampering and implementation attacks, and we show their impact on the IMS. Further, we address the topics of denial of service (DoS) attacks and unsolicited communication (UC). Despite the large number of security threats, the IMS standardization foresees no security monitoring and fully relies on standard countermeasures as known from the Internet. This paper provides a survey of these threats and relates them to the available solutions. We motivate the need for a powerful and modular monitoring platform for the IMS and provide an overview of the existing work in this research field. Copyright © 2009 John Wiley & Sons, Ltd.

Network operators gradually open their interfaces to formerly hidden services. This fosters the development of a new class of mobile applications that take into account user's location and presence information. However, this development also raises problems especially the lack of protection of privacy in location-based services. This paper proposes a service architecture that is aimed at overcoming some of the shortages of currently existing context-aware applications that make use of network providers services as well as existing mobile payment systems. We therefore introduce the combination of tickets together with a novel privacy enhancing mechanism that is based on the notion of pseudonyms. Compared to other privacy enhancing solutions our pseudonym mechanism can also be implemented on mobile devices that have some restrictions regarding resources like memory or processing power. Due to their flexibility tickets can be used for many different kinds of applications. One important aspect in this respect is the highly postulated pay-as-you-go model. We give an example of a transport ticket application and explain the message interaction patterns for the basic functionalities of the systems, regarding aspects like data and privacy protection. This example further shows how 3rd party application providers can build meaningful mobile applications that are accepted by users