WEAK CONTEXT ESTABLISHMENT PROCEDURE FOR MOBILITY AND MULTI- HOMING MANAGEMENT

Abstract: Trust establishment seems to be the most difficult problem in mobility and multi-homing management. Many protocol proposals assume the presence of some security infrastructure (e.g. a Public-Key Infrastructure). However, building such a global infrastructure has not taken place, maybe because it would be too expensive and difficult to deploy. In this paper, we introduce a security context establishment procedure that utilizes reverse hash chains, and does not require pre-existing security information. The procedure is known to be vulnerable to an active Man-in-the-Middle attack in the first message exchange, however, the procedure is efficient, and does not have inherent scalability problems. Key words: security, mobility management, multi-homing management, and trust establishment 1

Rethinking security in IP based micro-mobility

Security problems in micro-mobility are mostly related to trust establishment between mobile nodes and middle-boxes, i.e. mobile anchor points. In this paper, we present a secure micro-mobility architecture that scales well between administrative domains, which are already using different kind of network access authentication techniques. The trust between the mobile nodes and middle boxes is established using one-way hash chains and a technique known as secret splitting. Our protocol protects the middle-boxes from traffic re-direction and related Denial-of-Service attacks. The hierarchical scheme supports signaling optimization and secure fast hand-offs. The implementation and simulation results are based on an enhanced version of Host Identity Protocol (HIP). To our knowledge, our micro-mobility protocol is the first one-and-half round-trip protocol that establishes simultaneously a trust relationship between a mobile node and an anchor point, and updates address bindings at the anchor point and at a peer node in a secure way

Evaluation of 3D Markerless Motion Capture System Accuracy during Skate Skiing on a Treadmill

In this study, we developed a deep learning-based 3D markerless motion capture system for skate skiing on a treadmill and evaluated its accuracy against marker-based motion capture during G1 and G3 skating techniques. Participants performed roller skiing trials on a skiing treadmill. Trials were recorded with two synchronized video cameras (100 Hz). We then trained a custom model using DeepLabCut, and the skiing movements were analyzed using both DeepLabCut-based markerless motion capture and marker-based motion capture systems. We statistically compared joint centers and joint vector angles between the methods. The results demonstrated a high level of agreement for joint vector angles, with mean differences ranging from −2.47° to 3.69°. For joint center positions and toe placements, mean differences ranged from 24.0 to 40.8 mm. This level of accuracy suggests that our markerless approach could be useful as a skiing coaching tool. The method presents interesting opportunities for capturing and extracting value from large amounts of data without the need for markers attached to the skier and expensive cameras

5G-ENSURE - D2.1 Use Cases

This document describes a number of use cases illustrating security and privacy aspects of 5G networks. Based on similarities in technical, service and/or business-model related aspects, the use cases are grouped into use case clusters covering a wide variety of deployments including, for example, the Internet of Things, Software Defined Networks and virtualization, ultra-reliable and standalone operations. The use cases address security and privacy enhancements of current networks as well as security and privacy functionality needed by new 5G features. Each use case is described in a common format where actors, assumptions and a sequence of steps characterising the use case are presented together with a short analysis of the security challenges and the properties of a security solution. Each use case cluster description is concluded with a “5G Vision” outlining the associated enhancements in security and privacy anticipated in 5G networks and systems. A summary of the 5G visions and conclusions are provided at the end of the document

5G-ENSURE D2.4: Security Architecture (draft)

This deliverable (D2.4) of the 5G-ENSURE project describes a draft security architecture for 5G networks. The focus lies on a logical and functional architecture and omits (most) aspects related to physical/deployment architecture. This focus is motivated by general trends such as network de-perimetrization as well as 5G systems’ strong dependency on software defined networking and virtualization in general. Furthermore, this focus has reduced the otherwise strong interdependency between this architecture task and the trust modelling and risk analysis tasks in 5G-ENSURE. Still, each of these three tasks have at the time of writing produced initial draft documents, which will then be re-used in a second iteration of all three tasks, producing updated, final versions

5G-ENSURE D2.4: Security Architecture (draft)

This deliverable (D2.4) of the 5G-ENSURE project describes a draft security architecture for 5G networks. The focus lies on a logical and functional architecture and omits (most) aspects related to physical/deployment architecture. This focus is motivated by general trends such as network de-perimetrization as well as 5G systems’ strong dependency on software defined networking and virtualization in general. Furthermore, this focus has reduced the otherwise strong interdependency between this architecture task and the trust modelling and risk analysis tasks in 5G-ENSURE. Still, each of these three tasks have at the time of writing produced initial draft documents, which will then be re-used in a second iteration of all three tasks, producing updated, final versions