Nowadays, all the 4G/5G voice solutions are offered by the IMS (IP Multimedia Subsystem) system. They include 4G VoLTE (Voice over LTE) and 5G VoNR (Voice over New Radio), as well as a non-3GPP access solution, VoWiFi (Voice over WiFi). Since VoWiFi is implemented on mobile OS, instead of the modem with hardware security for VoLTE and VoNR, it can be a vulnerability of the IMS system and may further impair other IMS-based services. It has been exposed that due to vulnerable Vo WiFi sessions, several IMS vulnerabilities are discovered and the smartphones with IMS-based call services may suffer from a stealthy call DoS attack, where the smartphones cannot make or receive any calls and no ringtone or messages are appeared on them during the attack. In this paper, we develop a detector that can remotely and concurrently detect such DoS attack for multiple UEs (User Equipments). It consists of three major components: session hijacking, SIP fabrication, and call detection. We demonstrate its effectiveness in the operational networks of two carriers from different countries by considering three different phone models with VoLTE and VoWiFi call services.
In this poster paper, we propose and demonstrate an architectural framework for service Management and Orchestration (M&O) in Sixth-Generation (6G) communication systems. This architecture was designed by the Hexa-X project, which is a European flagship project dedicated to developing a vision and technological enablers for 6G. To provide a comprehensive and high-level description, we consider three views: (i) Functional View; (ii) Structural View; and (iii) Deployment View. We first discuss 6G service M&O before delving deeper into each view.
Wireless collaborative mixed reality (WCMR) has many fascinating applications in education, training, manu-facturing, and gaming. In this demo, we develop a WCMR-based firefighter training system that provides firefighters with experiences in extreme and diverse fire accidents without any safety concerns. In such a system, it is important to ensure that all firefighters can see almost the same status of the fire accident to facilitate collaborative training. This is challenging due to the heterogeneous communication delays of different network users. We propose the latency compensation algorithm that determines when the edge server should transmit the message to users based on the estimated latency of each user. Our experiment demon-strates around 55% synchronization performance improvement while guaranteeing at least 60 frames per second (FPS).
The infrastructure of mobile networks in 5G will offer various services in the form of network slices that can be deployed and implemented in a highly customizable manner. A real dynamic network with time-varying network utility has not been considered in previous works. In this paper, we examine the multi-resource allocation problem for network slicing in an online manner where the utility functions change over time. To solve the problems as mentioned above, we present Metis, a first systematic solution. Metis is an online network slice resource allocation framework that combines the time-varying property of the network utility function given the bandwidth and processing capacity constraints with the virtual network functions isolation requirements. As a result, we aim to maximize the cumulative network utility over time. Utilizing state-of-the-art concave optimization methods, we formulate the multi-resource allocation problem. To the best of our knowledge, this is the first work investigating an online method for multi-resources allocation for network slicing in a real-time network. Metis can proveably converge to the optimal solution, and the experiment results show a steady state behavior for Metis which converges in dynamic network settings.
With growing security and privacy concerns in the Smart Grid domain, intrusion detection on critical energy infrastructure has become a high priority in recent years. To remedy the challenges of privacy preservation and decentralized power zones with strategic data owners, Federated Learning (FL) has contemporarily surfaced as a viable privacy-preserving alternative which enables collaborative training of attack detection models without requiring the sharing of raw data. To address some of the technical challenges associated with conventional synchronous FL, this paper proposes FeDiSa, a novel Semi-asynchronous Federated learning framework for power system faults and cyberattack Discrimination which takes into account communication latency and stragglers. Specifically, we propose a collaborative training of deep auto-encoder by Supervisory Control and Data Acquisition sub-systems which upload their local model updates to a control centre, which then perform a semi-asynchronous model aggregation for a new global model parameters based on a buffer system and a preset cut-off time. Experiments on the proposed framework using publicly available industrial control systems datasets reveal superior attack detection accuracy whilst preserving data confidentiality and minimizing the adverse effects of communication latency and stragglers. Furthermore, we see a 35% improvement in training time, thus validating the robustness of our proposed method.
