Harnessing the network slicing feature of the 5G architecture and the mobile cloud computing capabilities offered by Mobile Edge Computing (MEC), mobile edge communication systems are emerging that contribute to the convergence of telecom and cloud services. In this scenario, mobile edge slices can be created and deployed by leveraging multi-cloud infrastructure. However, it becomes difficult for potential users, such as mobile edge slice tenants, to evaluate and select the Cloud Infrastructure Provider (CIP) which best meets their heterogeneous require-ments. Therefore, mobile edge systems require slice brokering to mediate with resource and infrastructure providers, so that mobile edge slice tenants can specify a single set of network and cloud service Key Performance Indicator (KPI) requirements. In this paper we present a new type of broker, the Mobile Edge Slice Broker, that allows designing, provisioning, and orchestrating the deployment and operation of end-to-end mobile edge slices over edge and multi-cloud environments, while maintaining dynamic monitoring, reconfiguration and optimization capabilities.

The Sixth Generation (6G) of mobile networks offers the promise of a global interconnected system, serving a large set of applications across multiple fields such as satellite, air, ground, and underwater networks. It will evolve towards a unified network compute fabric that facilitates convergence across ecosystems, fostering design and innovation of new Internet of Things (IoT) applications and services, further leading to an exponential growth of IoT use cases in the post-6G era. This profound evolution will also impact the threat landscape, adding new threat actors, and leading to a new set of cyber security challenges. This paper reviews 6G applications and analyzes their security challenges and existing solutions, covering both the network, application and data layers. It introduces a new concept to security monitoring and attack detection in 6G-enabled IoT systems, leveraging on hierarchical and collaborative approaches, while also satisfying the main 6G’s Key Performance Indicators (KPIs) such as trustworthiness, latency, connectivity, data rate and energy consumption. The proposed solution implements a multi-level Federated Learning (FL) approach between IoT devices and edge computing applications. As compared to current centralized security monitoring and detection solutions, it conciliates better between the attack detection accuracy and the network overhead for implementing this model. We demonstrate the use of the proposed solution through an example scenario involving an Internet of Vehicles that communicate over a 6G network.

5G is envisioned as a transformation of the communications architecture towards multi-tenant, scalable and flexible infrastructure, which heavily relies on virtualised network functions and programmable networks. In particular, orchestration will advance one step further in blending both compute and data resources, usually dedicated to virtualisation technologies, and network resources into so-called slices. Although 5G security is being developed in current working groups, slice security is seldom addressed.

The goal of network intrusion detection is to inspect network traffic in order to identify threats and known attack patterns. One of its key features is Deep Packet Inspection (DPI), that extracts the content of network packets and compares it against a set of detection signatures. While DPI is commonly used to protect networks and information systems, it requires direct access to the traffic content, which makes it blinded against encrypted network protocols such as HTTPS. So far, a difficult choice was to be made between the privacy of network users and security through the inspection of their traffic content to detect attacks or malicious activities.

Phishing is a form of online identity theft that deceives unaware users into disclosing their confidential information. While significant effort has been devoted to the mitigation of phishing attacks, much less is known about the entire life-cycle of these attacks in the wild, which constitutes, however, a main step toward devising comprehensive anti-phishing techniques. In this paper, we present a novel approach to sandbox live phishing kits that completely protects the privacy of victims. By using this technique, we perform a comprehensive real-world assessment of phishing attacks, their mechanisms, and the behavior of the criminals, their victims, and the security community involved in the process -- based on data collected over a period of five months.