Biblio

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. In this work, we propose to integrate security in the slice life cycle, impacting its management and orchestration that relies on the virtualization/softwarisation infrastructure. The proposed security architecture connects the demands specified by the tenants through as-a-service mechanisms with built-in security functions relying on the ability to combine enforcement and monitoring functions within the software-defined network infrastructure. The architecture exhibits desirable properties such as isolating slices down to the hardware resources or monitoring service-level performance.

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. This paper presents a novel approach that bridges the gap between network security and privacy. It makes possible to perform DPI directly on encrypted traffic, without knowing neither the traffic content, nor the patterns of detection signatures. The relevance of our work is that it preserves the delicate balance in the security market ecosystem. Indeed, security editors will be able to protect their distinctive detection signatures and supply service providers only with encrypted attack patterns. In addition, service providers will be able to integrate the encrypted signatures in their architectures and perform DPI without compromising the privacy of network communications. Finally, users will be able to preserve their privacy through traffic encryption, while also benefiting from network security services. The extensive experiments conducted in this paper prove that, compared to existing encryption schemes, our solution reduces by 3 orders of magnitude the connection setup time for new users, and by 6 orders of magnitude the consumed memory space on the DPI appliance.

Web applications are the core enabler for most Internet services today. Their standard interfaces allow them to be composed together in different ways in order to support different service workflows. While the modular composition of applications has considerably simplified the provisioning of new Internet services, it has also added new security challenges; the impact of a security breach propagating through the chain far beyond the vulnerable application. To secure web applications, two distinct approaches have been commonly used in the literature. First, white-box approaches leverage the source code in order to detect and fix unintended flaws. Although they cover well the intrinsic flaws within each application, they can barely leverage logic flaws that arise when connecting multiple applications within the same service. On the other hand, black-box approaches analyze the workflow of a service through a set of user interactions, while assuming only little information about its embedded applications. These approaches may have a better coverage, but suffer from a high false positives rate. So far, to the best of our knowledge, there is not yet a single solution that combines both approaches into a common framework. In this paper, we present a new grey-box approach that leverages the advantages of both white-box and black-box. The core component of our system is a semi-supervised learning framework that first learns the nominal behavior of the service using a set of elementary user interactions, and then prune this nominal behavior from attacks that may have occurred during the learning phase. To do so, we leverage a graph-based representation of known attack scenarios that is built using a white-box approach. We demonstrate in this paper the use of our system through a practical use case, including real world attack scenarios that we were able to detect and qualify using our approach.

In this paper, we propose a new risk analysis framework that enables to supervise risks in complex and distributed systems. Our contribution is twofold. First, we provide the Risk Assessment Graphs (RAGs) as a model of risk analysis. This graph-based model is adaptable to the system changes over the time. We also introduce the potentiality and the accessibility functions which, during each time slot, evaluate respectively the chance of exploiting the RAG's nodes, and the connection time between these nodes. In addition, we provide a worst-case risk evaluation approach, based on the assumption that the intruder threats usually aim at maximising their benefits by inflicting the maximum damage to the target system (i.e. choosing the most likely paths in the RAG). We then introduce three security metrics: the propagated risk, the node risk and the global risk. We illustrate the use of our framework through the simple example of an enterprise email service. Our framework achieves both flexibility and generality requirements, it can be used to assess the external threats as well as the insider ones, and it applies to a wide set of applications.

In this paper, we propose a new risk analysis framework that enables to supervise risks in complex and distributed systems. Our contribution is twofold. First, we provide the Risk Assessment Graphs (RAGs) as a model of risk analysis. This graph-based model is adaptable to the system changes over the time. We also introduce the potentiality and the accessibility functions which, during each time slot, evaluate respectively the chance of exploiting the RAG's nodes, and the connection time between these nodes. In addition, we provide a worst-case risk evaluation approach, based on the assumption that the intruder threats usually aim at maximising their benefits by inflicting the maximum damage to the target system (i.e. choosing the most likely paths in the RAG). We then introduce three security metrics: the propagated risk, the node risk and the global risk. We illustrate the use of our framework through the simple example of an enterprise email service. Our framework achieves both flexibility and generality requirements, it can be used to assess the external threats as well as the insider ones, and it applies to a wide set of applications.

In this paper, we propose a new risk analysis framework that enables to supervise risks in complex and distributed systems. Our contribution is twofold. First, we provide the Risk Assessment Graphs (RAGs) as a model of risk analysis. This graph-based model is adaptable to the system changes over the time. We also introduce the potentiality and the accessibility functions which, during each time slot, evaluate respectively the chance of exploiting the RAG's nodes, and the connection time between these nodes. In addition, we provide a worst-case risk evaluation approach, based on the assumption that the intruder threats usually aim at maximising their benefits by inflicting the maximum damage to the target system (i.e. choosing the most likely paths in the RAG). We then introduce three security metrics: the propagated risk, the node risk and the global risk. We illustrate the use of our framework through the simple example of an enterprise email service. Our framework achieves both flexibility and generality requirements, it can be used to assess the external threats as well as the insider ones, and it applies to a wide set of applications.

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. Our infrastructure allowed us to draw the first comprehensive picture of a phishing attack, from the time in which the attacker installs and tests the phishing pages on a compromised host, until the last interaction with real victims and with security researchers. Our study presents accurate measurements of the duration and effectiveness of this popular threat, and discusses many new and interesting aspects we observed by monitoring hundreds of phishing campaigns.