Detailed Program

The Internet of Everything (IoE) connects things, people, data, and processes into one intelligent network. It provides flexibility and ease of use capabilities to smart connected devices used in our daily life. Under the 5G umbrella, IoE evolved rapidly due to the development of low-cost sensors and massive Machine-Type Communication (mMTC) applications in 5G and Beyond (B5G) wireless networks. The IoE ecosystem is characterized by a huge number of connected devices and people, user-context heterogeneity, and fully distributed interactions, hence high levels of complexity. The weak security for Access Control (AC) mechanisms and default configuration can get the whole system compromised and result in sensitive information theft and disclosure. AC systems need to adapt dynamically to incorporate context factors such as the identity of the user or device, situational context, degree of trust, and current security threat level. Additionally, the AC decision will be dynamically adjusted based on the changes in user and device environments to enhance the security of the underlying systems. One of the challenges in building a dynamic environment is accessing resources when subjects are usually restricted by their roles in traditional AC systems. Relying on credentials like identity and roles for authenticating a subject is complex in IoE as they are easily intercepted and forged. Instead, using IoT device fingerprint (DF) as an identity device-specific signature allows for accomplishing robust authentication and dynamically re-evaluating it when the context changes. It gathers the device’s physical and environment-based features to create an inherent and unique identity of devices reducing the vulnerability of node forgery and identity theft. Device fingerprints provide low complexity, robustness, and secure capability that make it hard to forge or duplicate identity. In this paper, we conduct a comprehensive overview of device fingerprint mechanisms for securing IoT-5G devices and we explore the applicability of device fingerprints in AC systems.

Today, many technologies make it possible to secure the fleets of professional smartphones. But what about the BYOD setting where personal devices, in particular smartphones, are used at work? Unlike the devices provided by companies to their members, which are administered and on which the IT service can control uses and threats, personal mobiles represent a private space on which organizations have reduced control. Thus, entities implementing BYOD are found in a dead end: how to secure the use of personal mobiles without invading personal space and in compliance with data protection laws? When the French army shared this problem with us, we have developed together a security response adapted to their operational reality. This talk will present the course of a real case, from the first challenges encountered until technological innovation today deployed on more than 10,000 mobile devices.The presentation will be provided by LCL Bruno Camel, head of the Digital Transformation and Data section of the Army and Renaud Gruchet, Chief Business Innovation Officer of Pradeo.

As the number of geo-distributed connected sensors and the need to perform complex functionality increase in Industrial IoT-based systems, so does the transferred data volume. Mainframes and even edge computing show their limits when facing this intensive computing. To answer this challenge, an emerging trend, called Industrial Continuous Computing (ICC), advocates for truly distributed computation in the network, from the Smart Peripheral Devices (SPD) to the end-server or actuators. This, in turn, raises the issue of communication security. In this context, protecting communications in a dynamic architecture is challenging, as attackers may have physical access to a legitimate device. Surface attack on the communication includes confidentiality and integrity of the data, which in turn requires integrity of the software stack manipulating them. Breaches on these properties are likely to lead to secret exposure or sabotage. We present a novel approach based on open source software and secure hardware aiming to ensure end- to-end security for communication as well as securing devices authentication, enabling the confidentiality and integrity of data exchanges between the different SPDs and the end-server. Our approach uses publish-subscribe communication protocols (i.e. many-to-many) to build scalable and dynamic computing architecture. Amongst them, OPC UA PubSub provides security and interoperability to the ICC, allowing end-to-end encryption. This security however relies on securely embedded secrets on the node. We further secure authentication against specific threats to ICC, by using a Trusted Platform Module (TPM) as a secure element to protect the secrets embedded on devices, and relying on secure boot to protect this secure element against attackers. By doing so, we were able to set up fully secure a geo-distributed industrial computing infrastructure dedicated to the monitoring of railway facilities, connecting sensors, edge devices and data server in order to perform predictive maintenance.

The Industrial Internet of Thing (IIOT) induces new uses based on increased treatment and recovery of industrial data. These new uses generate additional value for manufacturers: reduction of maintenance costs, improvement of yields, resale of data, etc. This intervention offers a method for taking into account the risks associated with the use of IIOTs, architectural principles and adaptations of industrial information systems.

With the increasing demand for smart and connected devices (IoT) and embedded systems have become an integral part of our daily lives. However, this also brings new challenges in terms of security, as these systems often deal with sensitive data and / or perform critical operations. Machine learning techniques have emerged as a promising solution for enhancing the security of embedded systems. By leveraging large amounts of data, machine learning algorithms can identify patterns and anomalies that may indicate a security breach and trigger appropriate responses in real-time. In this article we will provide an overview of the use of machine learning in securing embedded systems highlighting its benefits, potential challenges while discussing some of the recent research in this area.

The Internet of Things (IoT) is a collection of interconnected devices, each becoming increasingly complicated and numerous. They frequently employ modified hardware and software without taking security risks into account, which makes them a target for cybercriminals, especially malware and rootkit crafter. In this extended abstract, we will present two strategies for exploiting electromagnetic side channels to address two issues: rootkit detection difficulties and malware categorization challenges in the presence of obfuscations. Both tactics center on IoT devices, target ARM (raspberry-Pi) and MIPS (CI.20) architectures, and use machine/deep learning techniques. The talk will highlight all the results obtained from the ARN project "Automated Hardware Malware Analysis" (AHMA - Annelie's JCJC).

The Zero-trust (ZT) model is an increasingly popular model that relies on the idea that no trust should be granted to any entity (network, persons, devices) by default. ZT model is gaining attention from both research and practice, with various levels of adequation between research developed and real-life applications. NIST provided a standard to fulfill requirements of ZT architecture of network core but many practical aspects remain unspecified, some of them requiring solving first research challenges in order to be implemented efficiently. An example of such an unspecified field is the integration of IoT/Smart Peripheral Devices (SPD). Various reasons explain this gap: specificities of such resources (possibly lower energy/computation power), their lifecycle, and their use, strongly depending on the use of the whole platform IoT devices are part of. Moreover, additional difficulty to have a good understanding is induced by the fact that both Zero Trust and IoT are identified as promising trends in cybersecurity: many vendors/researchers tag their solutions as IoT integration into the ZT model, with little to no effective compliance to ZT model or standard. Industry is providing many practice-oriented literature, that has to be compared to academic work and standards, in order to consolidate the current state of knowledge and solutions offered to realize this integration. In this paper, we conduct a literature review of non-academic publications, in order to consolidate current knowledge, trends, and future challenges for the industrial integration of IoT devices in ZT architecture.

Military and tactical systems are heterogeneous, encompassing devices with low computing power and network capacity. Such networks can be secured by following the zero trust paradigm: every access request to resources is verified, without relying on inherent trust between the requester and the resource. However, operational needs can require different domains, such as different nations in a coalition, to federate, to enable sharing of resources between domains. This contradicts the principle of zero trust, as information on the requester cannot be verified by the domain offering the resource, and therefore access inherently relies on trust between domains. This paper explores a solution for federating tactical network architectures, while following the zero trust paradigm. In particular, due to the power constraints on devices composing tactical architectures, the presented solution does not require invasive software to be installed in requester devices.

The object of the presentation is on the one hand to demonstrate the possibilities offered by the software radio to extract the MAC address (BD_ADDR) from Bluetooth BR/EDR devices by a radio listening procedure of a Bluetooth communication established withinFrom a pico-russet, and on the other hand to extract this Mac address again by interacting by software radio with a Bluetooth device configured in a "discoverable" mode.This demonstration illustrates the ability to extract the BD_ADDR address from a Bluetooth device even if the standard has taken precautions to hide this information.