Industrial Control Systems (ICS) are designed to provide a service, such as power generation or water treatment, while protecting people, assets, and the environment against hazard. However, ICS now integrate Information Technology (IT) and are interconnected with the outside world such as the Internet, thereby exposing their infrastructures to cyberattacks. Cyberattacks have thus become new threats for industrial system operations and, more specifically, for their safety. To address the issue, this paper presents a comprehensive cybersecurity risk assessment for the safety of ICS. We apply our method to automate industrial cybersecurity risk assessment as specified in the recent (2020) IEC 62443-3-2 standard, which is widely used in the industrial cybersecurity domain. By automating parts of these risk assessment processes, we can reduce the error-prone manual efforts and increase the consistency of risk assessment. More specifically, the proposed risk assessment comprises three parts which, respectively: (1) identify the specific vulnerabilities of industrial control systems, (2) determine the attack scenarios that compromise the safety of the system and (3) assess whether the attack scenarios are tolerable by the organization’s policy. In the first part, we automated the entire threat modeling process of Microsoft Threat Modeling Tool by developing an automatable method for building the system model, in the form of a data flow diagram, from a standard XML file called PLCOpen. This automation of the Microsoft Threat Modeling Tool process enables us to automate vulnerability identification for industrial control systems. In the second part, we enhance a previous work that generates theoretical safety-compromising attack scenarios by building a complete attack scenario from system vulnerabilities to safety compromise. Finally, in the third part, we rank the attack scenarios using a specific risk matrix in order to determine which scenarios exceed the risk tolerable by the organization and therefore require additional controls.

In recent years, Informational Technologies (IT) was massively deployed into Industrial Control Systems (ICS) mainly for its economic benefits. However, this new paradigm, converging IT and Operational Technologies (OT), brings new challenges that companies need to face. Historically, ICS had to cope with safety requirements which ensure the protection of people, environment, and assets. Now, ICS must deal with additional threats, coming from cyberattacks, in order to maintain safety. For that purpose, it becomes essential to develop new cybersecurity technologies and methodologies that allow to assess the safety of ICS against cyberattacks.

Industrial Control Systems (ICS) are specific systems that combine information technology (IT) and operational technology (OT). Due to their interconnection and remote accessibility, they become a target for cyberattacks. As a result of their complexity and heterogeneity in terms of devices and communication protocols, specific security controls and risk analysis methods need to be developed. In particular, in order to reduce the effort of deployment of risk analysis on such complex systems, automated methods need to be provided. This paper deals with automation of the risk identification process for ICS using the STRIDE threat modeling framework. We extend the well-known STRIDE modeling tool, namely Microsoft Threat Modeling Tool (MTMT), with an incremental template dedicated to ICS and provide additional tools to automate the analysis using specific vulnerability extraction from Internet CVE databases.

With the advances in the field of the Internet of Things (IoT) and Industrial IoT (IIoT), these devices are increasingly used in daily life or industry. To reduce costs related to the time required to develop these devices, security features are usually not considered. This situation creates a major security concern. Many solutions have been proposed to protect IoT/IIoT against various attacks, most of which are based on attacks involving physical access. However, a new class of attacks has emerged targeting hardware vulnerabilities in the micro-architecture that do not require physical access. We present attacks based on micro-architectural hardware vulnerabilities and the side effects they produce in the system. In addition, we present security mechanisms that can be implemented to address some of these attacks. Most of the security mechanisms target a small set of attack vectors or a single specific attack vector. As many attack vectors exist, solutions must be found to protect against a wide variety of threats. This survey aims to inform designers about the side effects related to attacks and detection mechanisms that have been described in the literature. For this purpose, we present two tables listing and classifying the side effects and detection mechanisms based on the given criteria.

In this paper, we present a SCADA cybersecurity awareness and training program based on a Hands-On training using two twin cyber-ranges named WonderICS and G-ICS. These labs are built using a Hardware-In-the-Loop simulation system of the physical process developed by the two partners. The cyber-ranges allow replication of realistic Advanced Persistent Threat (APT) attacks and demonstration of known vulnerabilities, as they rely on real industrial control devices and softwares. In this work, we present both the demonstration scenarios used for awareness on WonderICS and the training programs developed for graduate students on G-ICS.

The increasing complexity of modern microprocessors created new attack areas. Attackers exploit these areas using Software Attacks Targeting Hardware Vulnerabilities (SATHV) such as Cache Side-Channel, Spectre, and Rowhammer attacks. These attacks target the microarchitecture to extract privileged information. As their target is the hardware, antivirus programs cannot detect them. But, they modify the normal behavior of the microarchitecture. Modern systems are equipped with hardware performance counters (HPCs), which measure events related to hardware components. Designers can take advantage of these counters to monitor and protect the system. In the literature, there exist many solutions that use HPCs to detect SATHV. But, due to the limited number of counters, proposed solutions only protect the microprocessor against a limited set of SATHV. In contrast, we propose MaDMAN, a Malware Detector, which gathers information from HPCs to detect a large set of SATHV. MaDMAN uses a Logistic Regression classifier. In our threat model, we include Cache Side-Channel, Rowhammer, and Spectre SATHV. Our detection mechanism succeeds to detect these attacks with 98.96% accuracy, 96.3% F-score, and 0% false positive rate. In addition, MaDMAN works in noisy environments and can detect successfully evasive malware.

International audience

International audience

In the domain of smart cards, secured devices must be protected against high level attack potential [1]. According to norms such as the Common Criteria [2], the vulnerability analysis must cover the current state-of-the-art in term of attacks. Nowadays, a very classical type of attack is fault injection, conducted by means of laser based techniques. We propose a global approach, called Lazart, to evaluate code robustness against fault injections targeting control flow modifications. The originality of Lazart is two folds. First, we encompass the evaluation process as a whole: starting from a fault model, we produce (or establish the absence of) attacks, taking into consideration software countermeasures. Furthermore, according to the near state-of-the-art, our methodology takes into account multiple transient fault injections and their combinatory. The proposed approach is supported by an effective tool suite based on the LLVM format [3] and the KLEE symbolic test generator [4].