Jose Luis Flores

Ángel Longueira-Romero, Rosa Iglesias, Jose Luis Flores et al.

Industrial components are of high importance because they control critical infrastructures that form the lifeline of modern societies. However, the rapid evolution of industrial components, together with the new paradigm of Industry 4.0, and the new connectivity features that will be introduced by the 5G technology, all increase the likelihood of security incidents. These incidents are caused by the vulnerabilities present in these devices. In addition, although international standards define tasks to assess vulnerabilities, they do not specify any particular method. Having a secure design is important, but is also complex, costly, and an extra factor to manage during the lifespan of the device. This paper presents a model to analyze the known vulnerabilities of industrial components over time. The proposed model is based on two main elements: a directed graph representation of the internal structure of the component, and a set of quantitative metrics that are based on international security standards; such as, the Common Vulnerability Scoring System (CVSS). This model is applied throughout the entire lifespan of a device to track vulnerabilities, identify new requirements, root causes, and test cases. The proposed model also helps to prioritize patching activities. To test its potential, the proposed model is applied to the OpenPLC project. The results show that most of the root causes of these vulnerabilities are related to memory buffer operations and are concentrated in the \textit{libssl} library. Consequently, new requirements and test cases were generated from the obtained data.

Unai Rioja, Lejla Batina, Igor Armendariz et al.

Evaluating side-channel analysis (SCA) security is a complex process, involving applying several techniques whose success depends on human engineering. Therefore, it is crucial to avoid a false sense of confidence provided by non-optimal (failing) attacks. Different alternatives have emerged lately trying to mitigate human dependency, among which deep learning (DL) attacks are the most studied today. DL promise to simplify the procedure by e.g. evading the need for point of interest selection or the capability of bypassing noise and desynchronization, among other shortcuts. However, including DL in the equation comes at a price, since working with neural networks is not straightforward in this context. Recently, an alternative has appeared with the potential to mitigate this dependence without adding extra complexity: Estimation of Distribution Algorithm-based SCA. In this paper, we compare these two relevant methods, supporting our findings by experiments on various datasets.

Unai Rioja, Lejla Batina, Jose Luis Flores et al.

Due to the constant increase and versatility of IoT devices that should keep sensitive information private, Side-Channel Analysis (SCA) attacks on embedded devices are gaining visibility in the industrial field. The integration and validation of countermeasures against SCA can be an expensive and cumbersome process, especially for the less experienced ones, and current certification procedures require to attack the devices under test using multiple SCA techniques and attack vectors, often implying a high degree of complexity. The goal of this paper is to ease one of the most crucial and tedious steps of profiling attacks i.e. the points of interest (POI) selection and hence assist the SCA evaluation process. To this end, we introduce the usage of Estimation of Distribution Algorithms (EDAs) in the SCA field in order to automatically tune the point of interest selection. We showcase our approach on several experimental use cases, including attacks on unprotected and protected AES implementations over distinct copies of the same device, dismissing in this way the portability issue.