Biblio

SCADA Systems can be seen as a fundamental component in Critical Infrastructures, having an impact in the overall performance of other Critical Infrastructures interconnected. Currently, these systems include in their network designs different types of Information and Communication Technology systems (such as the Internet and wireless technologies), not only to modernize operational processes but also to ensure automation and real-time control. Nonetheless, the use of these new technologies will bring new security challenges, which will have a significant impact on both the business process and home users. Therefore, the main purpose of this Chapter is to address these issues and to analyze the interdependencies of Process Control Systems with ICT systems, to discuss some security aspects and to offer some possible solutions and recommendations.

SCADA systems represent a challenging scenario where the management of critical alarms is crucial. Their response to these alarms should be efficient and fast in order to mitigate or contain undesired effects. This work presents a mechanism, the Adaptive Assignment Manager (AAM) that will aid to react to incidences in a more efficient way by dynamically assigning alarms to the most suitable human operator. The mechanism uses various inputs for identifying the operators such as their availability, workload and reputation. In fact, we also define a reputation component that stores the reputation of the human operators and uses feedback from past experiences.

SCADA systems represent a challenging scenario where the management of critical alarms is crucial. Their response to these alarms should be efficient and fast in order to mitigate or contain undesired effects. This work presents a mechanism, the Adaptive Assignment Manager (AAM) that will aid to react to incidences in a more efficient way by dynamically assigning alarms to the most suitable human operator. The mechanism uses various inputs for identifying the operators such as their availability, workload and reputation. In fact, we also define a reputation component that stores the reputation of the human operators and uses feedback from past experiences.

Cyber-attacks against Industrial Control Systems (ICS) can lead to catastrophic events which can be prevented by the use of security measures such as the Intrusion Prevention Systems (IPS). In this work we experimentally demonstrate how to exploit the configuration vulnerabilities of SNORT one of the most adopted IPSs to significantly degrade the effectiveness of the IPS and consequently allowing successful cyber-attacks. We illustrate how to design a batch script able to retrieve and modify the configuration files of SNORT in order to disable its ability to detect and block Denial of Service (DoS) and ARP poisoning-based Man-In-The-Middle (MITM) attacks against a Programmable Logic Controller (PLC) in an ICS network. Experimental tests performed on a water distribution testbed show that, despite the presence of IPS, the DoS and ARP spoofed packets reach the destination causing respectively the disconnection of the PLC from the ICS network and the modification of packets payload.

Anomaly-based detection applied in strongly interdependent systems, like Smart Grids, has become one of the most challenging research areas in recent years. Early detection of anomalies so as to detect and prevent unexpected faults or stealthy threats is attracting a great deal of attention from the scientific community because it offers potential solutions for context-awareness. These solutions can also help explain the conditions leading up to a given situation and help determine the degree of its severity. However, not all the existing approaches within the literature are equally effective in covering the needs of a particular scenario. It is necessary to explore the control requirements of the domains that comprise a Smart Grid, identify, and even select, those approaches according to these requirements and the intrinsic conditions related to the application context, such as technological heterogeneity and complexity. Therefore, this paper analyses the functional features of existing anomaly-based approaches so as to adapt them, according to the aforementioned conditions. The result of this investigation is a guideline for the construction of preventive solutions that will help improve the context-awareness in the control of Smart Grid domains in the near future.

In recent years, the Smart Grid has increasingly integrated cutting-edge technologies that generate several benefits for all the stakeholders involved, such as a more accurate billing system and enhanced Demand Response procedures. However, this modernization also brings with it diverse cyber security and privacy issues, which sets the necessity for developing a security platform specifically tailored to this scenario. In this paper, we present SealedGRID, which proposes a flexible architecture that provides security services at all levels by implementing Trusted Execution Environments on their devices, together with advanced authentication and authorization mechanisms, as well as privacy preserving techniques. These technologies are presented in depth and a final security analysis is conducted, which highlights the contributions of this project.

Most of energy control or SCADA (Supervisory Control and Data Acquisition) systems are very dependent on advanced technologies and on traditional security mechanisms for protecting the a system against anomalous events. Security mechanisms are not enough to be used in critical systems, since they can only detect anomalous events occurring at a certain moment in time. For this reason it becomes of paramount importance the usage of intelligent systems with capability for preventing anomalous situations and reacting against them on time. This type of systems are, for example, Early Warning Systems (EWS). In this paper, we propose an EWS based on Wireless Sensor Networks (WSNs) (under the ISA100.11a standard) and reputation for controling the network behaviour. The WSN are organized into clusters where a Cluster Head (CH) is designated. This CH will contain a Reputation Manager Module. The usability of this approach is also analyzed considering a Smart Grid scenario.} keywords = {Critical Information Infrastructures, Sensor Networks, Early Warning Systems, Reputation, SCADA Systems, Smart Grid.

En el momento que se introduce en el mercado nuevas tecnologías basadas en entornos distribuidos comienzan a surgir en paralelo nuevos problemas de seguridad en los sistemas SCADA (Supervisory Control and Data Acquisition), los cuales monitorizan y gestionan otras infraestructuras de gran complejidad y escala. Un fallo o una interrupción en uno de sus componentes podría suponer un impacto negativo sobre la funcionalidad de otras infraestructuras, por lo que se hace necesario realizar frecuentes análisis de seguridad para así mantener actualizado el conocimiento y proveer recomendaciones y/o soluciones para mitigar o evitar futuras ocurrencias, garantizando una gestión de red fiable y siempre disponible.

When a Supervisory Control and Data Acquisition (SCADA) system monitors and manages other complex infrastructures through the use of distributed technologies, it becomes a critical infrastructure by itself: A failure or disruption in any of its components could implicate a serious impact on the performance of the other infrastructures. The connection with other systems makes a SCADA system more vulnerable against attacks, generating new security problems. As a result, it is essential to perform diverse security analysis frequently in order to keep an updated knowledge and to provide recommendations and/or solutions to mitigate or avoid anomalous events. This will facilitate the existence of a suitable, reliable, and available control network.

The class of Trustworthy Autonomous Systems (TAS) includes cyber-physical systems leveraging on self-x technologies that make them capable to learn, adapt to changes, and reason under uncertainties in possibly critical applications and evolving environments. In the last decade, there has been a growing interest in enabling artificial intelligence technologies, such as advanced machine learning, new threats, such as adversarial attacks, and certification challenges, due to the lack of sufficient explainability. However, in order to be trustworthy, those systems also need to be dependable, secure, and resilient according to well-established taxonomies, methodologies, and tools. Therefore, several aspects need to be addressed for TAS, ranging from proper taxonomic classification to the identification of research opportunities and challenges. Given such a context, in this paper address relevant taxonomies and research perspectives in the field of TAS. We start from basic definitions and move towards future perspectives, regulations, and emerging technologies supporting development and operation of TAS.