The Energy Sector is highly targeted by cyber threats because of its inherent value and profitability. Recent reported security incidents verify its key playing role in the entire economic and societal concurrent reality. This paper aims to assess the cyber-security culture status of European representatives in the entire electrical power supply chain during the coronavirus pandemic and the Ukrainian war. An evaluation campaign has been carefully designed and held from 3rd March 2022 to 18th March 2022. During that period, participants from different Electrical Power and Energy Systems (EPES) organizations participated in the campaign. Gathered results were analyzed and co-examined using different techniques revealing important findings regarding the cyber-security status and resilience of individuals and organizations in the European EPES sector.

Times of crisis have long been combined with an increase in cybercrime, exploiting the general instability; therefore, in such times, systems and infrastructures face greater exposure to vulnerabilities. On top of that, the COVID-19 crisis has increased our reliance on the internet, while working-from-home has been the daily reality for a large proportion of the population worldwide. Increased cyber-security awareness becomes a necessity for everyone, starting from a more knowledgeable audience; IT professionals, and software engineers. In this context, this paper aims to assess the cyber-security culture readiness of representatives studying or working within a European Polytechnique Academic Institution, during the COVID-19 crisis. Towards that end, a targeted evaluation campaign was launched for two weeks, from 28th February 2022 to 13th March 2022. The campaign consisted of four questionnaires of increased difficulty and a phishing quiz, all assessing the security culture of the participants against three dimensions; their security attitude, their competency, and their actual behavior. The campaign results have been thoroughly analyzed, and the findings were unforeseen in many cases, supporting the identification of security awareness weaknesses and assisting in drafting targeted, customized training programs.

The exponential growth of networking capabilities including the Internet of Things (IoT), has led to an outburst of cyberattacks. Many well-documented cyber-attacks have targeted critical energy infrastructures as well as any kind of cloud-based IT platforms. Early examination of critical systems’ vulnerabilities, as well as previous cyber-security incidents, are of utmost importance to prevent new ones. A thorough investigation to examine the context of the cyber-security breach can reveal facts about the source of the attack, the profile of the attacker, the resources, and the skills required and can further reveal mitigations for preventing the attack from re-appearing in the future. To safeguard critical energy infrastructures, many forensic approaches have been developed to collect, analyze, and digitalize evidence assisting in the in-depth investigation of an incident. However, up to now, the many open-source vulnerability data sources which have been developed to provide valuable information for a cyber-attack are yet to be employed to assist in forensic investigation. This paper introduces the Automated Forensic Tool, a platform that employs machine learning algorithms to combine different vulnerability data sources for facilitating the forensic procedure while minimizing the time and effort needed. A use case is also demonstrated that displays how the tool can be used towards assisting the forensic investigation of cyber-security incidents on an energy infrastructure, but the tool can also be applied to other critical energy and IT infrastructures with minor adaptations.

The increase of cyber-attacks raised security concerns for critical assets worldwide in the last decade. Leading to more efforts spent towards increasing the cyber security among companies and countries. For the sake of enhancing cyber security, representation and testing of attacks have prime importance in understanding system vulnerabilities. One of the available tools for simulating attacks on systems is the Meta Attack Language (MAL), which allows representing the effects of certain cyber-attacks. However, only understanding the component vulnerabilities is not enough in securing enterprise systems. Another important factor is the ‘human‘, which constitutes the biggest ‘insider threat‘. For this, Security Behavior Analysis (SBA) helps understanding which system components that might be directly affected by the ‘human‘. As such, in this work, the authors present an approach for integrating user actions, so called “security behavior”, by mapping SBA to a MAL-based language through MITRE ATT&CK techniques.

In order to get a deeper understanding of security breaches, their severity, impact and ways to mitigate them, many vulnerability databases and dictionaries have been developed. However, all that information on vulnerabilities is scattered all over the web, which makes locating and mitigating vulnerabilities an arduous task. This paper introduces the Vulnerabilities Manager, a tool that automates the process of linking information from well-known external vulnerability data sources. Its goal is to present an enriched vulnerability report to its final users, assisting them in pinpointing software and hardware assets’ defects, categorizing and prioritizing them, thus, contributing to the cyber defense against potential security breaches and adversary actions. To achieve this, the Vulnerabilities Manager exploits current state of the art machine learning and artificial intelligence techniques. The tool may also be enriched with forensic capabilities, detecting cyber threats, unveiling information about the nature of the attacker, and proposing mitigations against them in real-time.

Although customers become more and more vocal in expressing their experiences, demands and needs in various social networks, companies of any size typically fail to effectively gain insights from such social data and to eventually catch the market realm. This paper introduces the Anlzer analytics engine that aims at leveraging the "social" data deluge to help companies in their quest for deeper understanding of their products' perceptions as well as of the emerging trends in order to early embed them into their product design phase. The proposed approach brings together polarity detection and trend analysis techniques as presented in the architecture and demonstrated through a simple walkthrough in the Anlzer solution. The Anlzer implementation is by design domain-independent and is being tested in the furniture domain at the moment, yet it brings significant added value to software design and development, as well, through its experimentation playground that may provide indirect feedback on future software features while monitoring the reactions to existing releases.