Marko Schuba’s research while affiliated with FH Aachen and other places

The increasing complexity of information security threats and ever more stringent legal requirements mean that more and more organizations are setting themselves the goal of implementing an effective and efficient information security management system (ISMS). This paper examines the ways in which artificial intelligence (AI) in the form of a chatbot can support the development and operation of an ISMS. In particular, it evaluates how a chatbot can be integrated into standard setup and operating processes within an ISMS. In addition, various possible applications are shown and advantages, disadvantages and limitations are discussed. It turns out that the use of a chatbot as a supporting tool has many advantages and, in the hands of specialist personnel, offers a useful addition to established methods. Consequently, chatbots open up the possibility for organizations to optimize their organizational and operational processes.

Phishing remains one of the most common and effective forms of social engineering, with cybercriminals constantly refining their tactics to exploit human vulnerabilities. The sheer volume of phishing attacks is staggering: almost 1.2% of all emails sent are malicious. This equates to around 3.4 billion phishing emails per day. The effectiveness of phishing attacks is also underlined by numerous studies. Phishing is identified as the leading initial attack vector, responsible for 41% of security incidents. This means that practically every company is threatened by phishing attacks.In parallel, there have been rapid advances in the field of artificial intelligence (AI) in recent years, giving the general public access to powerful tools that can handle complex tasks with ease. However, alongside these benefits, the potential for abuse has also become a major concern. The integration of AI into social engineering attacks has significantly increased the opportunities for cybercriminals. Research has shown that AI-generated phishing emails are difficult for humans to distinguish from real messages. According to one study, phishing emails written by AI were opened by 78% of recipients, with 21% clicking on malicious content such as links or attachments. Although the click-through rate is still lower compared to human-crafted emails, generative AI tools (GenAI) can help cybercriminals compose phishing emails at least 40% faster, which can lead to a significant increase in phishing success rates. The increasing potential to use public AI tools for abusive purposes has also been recognized by the developers of AI models. Thus, publicly available AI tools often have built-in mechanisms to detect and prevent misuse. This paper examines the potential for misuse of publicly available AI in the context of phishing attacks, focusing on the content generation phase. In particular, the study examines the effectiveness of existing abuse prevention mechanisms implemented by AI platforms like fine-tuning, filters, rejection sampling, system prompts and dataset filtering. To this end, it is explored how prompts to the AI need to be altered for circumventing the misuse preventing mechanisms. While in some cases the simple request to write a phishing email succeeds, other AI tools implement more sophisticated mechanisms. In the end, however, all prevention safeguards could be circumvented. The findings highlight the significant threat posed by AI-powered social engineering attacks and emphasize the urgent need for robust defense in depth strategies against phishing attacks and increased awareness to mitigate the risks in the evolving digital landscape.In addition, the paper demonstrates that the quality of the AI tool varies in terms of the phishing emails generated. To this end, the phishing emails generated by circumventing the protection mechanisms of the AI are (subjectively) compared and evaluated by the authors. The preliminary conclusion is that automatically generated phishing emails of some public AI tools can certainly match that of manually generated emails. While the objective confirmation of this hypothesis requires further study even the subjective quality of the generated phishing emails shows the dimension of the problem.

Industrial automation and control systems (IACS) operate in complex and increasingly networked environments of industrial plants. Due to the increasing number of cyberattacks, these systems are also exposed to the growing threat of being attacked. IACS are often found in critical infrastructure such as power supply or water treatment plants, as well as in industry, so their compromise can result in devastating consequences. To prevent this, the IEC-62443 series of standards was developed to address the cybersecurity of IACS. In order to achieve cybersecurity in accordance with the IEC-62443 standard, the human factor plays a major role, as it is humans that need to implement and manage the cybersecurity controls. To help those users to get started and gain a basic understanding of important IEC-62443 concepts such as zones and conduits, defense in depth, and security levels, this paper defines an experience-based practical approach to train users w.r.t. application and implementation of the standard.

The work of an digital forensics expert is far more extensive and varied today than it was just a few years ago. Especially after hacking attacks on organizations, experts in DFIR (Digital Forensics and Incident Response) come into play. In this paper, we present a learning platform that enables people to learn DFIR from scratch. To achieve this goal, the content of the learning platform was defined, evaluated and prepared with the help of experts from industry and government. For this purpose, expert interviews were conducted, which were subsequently evaluated. The results of these interviews were incorporated into initial scenarios that were implemented in individual modules on the learning platform Ilias, with a distinction being made between the basics and the main DFIR part. In the basic part, an introduction to IT forensics is offered, which is supplemented by further technical modules. This includes training in the use of the Linux operating system, which is frequently used in digital forensics, as well as the acquisition and analysis of RAM iand hard disk images. In the main part, the focus is to apply the learnings from the basic sections and to enhance them with incident related knowledge for DFIR projects, in which digital forensics experts gather and analyse evidence on various systems of the attacked organizations by searching and gathering so-called IoCs (Indicators of Compromise) from log files and other sources. Once the analysis part is complete, and all evidence has been collected, cleanup, recovery and restart of systems may take place, which is handled in the last section of the main training module.