Keynote Speakers

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Adversary Emulation in the Age of Generative AI

Cybersecurity threat actors have been evolving into complex organizations, with technical and financial means to deliver powerful attacks, with significant impact on economy and infrastructures. These threat actors are also looking with high interest at the recent evolution of Generative AI for malicious purposes. Generative AI is also a valuable opportunity to enhance cybersecurity. This presentation will look at emerging applications of Generative AI for Adversary Emulation, that is, the emulation of attack techniques for assessment purposes. In particular, we will discuss the role of Large Language Models (LLMs) at supporting cybersecurity analysts, by automatically generating malicious code to mimic threat actors.

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Crossing the Border: How Adversarial Attacks Can Compromise Your Artificial Intelligence Model

You’re probably familiar with terms like Artificial Intelligence, Deep Learning, and Neural Networks, technologies driving disruptive innovations across various sectors today. However, what may not be as well known are the implications these technologies can have for the cybersecurity of your applications.

One of the main challenges faced is Adversarial Attacks, a form of attack targeting artificial intelligence models. They exploit vulnerabilities in AI systems, often using methods that evade human perception but can cause considerable damage.

A classic example occurs in image recognition. Here, an adversary can introduce tiny, nearly imperceptible alterations to an image, causing the AI model to misclassify it. For instance, an image of a cat could be manipulated in such a way that the model identifies it as a dog, even though to a human observer the image appears identical.

In this talk, we will provide a brief introduction to adversarial attacks, along with examples and suggestions to help mitigate such threats.

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Performability Assessment: Methods and Tools for System Design and Tuning

Evaluating Performability is crucial for systems experiencing degraded performance due to failures and repair activities. We will begin by discussing the foundational concepts of Performability, including definitions of key performance metrics such as utilization and response time and dependability attributes such as availability, reliability, safety, security, confidentiality, integrity, and maintainability. This talk aims to provide a broad understanding of the significance, methods, and benefits of Performability evaluation, considering the complexity and representativeness of the models.

Several evaluation strategies will be examined, including analytical solutions, numerical-based methods, and simulations. We will discuss the complexity and modeling power of techniques such as reliability block diagrams (RBD), fault trees (FT), Markov chains (DTMC and CTMC), and stochastic Petri nets (SPN). Additionally, the importance of hierarchical and heterogeneous modeling methodologies, sensitivity analysis, phase-type evaluation methods, and the development of user-friendly tools will be highlighted.

Furthermore, we will introduce the Mercury tool, which supports Performability evaluation using models like SPN, CTMC, DTMC, RBD, and FT.

Through this presentation, we highlight the critical role of Performability evaluation in ensuring systems meet their performance and dependability requirements, thereby contributing to developing more robust and reliable system designs.

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Leveraging LLMs for Secure and Trustworthy Software: Insights and Future Perspectives

Large Language Models (LLMs) are transforming software engineering, offering new possibilities for developing secure and trustworthy software. This keynote will explore the integration of LLMs into software development workflows, particularly their role in code generation. Supported by empirical evidence, we will discuss the capabilities of LLMs in vulnerability detection and mitigation, and delve into the importance of assessing the trustworthiness of code, including the role of LLMs in verifying code quality and adherence to best practices. We will conclude with a discussion on future directions, outlining emerging opportunities for LLMs in software engineering.

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Keynote title: TBA