Ing. Ondřej Lukáš

Deciding which XAI technique is best depends not only on the domain, but also on the given task, the dataset used, the model being explained, and the target goal of that model. We argue that the evaluation of XAI methods has not been thoroughly analyzed in the network security domain, which presents a unique type of challenge. While there are XAI methods applied in network security there is still a large gap between the needs of security stakeholders and the selection of the optimal method. We propose to approach the problem by first defining the stack-holders in security and their prototypical tasks. Each task defines inputs and specific needs for explanations. Based on these explanation needs (e.g. understanding the performance, or stealing a model), we created five XAI evaluation techniques that are used to compare and select which XAI method is best for each task (dataset, model, and goal). Our proposed approach was evaluated by running experiments for different security stakehol ders, machine learning models, and XAI methods. Results were compared with the AutoXAI technique and random selection. Results show that our proposal to evaluate and select XAI methods for network security is well-grounded and that it can help AI security practitioners find better explanations for their given tasks.

Large Language Models (LLMs) have gained widespread popularity across diverse domains involving text generation, summarization, and various natural language processing tasks. Despite their inherent limitations, LLM-based designs have shown promising capabilities in planning and navigating open-world scenarios. This paper introduces a novel application of pre-trained LLMs as agents within cybersecurity network environments, focusing on their utility for sequential decision-making processes. We present an approach wherein pre-trained LLMs are leveraged as attacking agents in two reinforcement learning environments. Our proposed agents demonstrate similar or better performance against state-of-the-art agents trained for thousands of episodes in most scenarios and configurations. In addition, the best LLM agents perform similarly to human testers of the environment without any additional training process. This design highlights the potential of LLMs to address complex decision-making tasks within cybersecurity efficiently. Furthermore, we introduce a new network security environment named NetSecGame. The environment is designed to support complex multi-agent scenarios within the network security domain eventually. The proposed environment mimics real network attacks and is designed to be highly modular and adaptable for various scenarios.

Active Directory (AD) is a crucial element of large organizations, given its central role in managing access to resources. Since AD is used by all users in the organization, it is hard to detect attackers. We propose to generate and place fake users (honeyusers) in AD structures to help detect attacks. However, not any honeyuser will attract attackers. Our method generates honeyusers with a Variational Autoencoder that enriches the AD structure with well-positioned honeyusers. It first learns the embeddings of the original nodes and edges in the AD, then it uses a modified Bidirectional DAG-RNN to encode the parameters of the probability distribution of the latent space of node representations. Finally, it samples nodes from this distribution and uses an MLP to decide where the nodes are connected. The model was evaluated by the similarity of the generated AD with the original, by the positions of the new nodes, by the similarity with GraphRNN and finally by making real intruders attack the generated AD structure to see if they select the honeyusers. Results show that our machine learning model is good enough to generate well-placed honeyusers for existing AD structures so that intruders are lured into them.