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 efficiently address complex decision-making tasks within cybersecurity.Furthermore, we introduce a new network security environment named NetSecGame.The environment is designed to eventually support complex multi-agent scenarios within the network security domain.The proposed environment mimics real network attacks and is designed to be highly modular and adaptable for various scenarios.

The ongoing rise in cyberattacks and the lack of skilled professionals in the cybersecurity domain to combat these attacks show the need for automated tools capable of detecting an attack with good performance.Attackers disguise their actions and launch attacks that consist of multiple actions, which are difficult to detect.Therefore, improving defensive tools requires their calibration against a well-trained attacker.In this work, we propose a model of an attacking agent and environment and evaluate its performance using basic Q-Learning, Naive Q-learning, and DoubleQ-Learning, all of which are variants of Q-Learning.The attacking agent is trained with the goal of exfiltrating data whereby all the hosts in the network have a non-zero detection probability.Results show that the DoubleQ-Learning agent has the best overall performance rate by successfully achieving the goal in 70% of the interactions.

Jet is an open source, high performance, distributed stream processor built at Hazelcast during the last five years. Jet was engineered with millisecond latency on the 99.99th percentile as its primary design goal. Originally Jet's purpose was to be an execution engine that performs complex business logic on top of streams generated by Hazelcast's In-memory Data Grid (IMDG): a set of in-memory, partitioned and replicated data structures. With time, Jet evolved into a full-fledged, scale-out stream processor that can handle out-of-order streams and provide exactly-once processing guarantees. Jet's end-to-end latency lies in the order of milliseconds, and its throughput in the order of millions of events per CPU-core. This paper presents the main design decisions we made in order to maximize the performance per CPU-core, alongside lessons learned, and an empirical performance evaluation.