In electrical distribution networks an adequate management is key for supporting the deployment of renewable generation sources and microgrids while extracting their maximum potential. Among the existing optimization approaches, stochastic and probabilistic methods are experiencing a growth in their use. However, one of the problems when applying these approaches is the complexity of creating and evaluating the quality of the required stochastic forecasts compared to deterministic forecasts. To mitigate this difficulty, this paper proposes a probabilistic forecasting framework that integrates model creation, their evaluation, and the selection of the best model for predicting. Additionally, two novel methods are proposed for creating scenario sets, and a new metric is defined for evaluating and selecting which model to use. The proposed framework is applied in a case study over a dataset of ten secondary distribution substations from a real distribution network located in Manzanilla (Spain), showing the effect of the selection criteria over the forecasting quality.Note to Practitioners—This article was motivated by the challenge of probabilistic forecasting inclusion in automatic management systems applied to power distribution networks and microgrids. Modern stochastic management optimization methods are fed with probabilistic forecasts, which offer richer information than classic deterministic forecasting. Therefore, the management systems should be able to automatically train a certain number of forecasting models (e.g., machine learning models), evaluate and compare them, and apply the best ones for obtaining the forecasts to feed the management optimization system. Considering the variety of models, techniques, probabilistic forecast types, and evaluation metrics, it can be unclear how to perform this process. For these reasons, this article proposes a probabilistic forecasting framework that integrates methods for the construction of diverse types of predictions (quantiles, intervals, and scenario sets), their evaluation, and the selection of the best model for performing each required prediction for feeding optimization systems. This framework could help to facilitate the implantation of modern stochastic optimization management systems for distribution networks and microgrids, as it simplifies the forecasting process.

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.

Machine learning has become one of the go-to methods for solving problems in the field of networking. This development is driven by data availability in large-scale networks and the commodification of machine learning frameworks. While this makes it easier for researchers to implement and deploy machine learning solutions on networks quickly, there are a number of vital factors to account for when using machine learning as an approach to a problem in networking and translate testing performance to real networks deployments successfully. This paper, rather than presenting a particular technical result, discusses the necessary considerations to obtain good results when using machine learning to analyze network-related data.

ABSTRACT Botnets are an important security problem on the Internet. They continuously evolve their structure, protocols and attacks. This survey analyzes and compares the most important efforts carried out in a network‐based detection area. It accomplishes four tasks: first, the comparison of previous surveys and the proposal of four new dimensions to analyze their classification schemes; second, a new classification and comparison of network‐based botnet detection proposals, which includes the definition of 20 desired properties of every botnet detection paper; third, an extensive comparison between the most representative detection proposals; and fourth, the description of the most important problems and highlights in the area. We conclude that the area has achieved great advances so far, but there are still many open problems. Copyright © 2013 John Wiley & Sons, Ltd.