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Mathematical modeling and data-driven methodologies are frequently required to optimize industrial processes in the context of Cyber-Physical Systems (CPS). This paper introduces the PipeGraph software library, an open-source python toolbox for easing the creation of machine learning models by using Directed Acyclic Graph (DAG)-like implementations that can be used for CPS. scikit-learn's Pipeline is a very useful tool to bind a sequence of transformers and a final estimator in a single unit capable of working itself as an estimator. It sequentially assembles several steps that can be cross-validated together while setting different parameters. Steps encapsulation secures the experiment from data leakage during the training phase. The scientific goal of PipeGraph is to extend the concept of Pipeline by using a graph structure that can handle scikit-learn's objects in DAG layouts. It allows performing diverse operations, instead of only transformations, following the topological ordering of the steps in the graph; it provides access to all the data generated along the intermediate steps; and it is compatible with GridSearchCV function to tune the hyperparameters of the steps. It is also not limited to (X , y) entries. Moreover, it has been proposed as part of the scikit-learn-contrib supported project, and is fully compatible with scikit-learn. Documentation and unitary tests are publicly available together with the source code. Two case studies are analyzed in which PipeGraph proves to be essential in improving CPS modeling and optimization: the first is about the optimization of a heat exchange management system, and the second deals with the detection of anomalies in manufacturing processes. [ABSTRACT FROM AUTHOR]

Advanced persistent threats (APTs) are a growing concern in cybersecurity. Many companies and governments have reported incidents related to these threats. Throughout the life cycle of an APT, one of the most commonly used techniques for gaining access is network attacks. Tools based on machine learning are effective in detecting these attacks. However, researchers usually have problems with finding suitable datasets for fitting their models. The problem is even harder when flow data are required. In this paper, we describe a framework to gather flow datasets using a NetFlow sensor. We also present the Docker-based framework for gathering netflow data (DOROTHEA), a Docker-based solution implementing the above framework. This tool aims to easily generate taggable network traffic to build suitable datasets for fitting classification models. In order to demonstrate that datasets gathered with DOROTHEA can be used for fitting classification models for malicious-traffic detection, several models were built using the model evaluator (MoEv), a general-purpose tool for training machine-learning algorithms. After carrying out the experiments, four models obtained detection rates higher than 93%, thus demonstrating the validity of the datasets gathered with the tool. [ABSTRACT FROM AUTHOR]

The assembly and maintenance of electrified railway systems is of vital importance for its correct operation. Contact wires are critical elements since the correct collection of energy from trains through pantographs depends on them. Periodical inspection of the state of these installations is essential. This task traditionally implies a heavy manual workload subject to errors. A new system that allows one to check the state (height and stagger) of contact and messenger wires is presented on this article blueA new method based on seven steps for identifying the contact wires and measuring their height and stagger from point clouds recorded by means of a LiDAR system is presented. This system can be used both in assembly and maintenance phases, as well as afterwards, allowing the analysis of point clouds previously recorded. The new method was evaluated in both test bench and real environments against the commonly used measurement method. Results with the comparison between this new system and the commonly used measurement method in both test bench and real railway environments are presented. Results of this comparison show differences of less than a centimetre on average and the amount of time spent for the measuring phase is significantly decreased and not prone to human errors. [ABSTRACT FROM AUTHOR]