Towards a Reliable Comparison and Evaluation of Network Intrusion Detection Systems Based on Machine Learning Approaches

Presently, we are living in a hyper-connected world where millions of heterogeneous devices are continuously sharing information in different application contexts for wellness, improving communications, digital businesses, etc. However, the bigger the number of devices and connections are, the higher the risk of security threats in this scenario. To counteract against malicious behaviours and preserve essential security services, Network Intrusion Detection Systems (NIDSs) are the most widely used defence line in communications networks. Nevertheless, there is no standard methodology to evaluate and fairly compare NIDSs. Most of the proposals elude mentioning crucial steps regarding NIDSs validation that make their comparison hard or even impossible. This work firstly includes a comprehensive study of recent NIDSs based on machine learning approaches, concluding that almost all of them do not accomplish with what authors of this paper consider mandatory steps for a reliable comparison and evaluation of NIDSs. Secondly, a structured methodology is proposed and assessed on the UGR'16 dataset to test its suitability for addressing network attack detection problems. The guideline and steps recommended will definitively help the research community to fairly assess NIDSs, although the definitive framework is not a trivial task and, therefore, some extra effort should still be made to improve its understandability and usability further

A Novel CAD Tool for Electric Educational Diagrams

Computer-aided design (CAD) is a technological revolution, very powerful and with large applicability to problem solving. It is essential in many different disciplines ranging from architecture to education, medicine, physics, or gaming. In this work, we propose a novel CAD tool, called CADDi, to assist in the design of electric diagrams in the educational context. We are applying the theory of formal languages to create WDLang, an easy-to-use, highly expressive, unequivocal, and correct programming language for designing electric circuits. This programming language is the cornerstone of CADDi, which automatically generates the equivalent ladder diagram (explains the circuit operation) to the programmed circuit, offering additional features that allow analysis of its functionality in an interactive way. It also offers a graphical interface to directly design ladder diagrams, or to modify the automatically generated ones. The existing electrical CAD tools are either very simple, e.g., for creating good-looking diagrams with no functionality, or too complex, for professional systems design. CADDi is extremely useful for learning purposes. It assists users on how to generate ladder diagrams, and on understanding the behavior of electrical circuits. Additionally, it serves as an assessment tool for self-evaluation in the translation from wiring diagrams to ladder ones. In order to make CADDi highly accessible, it was implemented as a web page

Improving the Reliability of Network Intrusion Detection Systems Through Dataset Integration

This work presents Reliable-NIDS (R-NIDS), a novel methodology for Machine Learning (ML) based Network Intrusion Detection Systems (NIDSs) that allows ML models to work on integrated datasets, empowering the learning process with diverse information from different datasets. We also propose a new dataset, called UNK22. It is built from three of the most well-known network datasets (UGR'16, USNW-NB15 and NLS-KDD), each one gathered from its own network environment, with different features and classes, by using a data aggregation approach present in R-NIDS. Therefore, R-NIDS targets the design of more robust models that generalize better than traditional approaches. Following R-NIDS, in this work we propose to build two well-known ML models for reliable predictions thanks to the meaningful information integrated in UNK22. The results show how these models benefit from the proposed approach, being able to generalize better when using UNK22 in the training process, in comparison to individually using the datasets composing it. Furthermore, these results are carefully analyzed with statistical tools that provide high confidence on our conclusions. Finally, the proposed solution is feasible to be deployed in network production environments, not usually taken into account in the literature.16 página

Optimal battery management strategies for plug-in electric hybrid buses on routes including green corridors

Public transport is a cornerstone in the transition towards sustainable cities. Moreover, greenhouse gas emissions can be further reduced through powertrain electrification. In this context, plug-in electric hybrid buses emerge as a suitable and flexible solution. They can switch between an electric motor and a combustion engine during operation. An optimal electric drive assignment strategy allows achieving a high electric range and reduced tailpipe emissions. In this work, we look for optimal strategies for maximizing the distance traversed in electric mode and minimizing the total emissions, for real routes including green corridors where the combustion engine cannot be used. Contrary to existing works, this approach does not only focus on the improvement of the bus performance in terms of energy consumption, but also on the environmental benefits and livability of cities. This challenge is solved using two multi-objective state-of-the-art evolutionary algorithms, and a novel heuristic, GreenK. Two real- world scenarios are analyzed, namely bus routes M6 in Badalona, and 18 in Grudziadz. Results show a significant reduction in emissions of up to 21% with respect to the strategy found by GreenK, meaning 24 kg less pollutants emitted daily and over 22.5% electric range increase, compared to the currently deployed solutio