Globally, cyberattacks are growing and mutating each month. Intelligent Intrusion Network Detection Systems are developed to analyze and detect anomalous traffic to face these threats. A way to address this is by using network flows, an aggregated version of communications between devices. Network Flow datasets are used to train Artificial Intelligence (AI) models to classify specific attacks. Training these models requires threat samples usually generated synthetically in labs as capturing them on operational network is a challenging task. As threats are fast-evolving, new network flows are continuously developed and shared. However, using old datasets is still a popular procedure when testing models, hindering a more comprehensive characterization of the advantages and opportunities of recent solutions on new attacks. Moreover, a standardized benchmark is missing rendering a poor comparison between the models produced by algorithms. To address these gaps, we present a benchmark with fourteen recent and preprocessed datasets and study seven categories of algorithms for Network Intrusion Detection based on Network Flows. We provide a centralized source of pre-processed datasets to researchers for easy download. All dataset are also provided with a train, validation and test split to allow a straightforward and fair comparison between existing and new solutions. We selected open state-of-the-art publicly available algorithms, representatives of diverse approaches. We carried out an experimental comparison using the Macro F1 score of these algorithms. Our results highlight each model operation on dataset scenarios and provide guidance on competitive solutions. Finally, we discuss the main characteristics of the models and benchmarks, focusing on practical implications and recommendations for practitioners and researchers.

Many of the works conducted on algorithm selection strategies—methods that choose a suitable solving method for a particular problem—start from scratch since only a few investigations on reusable components of such methods are found in the literature. Additionally, researchers might unintentionally omit some implementation details when documenting the algorithm selection strategy. This makes it difficult for others to reproduce the behavior obtained by such an approach. To address these problems, we propose to rely on existing techniques from the Machine Learning realm to speed-up the generation of algorithm selection strategies while improving the modularity and reproducibility of the research. The proposed solution model is implemented on a domain-independent Machine Learning module that executes the core mechanism of the algorithm selection task. The algorithm selection strategies produced in this work are implemented and tested rapidly compared against the time it would take to build a similar approach from scratch. We produce four novel algorithm selectors based on Machine Learning for constraint satisfaction problems to verify our approach. Our data suggest that these algorithms outperform the best performing algorithm on a set of test instances. For example, the algorithm selectors Multiclass Neural Network (MNN) and Multiclass Logistic Regression (MLR), powered by a neural network and linear regression, respectively, reduced the search cost (in terms of consistency checks) of the best performing heuristic (KAPPA), on average, by 49% for the instances considered for this work.

Through various imaging techniques, efforts have been made to diagnose and predict the course of different neurodegenerative diseases, which significantly increase their morbidity and mortality in the human population over the years, especially proteinopathies where the Tau polypeptide is a key participant in molecular pathogenesis. However, this standard approach to explore the phenomenon of neurodegener-ation has not been directed at understanding the molecular mechanism that causes the aberrant polymeric and fibrillar behavior of Tau protein in the neurofibrillary tangles that replace neuronal populations comprising the hippocampal formation and cortical regions of clinical cases with various proteinopathies. Our main objective was to implement a novel approach based on computational seg-mentation protocols in triple-labeled immunofluorescence imaging with different biomarkers based on pathological post-translational modifications undergone by Tau in brains of patients with tauopathies, implementing an adaptation of the U-Net neural network architecture. Importantly, we used the resulting segmentation masks and significantly quantified the individual and combined fluorescent signals of the different molecular changes Tau underwent in neurofibrillary tangles as individual study structures. Then, we separated them from the rest of the quadrant, detecting unconventional interaction signals between the different biomarkers. Our algorithm provides information that will be fundamental to understand the pathogenesis of dementias with another computational analysis approach in subsequent studies.