Network intrusion detection systems are fundamental to the early detection of anomalous behaviour in networks. Modern versions of these tools take advantage of Machine Learning to process large amounts of data, identify patterns, and make predictions. Their development relies on the ability to access good historical network data. Therefore, the research community has been actively working on creating new datasets, and network traffic analysis tools are frequently used in this context. This study provides a comprehensive review of existing tools for network traffic analysis, highlighting their main advantages and drawbacks. A categorisation for these tools is introduced, as well as an overview of the dataset creation process by combining one or more of these categories. An updated analysis of existing datasets is also provided, along with details regarding their creation, highlighting the progression in dataset production. Finally, the impact of dataset features is discussed, underscoring their role in enhancing the effectiveness of network intrusion detection systems.

Access control systems in large organizations often struggle with managing complex policies and workloads. However, there is potential for deep learning models to address these challenges. This study delves into the suitability of various deep learning architectures for making real-time access control decisions. Six prominent Convolutional Neural Network (CNN) models (ResNet, DenseNet, Xception, Inception, AlexNet, VGG-16) are evaluated, and the impact of data augmentation using SMOTE on their performance is analyzed. The findings demonstrate that most deep learning models consistently deliver results in access control. ResNet outperforms other models, showing high accuracy across original and SMOTE-augmented datasets. Moreover, SMOTE generally enhances performance for most models, highlighting its potential for addressing data imbalance. These results indicate that deep learning shows promise for improving access control tasks.

Cybersecurity threats highlight the need for robust network intrusion detection systems to identify malicious behaviour. These systems rely heavily on large datasets to train machine learning models capable of detecting patterns and predicting threats. In the past two decades, researchers have produced a multitude of datasets, however, some widely utilised recent datasets generated with CICFlowMeter contain inaccuracies. These result in flow generation and feature extraction inconsistencies, leading to skewed results and reduced system effectiveness. Other tools in this context lack ease of use, customizable feature sets, and flow labelling options. In this work, we introduce HERA, a new open-source tool that generates flow files and labelled or unlabelled datasets with user-defined features. Validated and tested with the UNSW-NB15 dataset, HERA demonstrated accurate flow and label generation.