A Survey of Network-based Intrusion Detection Data Sets

TL;DR: A novel CIDS dataset formation framework that can handle log files from a variety of operating systems and align log entities with network flows is presented and a transformer-based deep learning model named CIDS-Net is proposed that can take network flow and host features as inputs and outperform baseline models that rely on network flow features only.

TL;DR: In this article , the authors investigated the influence of feature selection IDS for UAV networks and proposed the IGC-MLP algorithm, which utilized feature selection algorithms to determine the optimal features and applied them to the multilayer perception classification model.

TL;DR: This research concentrates on modelling an efficient feature selection approach using correlation mapping, and shows that the anticipated model improves prediction outcomes more effectively than different processes with better trade-offs.

TL;DR: The FAIR Data Principles as mentioned in this paper are a set of data reuse principles that focus on enhancing the ability of machines to automatically find and use the data, in addition to supporting its reuse by individuals.

TL;DR: This whitepaper proposes OpenFlow: a way for researchers to run experimental protocols in the networks they use every day, based on an Ethernet switch, with an internal flow-table, and a standardized interface to add and remove flow entries.

TL;DR: A critical review of the nature of the problem, the state-of-the-art technologies, and the current assessment metrics used to evaluate learning performance under the imbalanced learning scenario is provided.

TL;DR: A new data set is proposed, NSL-KDD, which consists of selected records of the complete KDD data set and does not suffer from any of mentioned shortcomings.