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Introducing UWF-ZeekData22: A Comprehensive Network Traffic Dataset Based on the MITRE ATT&CK Framework

https://doi.org/10.3390/data8010018
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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1007/978-3-030-25109-3_9openalex-snapshot
A Detailed Analysis of the CICIDS2017 Data Set
inferredcites →https://doi.org/10.1016/j.cose.2017.11.004openalex-snapshot
UGR‘16: A new dataset for the evaluation of cyclostationarity-based network IDSs
inferredcites →https://doi.org/10.1016/j.dib.2020.105400openalex-snapshot
UMUDGA: A dataset for profiling algorithmically generated domain names in botnet detection
inferredcites →https://doi.org/10.1016/j.jnca.2015.11.016openalex-snapshot
A survey of network anomaly detection techniques
inferredcites →https://doi.org/10.1109/ahici.2011.6113948openalex-snapshot
SSENet-2011: A Network Intrusion Detection System dataset and its comparison with KDD CUP 99 dataset
inferredcites →https://doi.org/10.1109/cisda.2009.5356528openalex-snapshot
A detailed analysis of the KDD CUP 99 data set
inferredcites →https://doi.org/10.1109/jiot.2021.3085194openalex-snapshot
ToN_IoT: The Role of Heterogeneity and the Need for Standardization of Features and Attack Types in IoT Network Intrusion Data Sets
inferredcites →https://doi.org/10.1109/milcis.2015.7348942openalex-snapshot
UNSW-NB15: a comprehensive data set for network intrusion detection systems (UNSW-NB15 network data set)
inferredcites →https://doi.org/10.1155/2016/4731953openalex-snapshot
WSN-DS: A Dataset for Intrusion Detection Systems in Wireless Sensor Networks
inferredcites →https://doi.org/10.14569/ijacsa.2016.070159openalex-snapshot
Detecting Distributed Denial of Service Attacks Using Data Mining Techniques
inferredcites →https://doi.org/10.3103/s0146411619080121openalex-snapshot
Preparing Datasets for Training in a Neural Network System of Intrusion Detection in Industrial Systems
inferredcites →https://doi.org/10.3390/electronics9050800openalex-snapshot
LITNET-2020: An Annotated Real-World Network Flow Dataset for Network Intrusion Detection

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

inferred← citeshttps://doi.org/10.1007/978-3-031-54129-2_5openalex-snapshot
Labeling NIDS Rules with MITRE ATT &CK Techniques Using ChatGPT
inferred← citeshttps://doi.org/10.1007/978-981-96-9275-0_25openalex-snapshot
Optimizing Autoencoder Architectures for Network Anomaly Detection Using Memetic Algorithm
inferred← citeshttps://doi.org/10.1007/978-981-97-6588-1_24openalex-snapshot
Real-Time Anomaly Detection System for Network Administrators
inferred← citeshttps://doi.org/10.1007/s11277-023-10816-3openalex-snapshot
Usage of Published Network Traffic Datasets for Anomaly and Change Point Detection
inferred← citeshttps://doi.org/10.1016/j.adhoc.2026.104434openalex-snapshot
MANET cybersecurity: A systematic review of OSI attacks, AI-based detection, and adaptive security gaps (2020–2025)
inferred← citeshttps://doi.org/10.1016/j.array.2026.100984openalex-snapshot
Zero Trust Architecture Dataset (ZTAD): Advancing AI-driven cybersecurity through multi-source behavioral analysis and feature optimization
inferred← citeshttps://doi.org/10.1016/j.comnet.2025.111177openalex-snapshot
A review on intrusion detection datasets: tools, processes, and features
inferred← citeshttps://doi.org/10.1016/j.cose.2025.104510openalex-snapshot
Network intrusion datasets: A survey, limitations, and recommendations
inferred← citeshttps://doi.org/10.1016/j.cose.2026.105189openalex-snapshot
Detection capability and operational usability of network IDS Snort: A study based on MITRE ATT&CK framework
inferred← citeshttps://doi.org/10.1016/j.eij.2024.100526openalex-snapshot
DDoSNet: Detection and prediction of DDoS attacks from realistic multidimensional dataset in IoT network environment
inferred← citeshttps://doi.org/10.1016/j.future.2025.108272openalex-snapshot
Dynamic characterisation of cyberattacks based on the MITRE ATT&CK framework applied to the optimisation of a mitigation selection process
inferred← citeshttps://doi.org/10.1109/bigdata62323.2024.10825261openalex-snapshot
CIA-EBE: Class Imbalance-Aware Event-Based Embedding for SOC Log Screening
inferred← citeshttps://doi.org/10.1109/cloudnet59005.2023.10490058openalex-snapshot
OPTIMA-DEM: An Optimized Threat Behavior Prediction Method using DEMATEL-ISM
inferred← citeshttps://doi.org/10.1109/dsc65356.2025.11260881openalex-snapshot
A Scalable Multi-Datasource IDS Dataset with Technique and Lifecycle Labels Based on MITRE ATT&CK
inferred← citeshttps://doi.org/10.1109/iciscn64258.2025.10934183openalex-snapshot
Vision Transformer and Improved Bi-Directional Long Short-Term Memory based Secure Network Traffic Analysis and Threat Detection
inferred← citeshttps://doi.org/10.1109/icsece65727.2025.11257088openalex-snapshot
Research on the Application of Big Language Models in Cybersecurity Incident Response—A Log Analysis Approach Based on GPT Models
inferred← citeshttps://doi.org/10.1109/mnet.2025.3551333openalex-snapshot
Reproducing ATT&CK Techniques and Lifecycles to Train Machine Learning Classifier
inferred← citeshttps://doi.org/10.1109/southeastcon56624.2025.10971459openalex-snapshot
A GCN-Autoencoder Model to Advance Intrusion Detection on the ZeekData Dataset
inferred← citeshttps://doi.org/10.1109/tnse.2026.3716998openalex-snapshot
GenTI: Benchmarking LLMs for Autonomous IDPS Rule Generation for Unseen Attacks
inferred← citeshttps://doi.org/10.1145/3687300openalex-snapshot
MITRE ATT&CK: State of the Art and Way Forward
inferred← citeshttps://doi.org/10.3390/analytics2030035openalex-snapshot
Cyberpsychology: A Longitudinal Analysis of Cyber Adversarial Tactics and Techniques
inferred← citeshttps://doi.org/10.3390/bdcc9020023openalex-snapshot
Labeling Network Intrusion Detection System (NIDS) Rules with MITRE ATT&CK Techniques: Machine Learning vs. Large Language Models
inferred← citeshttps://doi.org/10.3390/computers13070171openalex-snapshot
Node Classification of Network Threats Leveraging Graph-Based Characterizations Using Memgraph
inferred← citeshttps://doi.org/10.3390/data10050059openalex-snapshot
Introducing UWF-ZeekData24: An Enterprise MITRE ATT&CK Labeled Network Attack Traffic Dataset for Machine Learning/AI
inferred← citeshttps://doi.org/10.3390/data9030039openalex-snapshot
CybAttT: A Dataset of Cyberattack News Tweets for Enhanced Threat Intelligence
inferred← citeshttps://doi.org/10.3390/electronics12245039openalex-snapshot
Introducing the UWF-ZeekDataFall22 Dataset to Classify Attack Tactics from Zeek Conn Logs Using Spark’s Machine Learning in a Big Data Framework
inferred← citeshttps://doi.org/10.3390/electronics13061015openalex-snapshot
Graphical Representation of UWF-ZeekData22 Using Memgraph
inferred← citeshttps://doi.org/10.3390/electronics14020257openalex-snapshot
DeepOP: A Hybrid Framework for MITRE ATT&CK Sequence Prediction via Deep Learning and Ontology
inferred← citeshttps://doi.org/10.3390/electronics14183597openalex-snapshot
Analyzing Performance of Data Preprocessing Techniques on CPUs vs. GPUs with and Without the MapReduce Environment
inferred← citeshttps://doi.org/10.3390/electronics15061291openalex-snapshot
A Systematic Ablation Study of GAN-Based Minority Augmentation for Intrusion Detection on UWF-ZeekData22
inferred← citeshttps://doi.org/10.3390/electronics15122598openalex-snapshot
RandomForestNN Classification for Adversarial AI Black-Box Techniques on MITRE ATT&CK Labeled Data
inferred← citeshttps://doi.org/10.3390/fi15040130openalex-snapshot
Resampling Imbalanced Network Intrusion Datasets to Identify Rare Attacks
inferred← citeshttps://doi.org/10.3390/fi15070236openalex-snapshot
Using a Graph Engine to Visualize the Reconnaissance Tactic of the MITRE ATT&CK Framework from UWF-ZeekData22
inferred← citeshttps://doi.org/10.3390/fi18040200openalex-snapshot
Class-Specific GAN Augmentation for Imbalanced Intrusion Detection: A Comparative Study Using the UWF-ZeekData22 Dataset
inferred← citeshttps://doi.org/10.3390/knowledge4010006openalex-snapshot
Resampling to Classify Rare Attack Tactics in UWF-ZeekData22
inferred← citeshttps://doi.org/10.3390/technologies14020117openalex-snapshot
Class-Specific GAN-Based Minority Data Augmentation for Cyberattack Detection Using the UWF-ZeekData22 Dataset
inferred← citeshttps://doi.org/10.4018/979-8-3693-5415-5.ch009openalex-snapshot
Security Considerations in Generative AI for Web Applications
inferred← citeshttps://doi.org/10.5055/jem.0968openalex-snapshot
Cyberattack trends in healthcare: A comparative analysis of publicly available incident databases
inferred← citeshttps://doi.org/10.70675/af0e8562z3bb0z4f00z9da6zbb79885a5237openalex-snapshot
Advancing Network Intrusion Detection : Datasets, Testbeds, Alert Explanations, and False Positives Reduction
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