UNSW-NB15: a comprehensive data set for network intrusion detection systems (UNSW-NB15 network data set)

By Nour Moustafa, Jill Slay. Military Communications and Information Systems Conference, 2015

3,775 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Networks and Communications, Signal Processing

View Paper: https://www.semanticscholar.org/paper/0e7af8e91b8cb2cea1164be5ac5d280b0d12c153

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Citations in the corpus

A Review of Intrusion Detection Systems Using Machine and Deep Learning in Internet of Things: Challenges, Solutions and Future Directions (2020, background): “It covers 10 targets: one benign, and nine anomalous, namely: DoS, Exploits, Analysis, Fuzzers, Worms, Reconnaissance, Generic, Shell Code and Backdoors [208].”
Performance Analysis of Intrusion Detection Systems Using a Feature Selection Method on the UNSW-NB15 Dataset (2020, methodology): “UNSW‐NB15 Dataset For our experimental processes we utilize the UNSW-NB15 attacks dataset [41].”
AI-Driven Cybersecurity: An Overview, Security Intelligence Modeling and Research Directions (2021, background): “Another popular dataset UNSW-NB15 [60] that consists of different types of attacks.”
Internet of Things Applications, Security Challenges, Attacks, Intrusion Detection, and Future Visions: A Systematic Review (2021, background): “The skewedness of KDD-99 and NSL-KDD is almost removed in UNSW-NB15 [79], [80], consisting of 49 features and 10 target classes, whereas KDD consists of 41 features and 5 target classes.”
Towards the Development of Realistic Botnet Dataset in the Internet of Things for Network Forensic Analytics: Bot-IoT Dataset (2018, methodology): “Although various research studies have been conducted [7, 8, 9, 32, 35, 38, 34, 36] to generate network datasets, the development of realistic IoT and network traﬃc dataset that includes recent Botnet scenarios still is an unexplored topic.”
TON_IoT Telemetry Dataset: A New Generation Dataset of IoT and IIoT for Data-Driven Intrusion Detection Systems (2020, methodology): “Various network datasets, for example, KDDCUP99, NSL-KDD [12], UNSW-NB15 [13] and ISCX [14], were generated for evaluating IDSs; however, they do not include any speciﬁc characteristics of IoT/IIoT applications as these datasets contain neither sensors’ reading data (i.e., telemetry or measurement…”
Cybersecurity for industrial control systems: A survey (2020, methodology): “As a part of our future research, we aim to prepare hybrid datasets using multiple publicly available datasets such as UNSW (University of New South Wales) dataset [74], ISOT dataset [68], Lawrence Berkeley National Lab (LBNL) dataset [73], Information Security Center of Excellence (ISCX) [76], next”
Deep Learning for Anomaly Detection (2020, neutral): “70% Tabular [58, 105, 106, 179] Intrusion detection UNSW-NB15 [102] 257,673 49 ≤9.”
A GA-LR wrapper approach for feature selection in network intrusion detection (2017, methodology): “The dataset used in the context of intrusion detection contains different forms of features such as continuous, discrete, and symbolic with varying resolution and ranges (Aburomman and Reaz, 2016; Moustafa and Slay, 2015).”
Machine Learning: Algorithms, Real-World Applications and Research Directions (2021, neutral): “These are, for example, cybersecurity datasets such as NSL-KDD [119], UNSW-NB15 [76], ISCX’12 [1], CIC-DDoS2019 [2], Bot-IoT [59], etc.”
Tackling Faults in the Industry 4.0 Era—A Survey of Machine-Learning Solutions and Key Aspects (2019, methodology): “0 data sets are not available, the evaluation of the threat intelligence scheme was realised by combining two well-known data sets; the CPS data set of sensors and physical devices [85] and the UNSW-NB15 data set of network traffic [86].”