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Nour Moustafa
Researcher at University of New South Wales
Publications - 152
Citations - 7334
Nour Moustafa is an academic researcher from University of New South Wales. The author has contributed to research in topics: Computer science & Intrusion detection system. The author has an hindex of 23, co-authored 105 publications receiving 3046 citations. Previous affiliations of Nour Moustafa include Australian Defence Force Academy & Cooperative Research Centre.
Papers
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Proceedings ArticleDOI
UNSW-NB15: a comprehensive data set for network intrusion detection systems (UNSW-NB15 network data set)
Nour Moustafa,Jill Slay +1 more
TL;DR: Countering the unavailability of network benchmark data set challenges, this paper examines a UNSW-NB15 data set creation which has a hybrid of the real modern normal and the contemporary synthesized attack activities of the network traffic.
Journal ArticleDOI
Towards the development of realistic botnet dataset in the Internet of Things for network forensic analytics: Bot-IoT dataset
TL;DR: In this paper, the authors proposed a new dataset, called Bot-IoT, which incorporates legitimate and simulated IoT network traffic, along with various types of attacks, and evaluated the reliability of the dataset using different statistical and machine learning methods for forensics purposes.
Journal ArticleDOI
The evaluation of Network Anomaly Detection Systems: Statistical analysis of the UNSW-NB15 data set and the comparison with the KDD99 data set
Nour Moustafa,Jill Slay +1 more
TL;DR: The experimental results show that UNSW-NB15 is more complex than KDD99 and is considered as a new benchmark data set for evaluating NIDSs.
Journal ArticleDOI
An Ensemble Intrusion Detection Technique Based on Proposed Statistical Flow Features for Protecting Network Traffic of Internet of Things
TL;DR: An ensemble intrusion detection technique is proposed to mitigate malicious events, in particular botnet attacks against DNS, HTTP, and MQTT protocols utilized in IoT networks, and shows that the proposed features have the potential characteristics of normal and malicious activity.
Journal ArticleDOI
TON_IoT Telemetry Dataset: A New Generation Dataset of IoT and IIoT for Data-Driven Intrusion Detection Systems
TL;DR: A new data-driven IoT/IIoT dataset with the ground truth that incorporates a label feature indicating normal and attack classes, as well as a type feature indicating the sub-classes of attacks targeting IoT/ IIoT applications for multi-classification problems is proposed.