Involving peers in research: the UNSW community reference panel by Melinda Walker, Kim Beadman, Steve Griffin, Mitchell Beadman, Carla Treloar

Get full text

UNSW Face Test: A screening tool for super-recognizers. by James D Dunn, Stephanie Summersby, Alice Towler, Josh P Davis, David White

Get full text

Spark Configurations to Optimize Decision Tree Classification on UNSW-NB15 by Sikha Bagui, Mary Walauskis, Robert DeRush, Huyen Praviset, Shaunda Boucugnani

Get full text

Students Perceptions of Excellence in Landscape Architecture Studio Projects: A UNSW Perspective by Linda Corkery

Get full text

A comparative study of Machine learning Algorithms on the UNSW-NB 15 Dataset by Tahri Rachid, Jarrar Abdessamad, Lasbahani Abdellatif, Balouki Youssef

Get full text

Enhanced Intrusion Detection Systems Performance with UNSW-NB15 Data Analysis by Shweta More, Moad Idrissi, Haitham Mahmoud, A. Taufiq Asyhari

Get full text

An Analysis of the KDD99 and UNSW-NB15 Datasets for the Intrusion Detection System by Muataz Salam Al-Daweri, Khairul Akram Zainol Ariffin, Salwani Abdullah, Mohamad Firham Efendy Md. Senan

Get full text

Classification of UNSW-NB15 dataset using Exploratory Data Analysis using Ensemble Learning by Neha Sharma, Narendra Yadav, Saurabh Sharma

Get full text

Anomaly Detection with Various Machine Learning Classification Techniques over UNSW-NB15 Dataset by Martina Shushlevska, Danijela Efnusheva, Goran Jakimovski, Zdravko Todorov

Get full text

An innovative network intrusion detection system (NIDS): Hierarchical deep learning model based on Unsw-Nb15 dataset by Mohammad A. Alsharaiah, Mosleh Abualhaj, Laith H. Baniata, Adeeb Al-saaidah, Qasem M. Kharma, Mahran M Al-Zyoud

“…We utilize the benchmark UNSW-NB15 dataset, which exhibits a diverse distribution of patterns, including a significant disparity in the size of the training and testing sets. …”
Get full text

Building Energy Performance Modeling through Regression Analysis: A Case of Tyree Energy Technologies Building at UNSW Sydney by Faham Tahmasebinia, Ruihan He, Jiayang Chen, Shang Wang, Samad M. E. Sepasgozar

Get full text

IGRF-RFE: a hybrid feature selection method for MLP-based network intrusion detection on UNSW-NB15 dataset by Yuhua Yin, Julian Jang-Jaccard, Wen Xu, Amardeep Singh, Jinting Zhu, Fariza Sabrina, Jin Kwak

Get full text

Protocol-Based Deep Intrusion Detection for DoS and DDoS Attacks Using UNSW-NB15 and Bot-IoT Data-Sets by Muhammad Zeeshan, Qaiser Riaz, Muhammad Ahmad Bilal, Muhammad K. Shahzad, Hajira Jabeen, Syed Ali Haider, Azizur Rahim

Get full text

Intrusion detection in internet of things using supervised machine learning based on application and transport layer features using UNSW-NB15 data-set by Muhammad Ahmad, Qaiser Riaz, Muhammad Zeeshan, Hasan Tahir, Syed Ali Haider, Muhammad Safeer Khan

“…Moreover, we show that the proposed feature clusters provide higher accuracy and requires lesser training time as compared to other state-of-the-art supervised ML-based approaches.…”
Get full text

Performance Evaluation of Deep Learning Based Network Intrusion Detection System across Multiple Balanced and Imbalanced Datasets by Azizjon Meliboev, Jumabek Alikhanov, Wooseong Kim

Get full text

Use the ensemble methods when detecting DoS attacks in Network Intrusion Detection Systems by Hoang Thanh, Tran Lang

Get full text

Integrated threat intelligence platform for security operations in organizations by K. U. Abinesh Kamal, S. V. Divya

Get full text

Cyberattack detection in wireless sensor networks using a hybrid feature reduction technique with AI and machine learning methods by Mohamed H. Behiry, Mohammed Aly

Get full text

Contemporary art, capitalization and the blockchain: On the autonomy and automation of art's value by Laura Lotti

Get full text

An Efficient Network Intrusion Detection and Classification System by Iftikhar Ahmad, Qazi Emad Ul Haq, Muhammad Imran, Madini O. Alassafi, Rayed A. AlGhamdi

“…Moreover, network intrusion detection remains a challenging task as a massive amount of data is required to train the state-of-the-art machine learning models to detect network intrusion threats. …”
Get full text