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Relevant bibliographies by topics / UNSW-NB15 Dataset

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Academic literature on the topic 'UNSW-NB15 Dataset'

Author: Grafiati

Published: 7 June 2025

Last updated: 2 August 2025

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Journal articles on the topic "UNSW-NB15 Dataset"

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Amien, Januar Al, Yoze Rizki, and Mukhlis Ali Rahman Nasution. "Implementasi Adasyn Untuk Imbalance Data Pada Dataset UNSW-NB15 Adasyn Implementation For Data Imbalance on UNSW-NB15 Dataset." Jurnal CoSciTech (Computer Science and Information Technology) 3, no. 3 (2022): 242–48. http://dx.doi.org/10.37859/coscitech.v3i3.4339.

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Di masa Machine Learning pada saat ini, para peneliti bekerja keras untuk mengembangkan algoritma yang meningkatkan kemungkinan prediksi yang benar dengan akurasi yang lebih baik. Data tidak seimbang adalah ketika ukuran sampel dari satu kelas jauh lebih besar dari kelas lain, sampel minoritas dapat diperlakukan sebagai noise dalam proses klasifikasi, yang mengakibatkan hasil algoritma klasifikasi yang tidak memuaskan. Pada penelitian ini peneliti menggunakan dataset UNSW-NB15, setelah menggabungkan data train dan test, terdapat data tidak seimbangan pada kelas label, yaitu 164673 untuk label

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Sonule, Avinash R. "Detection of Network Attacks using Machine Learning: A New Approach." International Journal for Research in Applied Science and Engineering Technology 9, no. 12 (2021): 1881–90. http://dx.doi.org/10.22214/ijraset.2021.39640.

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Abstract: The Cyber-attacks become the most important security problems in the today’s world. With the increase in use of computing resources connected to the Internet like computers, mobiles, sensors, IoTs in networks, Big Data, Web Applications/Server, Clouds and other computing resources, hackers and malicious users are planning new ways of network intrusions. Many techniques have been developed to detect these intrusions which are based on data mining and machine learning methods. These intrusions detection techniques have been applied on various IDS datasets. UNSW-NB15 is the latest datas

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AvinashR.Sonule, Kalla Mukesh, Jain Amit, and Chouhan D.S. "Unsw-Nb15 Dataset and Machine Learning Based Intrusion Detection Systems." International Journal of Engineering and Advanced Technology (IJEAT) 9, no. 3 (2020): 2638–48. https://doi.org/10.35940/ijeat.C5809.029320.

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The network attacks become the most important security problems in the today’s world. There is a high increase in use of computers, mobiles, sensors ,IoTs in networks, Big Data, Web Application/Server, Clouds and other computing resources. With the high increase in network traffic, hackers and malicious users are planning new ways of network intrusions. Many techniques have been developed to detect these intrusions which are based on data mining and machine learning methods. Machine learning algorithms intend to detect anomalies using supervised and unsupervised approaches .Both the dete

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Souhail et. al., Mefta. "Network Based Intrusion Detection Using the UNSW-NB15 Dataset." International Journal of Computing and Digital Systems 8, no. 5 (2019): 477–87. http://dx.doi.org/10.12785/ijcds/080505.

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Seo, Jae-Hyun. "Evolutionary Data Preprocessing to Alleviate Class Imbalance." Security and Communication Networks 2022 (October 11, 2022): 1–14. http://dx.doi.org/10.1155/2022/3761205.

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Intrusion detection technology for network attacks is developing rapidly with the development of artificial intelligence technology. Recently, machine learning-based methods that can detect new types of attacks have been developed. To improve the classification performance of the rare classes in the intrusion detection dataset, we study the efficient data preprocessing method based on machine learning. The UNSW-NB15, a well-known network intrusion detection dataset, is used in the experiments. The dataset includes 9 attack types and has severe class imbalance and overlap, so it is difficult to

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Rehman, Abdul, Omar Alharbi, Yazeed Qasaymeh, and Amer Aljaedi. "DC-NFC: A Custom Deep Learning Framework for Security and Privacy in NFC-Enabled IoT." Sensors 25, no. 5 (2025): 1381. https://doi.org/10.3390/s25051381.

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NFC has emerged as a critical technology in IoET ecosystems, facilitating seamless data exchange in proximity-based systems. However, the security and privacy challenges associated with NFC-enabled IoT devices remain significant, exposing them to various threats such as eavesdropping, relay attacks, and spoofing. This paper introduces DC-NFC, a novel deep learning framework designed to enhance the security and privacy of NFC communications within IoT environments. The proposed framework integrates three innovative components: the CE for capturing intricate temporal and spatial patterns, the PM

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Kottilingal, Shahir. "Deep Learning Based Network Intrusion Detection System: A Deep Abstract Networks (DANets) Model Approach." International Research Journal of Computer Science 11, no. 07 (2024): 539–44. http://dx.doi.org/10.26562/irjcs.2024.v1107.01.

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The need for frequent updates on Network Intrusion Detection Systems (NIDS) is crucial for safeguarding enterprise data against cyberattacks due to the advancement in the attackers' arsenal resulting from technological advancements. Conventional signature-based NIDS often struggle to keep up with newer types of intrusions due to their sophisticated nature. This has forced the advancement of machine learning-based NIDS, which have shown promising results. There is much recent research on machine learning and deep learning-based network intrusion detection system. Also, there is few important re

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Bagui, Sikha, Mary Walauskis, Robert DeRush, Huyen Praviset, and Shaunda Boucugnani. "Spark Configurations to Optimize Decision Tree Classification on UNSW-NB15." Big Data and Cognitive Computing 6, no. 2 (2022): 38. http://dx.doi.org/10.3390/bdcc6020038.

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This paper looks at the impact of changing Spark’s configuration parameters on machine learning algorithms using a large dataset—the UNSW-NB15 dataset. The environmental conditions that will optimize the classification process are studied. To build smart intrusion detection systems, a deep understanding of the environmental parameters is necessary. Specifically, the focus is on the following environmental parameters: the executor memory, number of executors, number of cores per executor, execution time, as well as the impact on statistical measures. Hence, the objective was to optimize resourc

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Hacılar, Hilal, Bilge Kagan Dedeturk, Burcu Bakir-Gungor, and Vehbi Cagri Gungor. "Network anomaly detection using Deep Autoencoder and parallel Artificial Bee Colony algorithm-trained neural network." PeerJ Computer Science 10 (October 8, 2024): e2333. http://dx.doi.org/10.7717/peerj-cs.2333.

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Cyberattacks are increasingly becoming more complex, which makes intrusion detection extremely difficult. Several intrusion detection approaches have been developed in the literature and utilized to tackle computer security intrusions. Implementing machine learning and deep learning models for network intrusion detection has been a topic of active research in cybersecurity. In this study, artificial neural networks (ANNs), a type of machine learning algorithm, are employed to determine optimal network weight sets during the training phase. Conventional training algorithms, such as back-propaga

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Thanh, Hoang Ngoc, and Tran Van Lang. "EVALUATING EFFECTIVENESS OF ENSEMBLE CLASSIFIERS WHEN DETECTING FUZZERS ATTACKS ON THE UNSW-NB15 DATASET." Journal of Computer Science and Cybernetics 36, no. 2 (2020): 173–85. http://dx.doi.org/10.15625/1813-9663/36/2/14786.

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The UNSW-NB15 dataset was created by the Australian Cyber Security Centre in 2015 by using the IXIA tool to extract normal behaviors and modern attacks, it includes normal data and 9 types of attacks with 49 features. Previous research results show that the detection of Fuzzers attacks in this dataset gives the lowest classification quality. This paper analyzes and evaluates the performance of using known ensemble techniques such as Bagging, AdaBoost, Stacking, Decorate, Random Forest and Voting to detect FUZZERS attacks on UNSW-NB15 dataset to create models. The experimental results show that

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Dissertations / Theses on the topic "UNSW-NB15 Dataset"

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Zoghi, Zeinab. "Ensemble Classifier Design and Performance Evaluation for Intrusion Detection Using UNSW-NB15 Dataset." University of Toledo / OhioLINK, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=toledo1596756673292254.

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Pacheco, Monasterios Yulexis D. "Adversarial Machine Learning: A Comparative Study on Contemporary Intrusion Detection Datasets." University of Toledo / OhioLINK, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=toledo1596794840894376.

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Book chapters on the topic "UNSW-NB15 Dataset"

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Dickson, Anne, and Ciza Thomas. "Analysis of UNSW-NB15 Dataset Using Machine Learning Classifiers." In Communications in Computer and Information Science. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-0419-5_16.

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Kumar, Vikash, Ayan Kumar Das, and Ditipriya Sinha. "Statistical Analysis of the UNSW-NB15 Dataset for Intrusion Detection." In Computational Intelligence in Pattern Recognition. Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-9042-5_24.

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Alani, Mohammed M. "Implementation-Oriented Feature Selection in UNSW-NB15 Intrusion Detection Dataset." In Intelligent Systems Design and Applications. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-96308-8_51.

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Seong, ChangMin, YoungRok Song, Jiwung Hyun, and Yun-Gyung Cheong. "Towards Building Intrusion Detection Systems for Multivariate Time-Series Data." In Silicon Valley Cybersecurity Conference. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-96057-5_4.

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AbstractRecent network intrusion detection systems have employed machine learning and deep learning algorithms to defend against dynamically evolving network attacks. While most previous studies have focused on detecting attacks which can be determined based on a single time instant, few studies have paid attention to subsequence outliers, which require inspecting consecutive points in time for detection. To address this issue, this paper applies a time-series anomaly detection method in an unsupervised learning manner. To this end, we converted the UNSW-NB15 dataset into the time-series data.

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Roy, Aditi, and Khundrakpam Johnson Singh. "Multi-classification of UNSW-NB15 Dataset for Network Anomaly Detection System." In Algorithms for Intelligent Systems. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-5077-5_40.

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Singh, Yambem Ranjan, Chandam Chinglensana Singh, Linthoingambi Takhellambam, Khumukcham Robindro Singh, and Nazrul Hoque. "ML-Based Intrusion Detection with Feature Analysis on Unbalanced UNSW-NB15 Dataset." In Lecture Notes in Electrical Engineering. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-6465-5_26.

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Sambandam, Rakoth Kandan, D. Daniel, R. Gokulapriya, Divya Vetriveeran, J. Jenefa, and Anuneshwar. "Comparison of Machine Learning-Based Intrusion Detection Systems Using UNSW-NB15 Dataset." In Artificial Intelligence: Theory and Applications. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-99-8479-4_23.

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Manasa, Koppula, and L. M. I. Leo Joseph. "A Machine Learning-Based Vulnerability Detection Approach for the Imbalanced Dataset UNSW-NB15." In Communication and Intelligent Systems. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-2100-3_23.

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Manneh, Madlyn, Patrick Ansah, Sumit Kumar Tetarave, Manoj Ranjan Mishra, and Ezhil Kalaimannan. "A Comparative Analysis of Random Forest and Support Vector Machine Techniques on the UNSW-NB15 Dataset." In Lecture Notes in Networks and Systems. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-65522-7_18.

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Kumar, Avinash, Anita Soni, and Manmohan Singh. "Performing Multiclass Classification on UNSW-NB15 Dataset by Applying Machine Learning Approach on Intrusion Detection System." In Data-Intensive Research. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-99-9179-2_36.

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Conference papers on the topic "UNSW-NB15 Dataset"

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Sharma, Rishabh, and Sakshi Sobti. "A Deep Neural Networks Model for Intrusion Detection in UNSW-NB15 Dataset." In 2024 4th Asian Conference on Innovation in Technology (ASIANCON). IEEE, 2024. https://doi.org/10.1109/asiancon62057.2024.10837906.

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Kumar, Atul, Kalpna Guleria, Rahul Chauhan, and Deepak Upadhyay. "Advancing Intrusion Detection with Machine Learning: Insights from the UNSW-NB15 Dataset." In 2024 IEEE International Conference on Information Technology, Electronics and Intelligent Communication Systems (ICITEICS). IEEE, 2024. http://dx.doi.org/10.1109/iciteics61368.2024.10625148.

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V, Santhosh Kumar, Saiharish S, and Dinesh Kumar A. "Network Intrusion Detection Through Stacked Machine Learning Models on UNSW-NB15 Dataset." In 2024 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems (ICSES). IEEE, 2024. https://doi.org/10.1109/icses63760.2024.10910677.

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Jose, Anish Mathew, Avirup Mukherjee, Joydeep Saha, et al. "Multi-Class SVM & Random Forest Based Intrusion Detection Using UNSW-NB15 Dataset." In 2024 15th International Conference on Computing Communication and Networking Technologies (ICCCNT). IEEE, 2024. http://dx.doi.org/10.1109/icccnt61001.2024.10725989.

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Pal, Kim Kristoffer, Aleksander Vanberg Eriksen, and Nga Dinh. "XGBoost Feature Selection for Multi-Class and Binary Classification on UNSW-NB15 Dataset." In 2025 IEEE International Conference on Consumer Electronics (ICCE). IEEE, 2025. https://doi.org/10.1109/icce63647.2025.10930023.

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Putrada, Aji Gautama, Nur Alamsyah, Mohamad Nurkamal Fauzan, and Ikke Dian Oktaviani. "Pearson Correlation for Efficient Network Anomaly Detection with Quantization on the UNSW-NB15 Dataset." In 2024 International Conference on ICT for Smart Society (ICISS). IEEE, 2024. http://dx.doi.org/10.1109/iciss62896.2024.10751550.

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G, Suchetha, and Pushpalatha K. "Optimizing Botnet Detection in IoT Networks: Feature Selection Analysis on the UNSW-NB15 Dataset." In 2024 IEEE International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics (DISCOVER). IEEE, 2024. http://dx.doi.org/10.1109/discover62353.2024.10750583.

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Zhuang, Licheng, Jun Hu, Qingqing Wang, Yanyan Wang, Kaikai Zhang, and Sheng Liu. "An Improved Lightweight CNN_BiLSTM Model for Network Intrusion Detection Using the UNSW-NB15 Dataset." In 2024 2nd International Conference on Computer, Vision and Intelligent Technology (ICCVIT). IEEE, 2024. https://doi.org/10.1109/iccvit63928.2024.10872444.

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Rashid, Omar Fitian, Saba A. Tuama, and Mohammed Ahmed Subhi. "Anomaly Intrusion Detection System Based on RNA Encoding and YAKE Algorithm Using UNSW-NB15 Dataset." In 2024 1st International Conference on Cyber Security and Computing (CyberComp). IEEE, 2024. https://doi.org/10.1109/cybercomp60759.2024.10913877.

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Pear, Zareen Tasnim, and Hafsa Binte Kibria. "Enhanced Network Intrusion Detection Using a Hybrid CNN-LSTM Approach on the UNSW-NB15 Dataset." In 2024 21st International Conference on Electrical Engineering, Computing Science and Automatic Control (CCE). IEEE, 2024. https://doi.org/10.1109/cce62852.2024.10770969.

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