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Journal articles on the topic 'UNSW-NB15 Dataset'

Author: Grafiati

Published: 7 June 2025

Last updated: 2 August 2025

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1

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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Yoon, Pil-Do, and Gyung-Ho Hwang. "Malicious Traffic Classification in a UNSW-NB15 Dataset by Using Tomeklinks and ClusBUS." Journal of Korean Institute of Communications and Information Sciences 46, no. 11 (2021): 1896–99. http://dx.doi.org/10.7840/kics.2021.46.11.1896.

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Fiona Lawrence. "Enhancing Intrusion Detection Systems with Ensemble Models and Hybrid Feature Selection Techniques." Journal of Information Systems Engineering and Management 10, no. 23s (2025): 937–54. https://doi.org/10.52783/jisem.v10i23s.3816.

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Detection Systems (IDS) play a critical role in safeguarding networks against cyber attacks. However, selecting the most effective machine learning model for intrusion detection is challenging due to varying dataset characteristics. This research investigates the performance of multiple machine learning models, including SVM (Linear, Poly, RBF, and Sigmoid), LightGBM, XGBoost, and CatBoost, across two widely used datasets: CICIDS2017 and NF-UNSW-NB15. The primary problem is the inconsistency in model performance across different datasets, affecting the reliability of IDS solutions. To address

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Toldinas, Jevgenijus, Algimantas Venčkauskas, Robertas Damaševičius, Šarūnas Grigaliūnas, Nerijus Morkevičius, and Edgaras Baranauskas. "A Novel Approach for Network Intrusion Detection Using Multistage Deep Learning Image Recognition." Electronics 10, no. 15 (2021): 1854. http://dx.doi.org/10.3390/electronics10151854.

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The current rise in hacking and computer network attacks throughout the world has heightened the demand for improved intrusion detection and prevention solutions. The intrusion detection system (IDS) is critical in identifying abnormalities and assaults on the network, which have grown in size and pervasiveness. The paper proposes a novel approach for network intrusion detection using multistage deep learning image recognition. The network features are transformed into four-channel (Red, Green, Blue, and Alpha) images. The images then are used for classification to train and test the pre-train

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E, Thriveni, and Mahaveerakannan R. "Developing the Security for Cloud Information Via Alexnet Learning Model versus the accuracy of Artificial Neural Network." E3S Web of Conferences 399 (2023): 04023. http://dx.doi.org/10.1051/e3sconf/202339904023.

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The main objective of the study is to protect cloud from different types of attacks by using AlexNet classifier compared accuracy with Artificial Neural Network and user’s data to be stored in the cloud safely by Advanced Encryption Standard. Materials and Methods: This research examines two groups AlexNet withArtificial Neural Network. Statistical study used 1300 training and 403 testing datasets from UNSW-NB15 dataset. ClinCalc programme utilised N=10, 0.05 is alpha value, 0.8% is G-Power, and 95% confidence interval.Result and Discussion: Novel Alexnet (91.081%) has an increased precision o

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E. L. Asry, Chadia, Ibtissam Benchaji, Samira Douzi, and Bouabid E. L. Ouahidi. "A robust intrusion detection system based on a shallow learning model and feature extraction techniques." PLOS ONE 19, no. 1 (2024): e0295801. http://dx.doi.org/10.1371/journal.pone.0295801.

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The escalating prevalence of cybersecurity risks calls for a focused strategy in order to attain efficient resolutions. This study introduces a detection model that employs a tailored methodology integrating feature selection using SHAP values, a shallow learning algorithm called PV-DM, and machine learning classifiers like XGBOOST. The efficacy of our suggested methodology is highlighted by employing the NSL-KDD and UNSW-NB15 datasets. Our approach in the NSL-KDD dataset exhibits exceptional performance, with an accuracy of 98.92%, precision of 98.92%, recall of 95.44%, and an F1-score of 96.

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A, Sagaya Priya, and Britto Ramesh Kumar S. "Semi-Supervised Intrusion Detection Based on Stacking and Feature-Engineering to Handle Data Imbalance." Indian Journal of Science and Technology 15, no. 46 (2022): 2548–54. https://doi.org/10.17485/IJST/v15i46.1885.

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Abstract <strong>Objectives:</strong> To design an architecture that can effectively handle the imbalance levels and complexities in the network data to provide qualitative predictions. <strong>Methods:</strong> Experiments were performed with KDD CUP 99 dataset, NSL- KDD dataset and UNSW- NB15 dataset. Comparisons were performed with SAVAERDNN model. Oversampling technique is used for data balancing, and the stacking architecture handles the issue of overtraining introduced due to oversampling.<strong> Findings:</strong> The proposed Stacking and Feature engineeringba

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Kocher, Geeta, and Gulshan Kumar. "Analysis of Machine Learning Algorithms with Feature Selection for Intrusion Detection using UNSW-NB15 Dataset." International Journal of Network Security & Its Applications 13, no. 1 (2021): 21–31. http://dx.doi.org/10.5121/ijnsa.2021.13102.

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In recent times, various machine learning classifiers are used to improve network intrusion detection. The researchers have proposed many solutions for intrusion detection in the literature. The machine learning classifiers are trained on older datasets for intrusion detection, which limits their detection accuracy. So, there is a need to train the machine learning classifiers on the latest dataset. In this paper, UNSW-NB15, the latest dataset is used to train machine learning classifiers. The selected classifiers such as K-Nearest Neighbors (KNN), Stochastic Gradient Descent (SGD), Random For

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Geeta, Kocher, and Kumar Gulshan. "ANALYSIS OF MACHINE LEARNING ALGORITHMS WITH FEATURE SELECTION FOR INTRUSION DETECTION USING UNSW-NB15 DATASET." International Journal of Network Security & Its Applications (IJNSA) 13, no. 1 (2021): 21–31. https://doi.org/10.5281/zenodo.4536793.

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In recent times, various machine learning classifiers are used to improve network intrusion detection. The researchers have proposed many solutions for intrusion detection in the literature. The machine learning classifiers are trained on older datasets for intrusion detection, which limits their detection accuracy. So, there is a need to train the machine learning classifiers on the latest dataset. In this paper, UNSW-NB15, the latest dataset is used to train machine learning classifiers. The selected classifiers such as K-Nearest Neighbors (KNN), Stochastic Gradient Descent (SGD), Random For

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19

Farooqi, Ashfaq Hussain, Shahzaib Akhtar, Hameedur Rahman, Touseef Sadiq, and Waseem Abbass. "Enhancing Network Intrusion Detection Using an Ensemble Voting Classifier for Internet of Things." Sensors 24, no. 1 (2023): 127. http://dx.doi.org/10.3390/s24010127.

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In the context of 6G technology, the Internet of Everything aims to create a vast network that connects both humans and devices across multiple dimensions. The integration of smart healthcare, agriculture, transportation, and homes is incredibly appealing, as it allows people to effortlessly control their environment through touch or voice commands. Consequently, with the increase in Internet connectivity, the security risk also rises. However, the future is centered on a six-fold increase in connectivity, necessitating the development of stronger security measures to handle the rapidly expand

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Omarov, Bauyrzhan S., O. А. Auelbekov, B. O. Kulambayev, and B. S. Omarov. "IOT NETWORK INTRUSION DETECTION USING MACHINE LEARNING ON UNSW-NB15 DATASET." Herald of the Kazakh-British technical university 21, no. 3 (2024): 48–57. http://dx.doi.org/10.55452/1998-6688-2024-21-3-48-57.

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This research presents a comprehensive investigation into the application of machine learning techniques for addressing the pervasive security challenges within Internet of Things (IoT) networks. With the exponential growth of interconnected devices, ensuring the integrity and confidentiality of data transmissions has become increasingly critical. In this study, we deploy and evaluate seven distinct machine learning methods tailored to the IoT network intrusion detection problem. Leveraging the rich and diverse UNSW-NB15 dataset, encompassing real-world network traffic scenarios, our analysis

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Baidoo, Charity Yaa Mansa, Winfred Yaokumah, and Ebenezer Owusu. "Estimating Overhead Performance of Supervised Machine Learning Algorithms for Intrusion Detection." International Journal of Information Technologies and Systems Approach 16, no. 1 (2023): 1–19. http://dx.doi.org/10.4018/ijitsa.316889.

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Estimating the energy and memory consumption of machine learning(ML) models for intrusion detection ensures efficient allocation of system resources. This study investigates the impact of supervised ML algorithms on the energy and memory consumption of intrusion detection systems. Experiments are conducted with seven ML algorithms and a proposed ensemble model, utilizing two intrusion detection datasets. Pearson correlation coefficient(PCC) and Spearman correlation coefficient are employed for the selection of optimum features. Regarding energy consumption, the findings reveal that the PCC wit

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Han, Hyojoon, Hyukho Kim, and Yangwoo Kim. "An Efficient Hyperparameter Control Method for a Network Intrusion Detection System Based on Proximal Policy Optimization." Symmetry 14, no. 1 (2022): 161. http://dx.doi.org/10.3390/sym14010161.

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The complexity of network intrusion detection systems (IDSs) is increasing due to the continuous increases in network traffic, various attacks and the ever-changing network environment. In addition, network traffic is asymmetric with few attack data, but the attack data are so complex that it is difficult to detect one. Many studies on improving intrusion detection performance using feature engineering have been conducted. These studies work well in the dataset environment; however, it is challenging to cope with a changing network environment. This paper proposes an intrusion detection hyperp

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Akintoye, Kayode Akinlekan. "Network Intrusion Detection and Classification System: A Supervised Machine Learning Approach." International Journal for Research in Applied Science and Engineering Technology 12, no. 7 (2024): 657–70. http://dx.doi.org/10.22214/ijraset.2024.63548.

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Abstract: Intrusion detection systems (IDSs) are crucial for computer security, as they identify and counteract malicious activities within computer networks. Anomaly-based IDSs, specifically, use classification models trained on historical data to detect these harmful activities. This paper proposes an enhanced IDS based on 3-level training and testing of machine learning models, feature selection, resampling, and normalization using Decision Tree, Gaussian Naïve Bayes, K-Nearest Neighbours, Logistic Regression, Random Forest, and Support Vector Machine. In the first stage, the six models are

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Cui, Bo, Yachao Chai, Zhen Yang, and Keqin Li. "Intrusion Detection in IoT Using Deep Residual Networks with Attention Mechanisms." Future Internet 16, no. 7 (2024): 255. http://dx.doi.org/10.3390/fi16070255.

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Connected devices in IoT systems usually have low computing and storage capacity and lack uniform standards and protocols, making them easy targets for cyberattacks. Implementing security measures like cryptographic authentication, access control, and firewalls for IoT devices is insufficient to fully address the inherent vulnerabilities and potential cyberattacks within the IoT environment. To improve the defensive capabilities of IoT systems, some research has focused on using deep learning techniques to provide new solutions for intrusion detection systems. However, some existing deep learn

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Shuxin, Shi, Han Bing, Wu Zhongdai, Han Dezhi, Wu Huafeng, and Mei Xiaojun. "BLSAE-SNIDS: A Bi-LSTM sparse autoencoder framework for satellite network intrusion detection." Computer Science and Information Systems, no. 00 (2024): 41. http://dx.doi.org/10.2298/csis240401041s.

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Due to disparities in tolerance, resource availability, and acquisition of labeled training data between satellite-terrestrial integrated networks (STINs) and terrestrial networks, the application of traditional terrestrial network intrusion detection techniques to satellite networks poses significant challenges. This paper presents a satellite network intrusion detection system named Bi-LSTM sparse selfencoder (BLSAE-SNIDS) to address this issue. Through the development of an innovative unsupervised training Bi-LSTM stacked self-encoder, BLSAE-SNIDS facilitates feature extraction from satelli

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ANOH, Nogbou Georges, Tiémoman KONE, Joel Christian ADEPO, Jean François M’MOH, and Michel BABRI. "IoT Intrusion Detection System based on Machine Learning Algorithms usingthe UNSW-NB15 dataset." International Journal of Advances in Scientific Research and Engineering 10, no. 01 (2024): 16–28. http://dx.doi.org/10.31695/ijasre.2024.1.3.

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The evolution of communications systems with the advent of IoT is leading to an increase in attacks against them. This is due to the fact that the security of connected objects in the IoT is an emerging area which still requires preventive solutions against various attacks. At the network security level, Intrusion Detection Systems (IDS) are used to analyze network data and detect abnormal behavior in the network. In this work, we implemented different machine learning models to build an intrusion detection system based on the UNSW NB15 dataset. To do this, we did data cleaning and feature eng

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Venkata Sai Rahul Trivedi Kothapalli. "LightGBM-Based Anomaly Detection System for Modern Network Traffic Using UNSW-NB15 Dataset." Journal of Information Systems Engineering and Management 10, no. 8s (2025): 197–209. https://doi.org/10.52783/jisem.v10i8s.1020.

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Intrusion detection systems play a vital role in identifying unauthorized activities and safeguarding against emerging threats. This study highlights the capabilities of the UNSW-NB15 dataset, a modern benchmark for network anomaly detection, capturing diverse network traffic patterns. Unlike earlier research that often lacked uniform validation or relied on limited evaluation methods, we leverage the LightGBM classifier for binary anomaly detection, integrating advanced feature engineering, preprocessing, and selection techniques. Our approach was evaluated using various experimental configur

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Duong, Lai Van. "Network anomaly detection technique based on LSTM network and UNSW-NB15 dataset." International Journal of Advanced Trends in Computer Science and Engineering 9, no. 4 (2020): 6527–32. http://dx.doi.org/10.30534/ijatcse/2020/340942020.

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Sharma, Neha, Narendra Yadav, and Saurabh Sharma. "Classification of UNSW-NB15 dataset using Exploratory Data Analysis using Ensemble Learning." EAI Endorsed Transactions on Industrial Networks and Intelligent Systems 8, no. 29 (2021): 171319. http://dx.doi.org/10.4108/eai.13-10-2021.171319.

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Journal, IJSREM. "Review of High Performance Network Intrusion Detection Engine." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 01 (2024): 1–13. http://dx.doi.org/10.55041/ijsrem28002.

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As the proliferation of connected devices and services increases, so does the demand for protective measures against cyber-attacks. Intrusion Detection Systems (IDS) are a crucial component of network perimeter security, detecting attacks by inspecting network traffic packets or operating system logs. While machine learning techniques have shown effectiveness in intrusion detection, few have utilized the time- series information of network traffic data, and none have included categorical information in neural network-based approaches. In this paper, we propose network intrusion detection model

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Pham-Quoc, Cuong, Tran Hoang Quoc Bao, and Tran Ngoc Thinh. "FPGA/AI-Powered Architecture for Anomaly Network Intrusion Detection Systems." Electronics 12, no. 3 (2023): 668. http://dx.doi.org/10.3390/electronics12030668.

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This paper proposes an architecture to develop machine learning/deep learning models for anomaly network intrusion detection systems on reconfigurable computing platforms. We build two models to validate the framework: Anomaly Detection Autoencoder (ADA) and Artificial Neural Classification (ANC) in the NetFPGA-sume platform. Three published data sets NSL-KDD, UNSW-NB15, and CIC-IDS2017 are used to test the deployed models’ throughput, latency, and accuracy. Experimental results with the NetFPGA-SUME show that the ADA model uses 20.97% LUTs, 15.16% FFs, 19.42% BRAM, and 6.81% DSP while the ANC

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Maasaoui, Zineb, Mheni Merzouki, Abdella Battou, and Ahmed Lbath. "A Scalable Framework for Real-Time Network Security Traffic Analysis and Attack Detection Using Machine and Deep Learning." Platforms 3, no. 2 (2025): 7. https://doi.org/10.3390/platforms3020007.

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This paper presents an advanced framework for real-time monitoring and analysis of network traffic and endpoint security in large-scale enterprises by addressing the increasing complexity and frequency of cyber-attacks. Our Network Security Traffic Analysis Platform employs a comprehensive technology stack including the Elastic Stack, ZEEK, Osquery, Kafka, and GeoLocation data. By integrating supervised machine learning models trained on the UNSW-NB15 dataset, we evaluate Random Forest (RF), Decision Trees (DT), and Support Vector Machines (SVM), with the Random Forest classifier achieving a n

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Kumar Silivery, Arun, Kovvur Ram Mohan Rao, and L. K. Suresh Kumar. "An Effective Deep Learning Based Multi-Class Classification of DoS and DDoS Attack Detection." International journal of electrical and computer engineering systems 14, no. 4 (2023): 421–31. http://dx.doi.org/10.32985/ijeces.14.4.6.

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In the past few years, cybersecurity is becoming very important due to the rise in internet users. The internet attacks such as Denial of service (DoS) and Distributed Denial of Service (DDoS) attacks severely harm a website or server and make them unavailable to other users. Network Monitoring and control systems have found it challenging to identify the many classes of DoS and DDoS attacks since each operates uniquely. Hence a powerful technique is required for attack detection. Traditional machine learning techniques are inefficient in handling extensive network data and cannot extract high

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Jegatheesan A., Anitha Govindaram,. "Enhancing Industrial IoT Security: Utilizing Blockchain-Assisted Deep Federated Learning for Collaborative Intrusion Detection." Journal of Electrical Systems 20, no. 2s (2024): 1345–63. http://dx.doi.org/10.52783/jes.1782.

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The Industrial Internet of Things (IIoT) is a rapidly evolving features with multiple applications, including critical infrastructure. Privacy policies are required to preserve the protection of user data in the threat intelligence community. Blockchain is a modern technology which used recently to provide more secure storage and efficiency. In this research, Blockchain Assisted Deep Federated Learning (BC_DFL) system is used to detect intruders. The three key processes used in the proposed intrusion detection architecture are data collection, pre-processing and intrusion detection. Data norma

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Farhad Gharehchopogh, Farhad Gharehchopogh, and Samir Bagirzada Samir Bagirzada. "ENHANCING NETWORK INTRUSION DETECTION WITH MOUNTAIN GAZELLE OPTIMIZER-BASED FEATURE SELECTION." PAHTEI-Procedings of Azerbaijan High Technical Educational Institutions 43, no. 08-01 (2024): 520–29. http://dx.doi.org/10.36962/pahtei4308012024-58.

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Network intrusion detection systems (NIDS) play a critical role in safeguarding modern networks. Feature selection techniques are essential for optimizing NIDS performance by reducing dimensionality and enhancing classifier efficiency. This study proposes the Mountain Gazelle Optimizer (MGO), a novel nature-inspired metaheuristic, for effective feature selection within the context of network intrusion detection. MGO's performance is evaluated on the benchmark UNSW-NB15 dataset in conjunction with a diverse suite of classifiers: Support Vector Machines (SVM), K-Nearest Neighbors (KNN), Random F

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Kilichev, Dusmurod, and Wooseong Kim. "Hyperparameter Optimization for 1D-CNN-Based Network Intrusion Detection Using GA and PSO." Mathematics 11, no. 17 (2023): 3724. http://dx.doi.org/10.3390/math11173724.

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This study presents a comprehensive exploration of the hyperparameter optimization in one-dimensional (1D) convolutional neural networks (CNNs) for network intrusion detection. The increasing frequency and complexity of cyberattacks have prompted an urgent need for effective intrusion-detection systems (IDSs). Herein, we focus on optimizing nine hyperparameters within a 1D-CNN model, using two well-established evolutionary computation methods—genetic algorithm (GA) and particle swarm optimization (PSO). The performances of these methods are assessed using three major datasets—UNSW-NB15, CIC-ID

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Malik, Mubasher, Hamid Ghous, Mutahira Mubeen, Ansar Munir Munir, and Nazir Ahmad. "Intelligent Intrusion Detection System for Internet of Things using Machine Learning Techniques." International Journal of Information Systems and Computer Technologies 3, no. 1 (2024): 23–39. http://dx.doi.org/10.58325/ijisct.003.01.0073.

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The Internet of Things (IoT) is an interconnected framework of computer devices, software, and sensors to work collectively to automate and monitor different tasks and procedures without human intervention. IoTs provide benefits and ease in performing operations for various industries and home and health stakeholders. Still, it faces security threats and challenges that make the IoT framework unreliable regarding security and privacy. Intrusion detection systems have been designed and deployed to overcome these security challenges. Over the past few years, for making the IDS intelligent artifi

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Jeyanthi, D. V., and B. Indrani. "An Efficient Intrusion Detection System with Custom Features using FPA-Gradient Boost Machine Learning Algorithm." International journal of Computer Networks & Communications 14, no. 1 (2022): 99–115. http://dx.doi.org/10.5121/ijcnc.2022.14107.

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An efficient Intrusion Detection System has to be given high priority while connecting systems with a network to prevent the system before an attack happens. It is a big challenge to the network security group to prevent the system from a variable types of new attacks as technology is growing in parallel. In this paper, an efficient model to detect Intrusion is proposed to predict attacks with high accuracy and less false-negative rate by deriving custom features UNSW-CF by using the benchmark intrusion dataset UNSW-NB15. To reduce the learning complexity, Custom Features are derived and then

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More, Shweta, Moad Idrissi, Haitham Mahmoud, and A. Taufiq Asyhari. "Enhanced Intrusion Detection Systems Performance with UNSW-NB15 Data Analysis." Algorithms 17, no. 2 (2024): 64. http://dx.doi.org/10.3390/a17020064.

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The rapid proliferation of new technologies such as Internet of Things (IoT), cloud computing, virtualization, and smart devices has led to a massive annual production of over 400 zettabytes of network traffic data. As a result, it is crucial for companies to implement robust cybersecurity measures to safeguard sensitive data from intrusion, which can lead to significant financial losses. Existing intrusion detection systems (IDS) require further enhancements to reduce false positives as well as enhance overall accuracy. To minimize security risks, data analytics and machine learning can be ut

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Imran, Faisal Jamil, and Dohyeun Kim. "An Ensemble of a Prediction and Learning Mechanism for Improving Accuracy of Anomaly Detection in Network Intrusion Environments." Sustainability 13, no. 18 (2021): 10057. http://dx.doi.org/10.3390/su131810057.

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The connectivity of our surrounding objects to the internet plays a tremendous role in our daily lives. Many network applications have been developed in every domain of life, including business, healthcare, smart homes, and smart cities, to name a few. As these network applications provide a wide range of services for large user groups, the network intruders are prone to developing intrusion skills for attack and malicious compliance. Therefore, safeguarding network applications and things connected to the internet has always been a point of interest for researchers. Many studies propose solut

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Alrayes, Fatma S., Syed Umar Amin, and Nada Hakami. "An Adaptive Framework for Intrusion Detection in IoT Security Using MAML (Model-Agnostic Meta-Learning)." Sensors 25, no. 8 (2025): 2487. https://doi.org/10.3390/s25082487.

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With the rapid emergence of the Internet of Things (IoT) devices, there were new vectors for attacking cyber, so there was a need for approachable intrusion detection systems (IDSs) with more innovative custom tactics. The traditional IDS models tend to find difficulties in generalization in the continuously changing and heterogeneous IoT environments. This paper contributes to an adaptive intrusion detection framework using Model-Agnostic Meta-Learning (MAML) and few-shot learning paradigms to quickly adapt to new tasks with little data. The goal of this research is to improve the security of

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Rincy N, Thomas, and Roopam Gupta. "Design and Development of an Efficient Network Intrusion Detection System Using Machine Learning Techniques." Wireless Communications and Mobile Computing 2021 (June 26, 2021): 1–35. http://dx.doi.org/10.1155/2021/9974270.

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Today’s internets are made up of nearly half a million different networks. In any network connection, identifying the attacks by their types is a difficult task as different attacks may have various connections, and their number may vary from a few to hundreds of network connections. To solve this problem, a novel hybrid network IDS called NID-Shield is proposed in the manuscript that classifies the dataset according to different attack types. Furthermore, the attack names found in attack types are classified individually helping considerably in predicting the vulnerability of individual attac

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TÜRK, Fuat. "Analysis of Intrusion Detection Systems in UNSW-NB15 and NSL-KDD Datasets with Machine Learning Algorithms." Bitlis Eren Üniversitesi Fen Bilimleri Dergisi 12, no. 2 (2023): 465–77. http://dx.doi.org/10.17798/bitlisfen.1240469.

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Recently, the need for Network-based systems and smart devices has been increasing rapidly. The use of smart devices in almost every field, the provision of services by private and public institutions over network servers, cloud technologies and database systems are almost completely remotely controlled. Due to these increasing requirements for network systems, malicious software and users, unfortunately, are increasing their interest in these areas. Some organizations are exposed to almost hundreds or even thousands of network attacks daily. Therefore, it is not enough to solve the attacks wi

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Rakhi A. Kalantri. "Advancing Cyber Threat Detection with Ai: Cutting-Edge Techniques and Future Trends." Journal of Information Systems Engineering and Management 10, no. 14s (2025): 338–52. https://doi.org/10.52783/jisem.v10i14s.2301.

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The digital age has made cyberspace indispensable for economic, social, and governmental functions, thus intensifying the critical need for robust cybersecurity. Our increasing dependence on digital platforms has exposed systems to a wide array of sophisticated cyber threats, including malware, phishing, distributed denial-of-service (DDoS) attacks, ransomware, and insider threats, often motivated by financial gain, political agendas, or espionage. These challenges underscore the urgent requirement for flexible and resilient cybersecurity strategies. Traditional signature-based and rule-based

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Alsulami, Majid H. "Residual Dense Optimization-Based Multi-Attention Transformer to Detect Network Intrusion against Cyber Attacks." Applied Sciences 14, no. 17 (2024): 7763. http://dx.doi.org/10.3390/app14177763.

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Achieving cyber-security has grown increasingly tricky because of the rising concern for internet connectivity and the significant growth in software-related applications. It also needs a robust defense system to defend itself from multiple cyberattacks. Therefore, there is a need to generate a method for detecting and classifying cyber-attacks. The developed model can be integrated into three phases: pre-processing, feature selection, and classification. Initially, the min-max normalization of original data was performed to eliminate the impact of maximum or minimum values on the overall char

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Vibhute, Amol D., Minhaj Khan, Chandrashekhar H. Patil, Sandeep V. Gaikwad, Arjun V. Mane, and Kanubhai K. Patel. "Network anomaly detection and performance evaluation of Convolutional Neural Networks on UNSW-NB15 dataset." Procedia Computer Science 235 (2024): 2227–36. http://dx.doi.org/10.1016/j.procs.2024.04.211.

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Al-Daweri, Muataz Salam, Khairul Akram Zainol Ariffin, Salwani Abdullah, and Mohamad Firham Efendy Md. Senan. "An Analysis of the KDD99 and UNSW-NB15 Datasets for the Intrusion Detection System." Symmetry 12, no. 10 (2020): 1666. http://dx.doi.org/10.3390/sym12101666.

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The significant increase in technology development over the internet makes network security a crucial issue. An intrusion detection system (IDS) shall be introduced to protect the networks from various attacks. Even with the increased amount of works in the IDS research, there is a lack of studies that analyze the available IDS datasets. Therefore, this study presents a comprehensive analysis of the relevance of the features in the KDD99 and UNSW-NB15 datasets. Three methods were employed: a rough-set theory (RST), a back-propagation neural network (BPNN), and a discrete variant of the cuttlef

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Ahsan, Mostofa, Rahul Gomes, Md Minhaz Chowdhury, and Kendall E. Nygard. "Enhancing Machine Learning Prediction in Cybersecurity Using Dynamic Feature Selector." Journal of Cybersecurity and Privacy 1, no. 1 (2021): 199–218. http://dx.doi.org/10.3390/jcp1010011.

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Machine learning algorithms are becoming very efficient in intrusion detection systems with their real time response and adaptive learning process. A robust machine learning model can be deployed for anomaly detection by using a comprehensive dataset with multiple attack types. Nowadays datasets contain many attributes. Such high dimensionality of datasets poses a significant challenge to information extraction in terms of time and space complexity. Moreover, having so many attributes may be a hindrance towards creation of a decision boundary due to noise in the dataset. Large scale data with

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Kotecha, Ketan, Raghav Verma, Prahalad V. Rao, et al. "Enhanced Network Intrusion Detection System." Sensors 21, no. 23 (2021): 7835. http://dx.doi.org/10.3390/s21237835.

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A reasonably good network intrusion detection system generally requires a high detection rate and a low false alarm rate in order to predict anomalies more accurately. Older datasets cannot capture the schema of a set of modern attacks; therefore, modelling based on these datasets lacked sufficient generalizability. This paper operates on the UNSW-NB15 Dataset, which is currently one of the best representatives of modern attacks and suggests various models. We discuss various models and conclude our discussion with the model that performs the best using various kinds of evaluation metrics. Alo

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Deshmukh, Amogh, Peplluis Esteva de la Rosa, Raul Villamarin Rodriguez, and Sandeep Dasari. "Enhancing Privacy in IoT-Enabled Digital Infrastructure: Evaluating Federated Learning for Intrusion and Fraud Detection." Sensors 25, no. 10 (2025): 3043. https://doi.org/10.3390/s25103043.

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Challenges in implementing machine learning (ML) include expanding data resources within the finance sector. Banking data with significant financial implications are highly confidential. Diverse breaches and privacy violations can result from a combination of user information from different institutions for banking purposes. To address these issues, federated learning (FL) using a flower framework is utilized to protect the privacy of individual organizations while still collaborating through separate models to create a unified global model. However, joint training on datasets with diverse dis

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