Network Anomaly Detection Using LightGBM: A Gradient Boosting Classifier

Islam, Md. Khairul; Hridi, Prithula; Hossain, S.; Narman, Husnu S.

doi:10.1109/itnac50341.2020.9315049

Cited by 15 publications

(7 citation statements)

References 18 publications

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“…The linear model is limited to identifying the linear relationship between variables and targets, which is often insufficient for non-linear relationships. Hence, more complex tree models were used, such as XGB, 45 LGB, 46 and RF, 47 which are strong learners that improve with ensemble learning based on a single tree. Inspired by ensemble learning, we use the stacking idea of ensemble learning to build a multi-layer model to improve the accuracy of the prediction model.…”

Section: Model Developmentmentioning

confidence: 99%

Ensemble learning to predict solar-to-hydrogen energy conversion based on photocatalytic water splitting over doped TiO₂

Liu,

Pan,

Zhu

et al. 2023

Green Chem.

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Section: Model Developmentmentioning

confidence: 99%

Ensemble learning to predict solar-to-hydrogen energy conversion based on photocatalytic water splitting over doped TiO₂

Liu,

Pan,

Zhu

et al. 2023

Green Chem.

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“…The complete description of this dataset is shown in table 1. [19] 93.13 Zakariyya et al, [10] 89.32 Gomes et al, [20] 95.32 Singh et al, [21] 95.51 Islam et al, [22] 96.19 Singh et al, [23] 90.35 Bhuvaneswari et al, [24] 94.48 Proposed work 96.60…”

Section: ░ 4 Results and Discussionmentioning

confidence: 99%

Empowering Smart City IoT Network Intrusion Detection with Advanced Ensemble Learning-based Feature Selection

Merlin,

Ravi

2024

IJEER

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This study presents an advanced methodology tailored for enhancing the performance of Intrusion Detection Systems (IDS) deployed in Internet of Things (IoT) networks within smart city environments. Through the integration of advanced techniques in data preprocessing, feature selection, and ensemble classification, the proposed approach addresses the unique challenges associated with securing IoT networks in urban settings. Leveraging techniques such as SelectKBest, Recursive Feature Elimination (RFE), and Principal Component Analysis (PCA), combined with the Gradient-Based One Side Sampling (GOSS) technique for model training, the methodology achieves high accuracy, precision, recall, and F1 score across various evaluation scenarios. Evaluation on the UNSW-NB15 dataset demonstrates the effectiveness of the proposed approach, with comparative analysis showcasing its superiority over existing techniques.

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“…The authors of [20] presented an improved version of KDDCup'99, termed NSL-KDD, to address the previously noted issues [1]. Despite the NSL-KDD dataset's severe intrinsic challenges, such as the insufficient representation of contemporary low footprint attack scenarios, it is still regarded the most recommended IDSs assessment dataset due to its unique feature of maximizing predictions for classifiers.…”

Section: Dataset Descriptionmentioning

confidence: 99%

“…Web applications are becoming more widespread, and the internet has become an integral component of our everyday lives. As a result, there has also been increasing attention paid to the issue of network security [1]. The identification of anomalous network behavior is an important issue in network security research.…”

Section: Introductionmentioning

confidence: 99%

An Intrusion Detection System for SDN Using Machine Learning

Logeswari¹,

Bose²,

Anitha³

2023

Intelligent Automation &Amp; Soft Computing

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Software Defined Networking (SDN) has emerged as a promising and exciting option for the future growth of the internet. SDN has increased the flexibility and transparency of the managed, centralized, and controlled network. On the other hand, these advantages create a more vulnerable environment with substantial risks, culminating in network difficulties, system paralysis, online banking frauds, and robberies. These issues have a significant detrimental impact on organizations, enterprises, and even economies. Accuracy, high performance, and realtime systems are necessary to achieve this goal. Using a SDN to extend intelligent machine learning methodologies in an Intrusion Detection System (IDS) has stimulated the interest of numerous research investigators over the last decade. In this paper, a novel HFS-LGBM IDS is proposed for SDN. First, the Hybrid Feature Selection algorithm consisting of two phases is applied to reduce the data dimension and to obtain an optimal feature subset. In the first phase, the Correlation based Feature Selection (CFS) algorithm is used to obtain the feature subset. The optimal feature set is obtained by applying the Random Forest Recursive Feature Elimination (RF-RFE) in the second phase. A LightGBM algorithm is then used to detect and classify different types of attacks. The experimental results based on NSL-KDD dataset show that the proposed system produces outstanding results compared to the existing methods in terms of accuracy, precision, recall and f-measure.

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Network Anomaly Detection Using LightGBM: A Gradient Boosting Classifier

Cited by 15 publications

References 18 publications

Ensemble learning to predict solar-to-hydrogen energy conversion based on photocatalytic water splitting over doped TiO₂

Ensemble learning to predict solar-to-hydrogen energy conversion based on photocatalytic water splitting over doped TiO₂

Empowering Smart City IoT Network Intrusion Detection with Advanced Ensemble Learning-based Feature Selection

An Intrusion Detection System for SDN Using Machine Learning

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Network Anomaly Detection Using LightGBM: A Gradient Boosting Classifier

Cited by 15 publications

References 18 publications

Ensemble learning to predict solar-to-hydrogen energy conversion based on photocatalytic water splitting over doped TiO2

Ensemble learning to predict solar-to-hydrogen energy conversion based on photocatalytic water splitting over doped TiO2

Empowering Smart City IoT Network Intrusion Detection with Advanced Ensemble Learning-based Feature Selection

An Intrusion Detection System for SDN Using Machine Learning

Contact Info

Product

Resources

About

Ensemble learning to predict solar-to-hydrogen energy conversion based on photocatalytic water splitting over doped TiO₂

Ensemble learning to predict solar-to-hydrogen energy conversion based on photocatalytic water splitting over doped TiO₂