Network attack classification using LSTM with XGBoost feature selection

Poornima, R.; Elangovan, M; Nagarajan, G.

doi:10.3233/jifs-212731

Cited by 5 publications

(2 citation statements)

References 22 publications

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“…We then classified the categorical items into 5, 10, and 6 categories, including normal traffic. Tables 17-19 present a performance comparison between the intrusion detection model based on feature extraction and various machine learning algorithms for multi-classification tasks on the three datasets [58][59][60]. It can be inferred from the table that the proposed integrated model outperforms other strategies in terms of overall performance metrics after completing feature engineering on the dataset, as well as hyperparameter tuning, overcoming the challenges of high latitude and class imbalance encountered in intrusion detection, and efficiently detecting intrusions on the three most well-represented datasets.…”

Section: • Multi Classificationmentioning

confidence: 99%

Feature Engineering and Model Optimization Based Classification Method for Network Intrusion Detection

Zhang,

Wang

2023

Applied Sciences

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In light of the escalating ubiquity of the Internet, the proliferation of cyber-attacks, coupled with their intricate and surreptitious nature, has significantly imperiled network security. Traditional machine learning methodologies inherently exhibit constraints in effectively detecting and classifying multifarious cyber threats. Specifically, the surge in high-dimensional network traffic data and the imbalanced distribution of classes exacerbate the predicament of ideal classification performance. Notably, the presence of redundant information within network traffic data undermines the accuracy of classifiers. To address these challenges, this study introduces a novel approach for intrusion detection classification which integrates advanced techniques of feature engineering and model optimization. The method employs a feature engineering approach that leverages mutual information maximum correlation minimum redundancy (mRMR) feature selection and synthetic minority class oversampling technique (SMOTE) to process network data. This transformation of raw data into more meaningful features effectively addresses the complexity and diversity inherent in network data, enhancing classifier accuracy by reducing feature redundancy and mitigating issues related to class imbalance and the detection of rare attacks. Furthermore, to optimize classifier performance, the paper applies the Optuna method to fine-tune the hyperparameters of the Catboost classifier, thereby determining the optimal model configuration. The study conducts binary and multi-classification experiments using publicly available datasets, including NSL_KDD, UNSW-NB15, and CICIDS-2017. Experimental results demonstrate that the proposed method outperforms traditional approaches regarding accuracy, recall, precision, and F-value. These findings highlight the method’s potential and performance in network intrusion detection.

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Section: • Multi Classificationmentioning

confidence: 99%

Feature Engineering and Model Optimization Based Classification Method for Network Intrusion Detection

Zhang,

Wang

2023

Applied Sciences

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“…By extracting the characteristics of the point cloud cluster and using a machine learning method to detect the motion state of each point cloud cluster in each frame point cloud, high-precision and high-efficiency LiDAR dynamic target detection is achieved. Among many machine learning algorithms, XGBoost [ 24 ] has the advantages of flexibility, accuracy, and efficiency and has been optimized by relevant experts and scholars from the perspectives of data processing, multilabel classification, and hyperparameter tuning [ 25 , 26 , 27 , 28 ]. Therefore, this algorithm is widely used to address various classification and regression problems [ 29 , 30 , 31 ].…”

Section: Introductionmentioning

confidence: 99%

LiDAR Dynamic Target Detection Based on Multidimensional Features

Xu,

Gao,

Sui

et al. 2024

Sensors

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To address the limitations of LiDAR dynamic target detection methods, which often require heuristic thresholding, indirect computational assistance, supplementary sensor data, or postdetection, we propose an innovative method based on multidimensional features. Using the differences between the positions and geometric structures of point cloud clusters scanned by the same target in adjacent frame point clouds, the motion states of the point cloud clusters are comprehensively evaluated. To enable the automatic precision pairing of point cloud clusters from adjacent frames of the same target, a double registration algorithm is proposed for point cloud cluster centroids. The iterative closest point (ICP) algorithm is employed for approximate interframe pose estimation during coarse registration. The random sample consensus (RANSAC) and four-parameter transformation algorithms are employed to obtain precise interframe pose relations during fine registration. These processes standardize the coordinate systems of adjacent point clouds and facilitate the association of point cloud clusters from the same target. Based on the paired point cloud cluster, a classification feature system is used to construct the XGBoost decision tree. To enhance the XGBoost training efficiency, a Spearman’s rank correlation coefficient-bidirectional search for a dimensionality reduction algorithm is proposed to expedite the optimal classification feature subset construction. After preliminary outcomes are generated by XGBoost, a double Boyer–Moore voting-sliding window algorithm is proposed to refine the final LiDAR dynamic target detection accuracy. To validate the efficacy and efficiency of our method in LiDAR dynamic target detection, an experimental platform is established. Real-world data are collected and pertinent experiments are designed. The experimental results illustrate the soundness of our method. The LiDAR dynamic target correct detection rate is 92.41%, the static target error detection rate is 1.43%, and the detection efficiency is 0.0299 s. Our method exhibits notable advantages over open-source comparative methods, achieving highly efficient and precise LiDAR dynamic target detection.

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Predicting DoS and DDoS attacks in network security scenarios using a hybrid deep learning model

Al-zubidi,

Farhan,

Towfek

2024

Journal of Intelligent Systems

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Network security faces increasing threats from denial of service (DoS) and distributed denial of service (DDoS) attacks. The current solutions have not been able to predict and mitigate these threats with enough accuracy. A novel and effective solution for predicting DoS and DDoS attacks in network security scenarios is presented in this work by employing an effective model, called CNN-LSTM-XGBoost, which is an innovative hybrid approach designed for intrusion detection in network security. The system is applied and analyzed to three datasets: CICIDS-001, CIC-IDS2017, and CIC-IDS2018. We preprocess the data by removing null and duplicate data, handling imbalanced data, and selecting the most relevant features using correlation-based feature selection. The system is evaluated using accuracy, precision, F1 score, and recall. The system achieves a higher accuracy of 98.3% for CICIDS-001, 99.2% for CICIDS2017, and 99.3% for CIC-ID2018, compared to other existing algorithms. The system also reduces the overfitting of the model using the most important features. This study shows that the proposed system is an effective and efficient solution for network attack detection and classification.

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Network attack classification using LSTM with XGBoost feature selection

Cited by 5 publications

References 22 publications

Feature Engineering and Model Optimization Based Classification Method for Network Intrusion Detection

Feature Engineering and Model Optimization Based Classification Method for Network Intrusion Detection

LiDAR Dynamic Target Detection Based on Multidimensional Features

Predicting DoS and DDoS attacks in network security scenarios using a hybrid deep learning model

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