Optimal feature selection for machine learning based intrusion detection system by exploiting attribute dependence

Dubey, Ghanshyam Prasad; Bhujade, Rakesh Kumar

doi:10.1016/j.matpr.2021.04.643

Cited by 18 publications

(6 citation statements)

References 20 publications

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“…We use the Spearman correlation coefficient to measure the correlation between features. Proposed by Spearman in 1904, it measures the strength of the relationship between two variables [ 16 ], and it takes values in the range (−1, 1). The Spearman correlation coefficient between variables x i and y i is calculated as

where x i ( i =1,2,…, n ) and y i ( i =1,2,…, n ) are elements of the vectors X and Y , respectively.…”

Section: Background and Related Workmentioning

confidence: 99%

Network Intrusion Detection Method Based on FCWGAN and BiLSTM

Song

et al. 2022

Computational Intelligence and Neuroscience

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Imbalanced datasets greatly affect the analysis capability of intrusion detection models, biasing their classification results toward normal behavior and leading to high false-positive and false-negative rates. To alleviate the impact of class imbalance on the detection accuracy of network intrusion detection models and improve their effectiveness, this paper proposes a method based on a feature selection-conditional Wasserstein generative adversarial network (FCWGAN) and bidirectional long short-term memory network (BiLSTM). The method uses the XGBoost algorithm with Spearman’s correlation coefficient to select the data features, filters out useless and redundant features, and simplifies the data structure. A conditional WGAN (CWGAN) is used to generate a small number of samples in the dataset, add them to the original training set to supplement the dataset samples, and apply BiLSTM to complete the training of the model and realize the classification. In comparative tests based on the NSL-KDD and UNSW-NB15 datasets, the accuracy of the proposed model reached 99.57% and 85.59%, respectively, which is 1.44% and 2.98% higher than that of the same type of CWGAN and deep neural network (CWGAN-DNN) model, respectively.

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where x i ( i =1,2,…, n ) and y i ( i =1,2,…, n ) are elements of the vectors X and Y , respectively.…”

Section: Background and Related Workmentioning

confidence: 99%

Network Intrusion Detection Method Based on FCWGAN and BiLSTM

Song

et al. 2022

Computational Intelligence and Neuroscience

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Section: Introductionmentioning

confidence: 99%

Enhancing Intrusion Detection through Unsupervised Deep Learning Models

S.P.

2024

IJSREM

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The Internet of Things (IoT) has experienced significant growth since its inception, representing a groundbreaking technological advancement. In essence, IoT involves the seamless integration of devices and data to automate and centralize various processes. This transformative technology is revolutionizing business operations and reshaping society as a whole. As IoT continues to evolve, the importance of detecting vulnerabilities and weaknesses becomes paramount in order to thwart unauthorized access to critical resources and business functions, which could potentially render the entire system unavailable. One prevalent threat in this context is Denial of Service (DoS) and Distributed DoS attacks. In this project, propose an innovative architecture known as Protocol Based Deep Intrusion Detection (PB-DID). To create our dataset, we gathered packets from IoT traffic and compared features from two well-known datasets: UNSWNB15 and Bot-IoT. We focused on flow and Transmission Control Protocol (TCP) characteristics. Our primary objective was to accurately classify network traffic into three categories: non-anomalous, DoS, and DDoS, while addressing issues like data imbalance and overfitting. Utilizing deep learning (DL) techniques, we achieved an impressive classification accuracy of 96.3%. This level of accuracy demonstrates the potential of our PB-DID architecture in effectively identifying and mitigating intrusion attempts in the context of IoT, thereby enhancing the security and reliability of IoT systems. Keywords—: Intrusion detection in IoT, Deep learning for intrusion detection, DoS detection, DDoS detection.

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“…One effective approach to address feature redundancy is feature selection. Feature selection plays a crucial role in machine learning-based intrusion detection systems, reducing the dimensionality of the dataset, lowering training time and computational costs, while improving model performance [10]. Recently, metaheuristic algorithms have gained considerable attention in the field of feature selection due to their excellent global search capabilities [11].…”

Section: Introductionmentioning

confidence: 99%

IoT Intrusion Detection System Based on Machine Learning

Xu,

Sun,

Mao

et al. 2023

Electronics

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With the rapid development of the Internet of Things (IoT), the number of IoT devices is increasing dramatically, making it increasingly important to identify intrusions on these devices. Researchers are using machine learning techniques to design effective intrusion detection systems. In this study, we propose a novel intrusion detection system that efficiently detects network anomalous traffic. To reduce the feature dimensions of the data, we employ the binary grey wolf optimizer (BGWO) heuristic algorithm and recursive feature elimination (RFE) to select the most relevant feature subset for the target variable. The synthetic minority oversampling technique (SMOTE) is used to oversample the minority class and mitigate the impact of data imbalance on the classification results. The preprocessed data are then classified using XGBoost, and the hyperparameters of the model are optimized using Bayesian optimization with tree-structured Parzen estimator (BO-TPE) to achieve the highest detection performance. To validate the effectiveness of the proposed method, we conduct binary and multiclass experiments on five commonly used IoT datasets. The results show that our proposed method outperforms state-of-the-art methods in four out of the five datasets. It is noteworthy that our proposed method achieves perfect accuracy, precision, recall, and an F1 score of 1.0 on the BoT-Iot and WUSTL-IIOT-2021 datasets, further validating the effectiveness of our approach.

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Optimal feature selection for machine learning based intrusion detection system by exploiting attribute dependence

Cited by 18 publications

References 20 publications

Network Intrusion Detection Method Based on FCWGAN and BiLSTM

Network Intrusion Detection Method Based on FCWGAN and BiLSTM

Enhancing Intrusion Detection through Unsupervised Deep Learning Models

IoT Intrusion Detection System Based on Machine Learning

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