XGBoost for IDS on WSN Cyber Attacks with Imbalanced Data

Putrada, Aji Gautama; Alamsyah, Nur; Pane, Syafrial Fachri; Fauzan, Mohamad Nurkamal

doi:10.1109/isesd56103.2022.9980630

Cited by 14 publications

(8 citation statements)

References 26 publications

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“…Some studies show that XGBoost is better than others tree-model-based due to its ensemble classifier. Putrada et al [26] proposed and successfully implemented XGBoost for IDS on the WSN cyberattack dataset with imbalanced data. Deepak et al [27] had the best-generalized model to detect real-time cyber-attacks using XGBoost.…”

Section: Related Workmentioning

confidence: 99%

Optimizing Feature Selection Method in Intrusion Detection System Using Thresholding

2024

IJIES

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Information and communication technology is growing rapidly, making it the target of various attacks. The attacks can be in the form of data theft, phishing, and Denial of Service (DoS). There are many ways to handle attacks on communication networks, including developing an Intrusion Detection System (IDS) model. Research on IDS has developed a lot and focuses on certain things such as feature selection, dealing with data imbalance problems. Feature selection is essential to the IDS model because of the dataset's characteristics, which have many features. Besides, the number of features included in the classification can affect the detection performance of the IDS model. This research proposes an IDS combining mutual information with thresholding feature selection and XGBoost classification algorithm. Mutual information is used to measure the dependency between every input feature and the target features. After the amount of information is obtained with mutual information, thresholding is used to decide the best number of features in the classification process. Then, the data are classified using XGBoost selected features. The proposed method was tested using four metrics: accuracy, precision, recall, and f1-score. This study used UNSW-NB15 as the primary dataset to analyze the best combinations of feature selection method and thresholding value. In addition, the proposed method has also been tested using NSL-KDD and CIC-IDS2017 datasets to evaluate the performance compared with previous research. The proposed method performs best using the CIC-IDS2017 dataset with 99.89 % accuracy and 99.68 % F1 score. Furthermore, it can reduce computational training time compared with other IDS methods that only use feature selection or tree-model-based algorithms without thresholds.

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Section: Related Workmentioning

confidence: 99%

Optimizing Feature Selection Method in Intrusion Detection System Using Thresholding

2024

IJIES

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“…tree. Then, the final result of the model is a majority vote from all weak learners [19]. Finally, CNN is a type of deep learning where one type of sublayer is the convolutional layer, namely a kernel that carries out the convolution process on the input data [20].…”

Section: The Cima Windowing Algorithmmentioning

confidence: 99%

“…In a dataset, an imbalance can occur when the number of one label is much greater than the other labels [21]. The impact of imbalance is twofold [22].…”

Section: The Cima Windowing Algorithmmentioning

confidence: 99%

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Human Activity Recognition Improvement on Smartphone Accelerometers Using Cima

Putrada,

Abdurohman,

Perdana

et al. 2024

tektrika

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Human activity recognition (HAR) is a research field that focuses on detecting user activities and has wide applications. However, the problems that need to be solved are real-time constraints and imbalanced datasets due to different activity frequencies. Our research aims to apply classification integrated moving averages (CIMA) to HAR by evaluating its performance regarding real-time constraints and imbalanced datasets. We achieved the smartphone accelerometer dataset from Kaggle, which consists of several activities: walking, jogging, climbing, and descending stairs. We develop a general CIMA windowing algorithm with hyperparameters J and W. We benchmark CIMA with two state-of-the-art HAR methods: distributed online activity recognition system (DOLARS) and convolutional neural network (CNN). We conducted some imbalance and model size analysis. The test results show that, with J = 10 and W = 240, CIMA performs better than DOLARS and CIMA with recall, precision, and f1-score of 0.996, 0.993, and 0.994. We also prove that CIMA, assisted by quantization, has the smallest model size compared to the CNN and DOLARS model sizes. Finally, we demonstrate that CIMA performs well for imbalanced datasets, where CIMA’s recall on upstairs and downstairs activities is better than DOLARS and CNN, with values of 1.00 and 0.98, respectively. Key Words: classification integrated moving average, human activity recognition, smartphone, accelerometer, imbalanced dataset

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“…There are now such well machine learning-based [16][17][18] intrusion detection methods have been developed for WSNs, which includes decision trees, random forests, naive Bayes, logistic regression, and deep learning models. Most of the existing works [19,20] facing the problems associated to the factors of ineffective detection performance, high false positives, computational burden, and complexity in intrusion detection. Thus, the proposed work aims to develop an effective and competent IDS framework for assuring the security of WSNs.…”

Section: Introductionmentioning

confidence: 99%

An Intelligent Prairie Dog Optimization (IPDO) and Deep Auto-Neural Network (DANN) based IDS for WSN Security

et al. 2023

ijcsm

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Wireless sensor networks (WSNs) are targets of intrusion, which seeks to make these networks less capable of performing their duties or even completely eradicate them. The Intrusion Detection System (IDS) is highly important for WSN, since it aids in the identification and detection of harmful attacks that impair the network's regular functionality. In order to strengthen the security of WSN, several machine learning and deep learning approaches are employed in the traditional works. However, its main drawbacks are computational burden, system complexity, poor network performance outcomes, and high false alarms. Therefore, the goal of this study is to develop an intelligent IDS framework for significantly enhancing WSN security through the use of deep learning model. Here, the min-max normalization and data discretization operations are carried out to produce the preprocessed dataset. Then, an Intelligent Prairie Dog Optimization (IPDO) algorithm is used to reduce the dimensionality of features by identifying the best optimal solution with a higher convergence rate. Moreover, a Deep Auto-Neural Network (DANN) based classification method is used to properly forecast the class of data with less false alarms and higher detection rate. For evaluation, a thorough analysis is conducted to evaluate the performance and detection results of the proposed IPDO-DANN model.

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XGBoost for IDS on WSN Cyber Attacks with Imbalanced Data

Cited by 14 publications

References 26 publications

Optimizing Feature Selection Method in Intrusion Detection System Using Thresholding

Optimizing Feature Selection Method in Intrusion Detection System Using Thresholding

Human Activity Recognition Improvement on Smartphone Accelerometers Using Cima

An Intelligent Prairie Dog Optimization (IPDO) and Deep Auto-Neural Network (DANN) based IDS for WSN Security

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