Optimizing the Classification of Network Intrusion Detection Using Ensembles of Decision Trees Algorithm

Mebawondu, Olamatanmi Josephine; Alowolodu, Olufunso Dayo; Adetunmbi, Adebayo Olusola; Mebawondu, Jacob O.

doi:10.1007/978-3-030-69143-1_23

Cited by 7 publications

(7 citation statements)

References 14 publications

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“…10, the CNN algorithm proposed in this paper has a good classification effect. Although the performance is not as good as decision tree [29], it is comparable to Logist regression [27] and better than Naive Bayes [28] and ensemble algorithm [30] for classification.…”

Section: Resultsmentioning

confidence: 89%

“…As shown in Fig. 11, in the performance of category classification, CNN is not inferior to decision tree [29], Naive Bayes [28], Logist regression [27], and ensemble algorithm [30]. More importantly, the CNN algorithm proposed in this paper combines the distributed architecture of federated learning, which can fully consider the privacy protection of users.…”

Section: Resultsmentioning

confidence: 91%

“…The research on the IDS system in this paper is to distinguish whether it is a malicious attack, or to distinguish the specific form of the attack. Different from the CNN algorithm proposed in this paper, the mainstream algorithms include decision tree [29], Naive Bayes [28], Logist regression [27], ensemble algorithm [30] and so on.…”

Section: Resultsmentioning

confidence: 99%

“…Finally, data separation is applied by forming clusters. Mebawondu et al [29] tried to optimize the IDS model by using a set of decision tree (DT) algorithms to build a network IDS. By using the C4.5 DT classifier, and the ensemble technique of bagging and AdaBoost, the performance of IDS is significantly improved.…”

Section: Related Workmentioning

confidence: 99%

See 3 more Smart Citations

Internet of Things Intrusion Detection System Based on Convolutional Neural Network

Yin¹,

Shi²,

Dong³

et al. 2023

Computers, Materials &Amp; Continua

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In recent years, the Internet of Things (IoT) technology has developed by leaps and bounds. However, the large and heterogeneous network structure of IoT brings high management costs. In particular, the low cost of IoT devices exposes them to more serious security concerns. First, a convolutional neural network intrusion detection system for IoT devices is proposed. After cleaning and preprocessing the NSL-KDD dataset, this paper uses feature engineering methods to select appropriate features. Then, based on the combination of DCNN and machine learning, this paper designs a cloud-based loss function, which adopts a regularization method to prevent overfitting. The model consists of one input layer, two convolutional layers, two pooling layers and three fully connected layers and one output layer. Finally, a framework that can fully consider the user's privacy protection is proposed. The framework can only exchange model parameters or intermediate results without exchanging local individuals or sample data. This paper further builds a global model based on virtual fusion data, so as to achieve a balance between data privacy protection and data sharing computing. The performance indicators such as accuracy, precision, recall, F1 score, and AUC of the model are verified by simulation. The results show that the model is helpful in solving the problem that the IoT intrusion detection system cannot achieve high precision and low cost at the same time.

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Section: Resultsmentioning

confidence: 89%

Section: Resultsmentioning

confidence: 91%

Section: Resultsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

See 2 more Smart Citations

Internet of Things Intrusion Detection System Based on Convolutional Neural Network

Yin¹,

Shi²,

Dong³

et al. 2023

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

show abstract

“…Boosting algorithm is adopted as a repetitive approach for producing a robust classifier, achieving a randomly small training error, from weak classifiers ensembled only from random guessing. Ensemble is employed to draw training records by repeatedly updating the sample distribution of the data that have been trained [37]. The base estimator used in this implementation is a Decision Tree classifier.…”

Section: Machine Learning Techniquesmentioning

confidence: 99%

IoT Dataset Validation Using Machine Learning Techniques for Traffic Anomaly Detection

Vigoya¹,

Fernández²,

Carneiro³

et al. 2021

Electronics

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With advancements in engineering and science, the application of smart systems is increasing, generating a faster growth of the IoT network traffic. The limitations due to IoT restricted power and computing devices also raise concerns about security vulnerabilities. Machine learning-based techniques have recently gained credibility in a successful application for the detection of network anomalies, including IoT networks. However, machine learning techniques cannot work without representative data. Given the scarcity of IoT datasets, the DAD emerged as an instrument for knowing the behavior of dedicated IoT-MQTT networks. This paper aims to validate the DAD dataset by applying Logistic Regression, Naive Bayes, Random Forest, AdaBoost, and Support Vector Machine to detect traffic anomalies in IoT. To obtain the best results, techniques for handling unbalanced data, feature selection, and grid search for hyperparameter optimization have been used. The experimental results show that the proposed dataset can achieve a high detection rate in all the experiments, providing the best mean accuracy of 0.99 for the tree-based models, with a low false-positive rate, ensuring effective anomaly detection.

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A Comprehensive Survey on Ensemble Learning-Based Intrusion Detection Approaches in Computer Networks

Lucas,

de Figueiredo,

Tojeiro

et al. 2023

IEEE Access

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Machine learning algorithms present a robust alternative for building Intrusion Detection Systems due to their ability to recognize attacks in computer network traffic by recognizing patterns in large amounts of data. Typically, classifiers are trained for this task. Together, ensemble learning algorithms have increased the performance of these detectors, reducing classification errors and allowing computer networks to be more protected. This research presents a comprehensive Systematic Review of the Literature where works related to intrusion detection with ensemble learning were obtained from the most relevant scientific bases. We offer 188 works, several compilations of datasets, classifiers, and ensemble algorithms, and document the experiments that stood out in their performance. A characteristic of this research is its originality. We found two surveys in the literature specifically focusing on the relationship between ensemble techniques and intrusion detection. We present for the last eight years covered by this survey a timeline-based view of the works studied to highlight evolutions and trends. The results obtained by our survey show a growing area, with excellent results in detecting attacks but with needs for improvement in pruning for choosing classifiers, which makes this work unprecedented for this context.

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Optimizing the Classification of Network Intrusion Detection Using Ensembles of Decision Trees Algorithm

Cited by 7 publications

References 14 publications

Internet of Things Intrusion Detection System Based on Convolutional Neural Network

Internet of Things Intrusion Detection System Based on Convolutional Neural Network

IoT Dataset Validation Using Machine Learning Techniques for Traffic Anomaly Detection

A Comprehensive Survey on Ensemble Learning-Based Intrusion Detection Approaches in Computer Networks

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