Intrusion detection system combined enhanced random forest with SMOTE algorithm

Wu, Tao; Fan, Huiqing; Zhu, Hongqing; You, Congzhe; Zhou, Hongyan; Huang, Xianzhen

doi:10.1186/s13634-022-00871-6

Cited by 41 publications

(11 citation statements)

References 20 publications

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“…Lastly, the synthetic minority oversampling technique (SMOTE) is implemented [ 58 , 59 ] to address class imbalance. SMOTE oversamples the minority classes in the dataset by generating synthetic samples, resulting in a more balanced distribution of classes.…”

Section: Proposed Network Intrusion Detection Frameworkmentioning

confidence: 99%

Enhancing Network Intrusion Detection Using an Ensemble Voting Classifier for Internet of Things

Farooqi,

Akhtar,

Rahman

et al. 2023

Sensors

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In the context of 6G technology, the Internet of Everything aims to create a vast network that connects both humans and devices across multiple dimensions. The integration of smart healthcare, agriculture, transportation, and homes is incredibly appealing, as it allows people to effortlessly control their environment through touch or voice commands. Consequently, with the increase in Internet connectivity, the security risk also rises. However, the future is centered on a six-fold increase in connectivity, necessitating the development of stronger security measures to handle the rapidly expanding concept of IoT-enabled metaverse connections. Various types of attacks, often orchestrated using botnets, pose a threat to the performance of IoT-enabled networks. Detecting anomalies within these networks is crucial for safeguarding applications from potentially disastrous consequences. The voting classifier is a machine learning (ML) model known for its effectiveness as it capitalizes on the strengths of individual ML models and has the potential to improve overall predictive performance. In this research, we proposed a novel classification technique based on the DRX approach that combines the advantages of the Decision tree, Random forest, and XGBoost algorithms. This ensemble voting classifier significantly enhances the accuracy and precision of network intrusion detection systems. Our experiments were conducted using the NSL-KDD, UNSW-NB15, and CIC-IDS2017 datasets. The findings of our study show that the DRX-based technique works better than the others. It achieved a higher accuracy of 99.88% on the NSL-KDD dataset, 99.93% on the UNSW-NB15 dataset, and 99.98% on the CIC-IDS2017 dataset, outperforming the other methods. Additionally, there is a notable reduction in the false positive rates to 0.003, 0.001, and 0.00012 for the NSL-KDD, UNSW-NB15, and CIC-IDS2017 datasets.

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Section: Proposed Network Intrusion Detection Frameworkmentioning

confidence: 99%

Enhancing Network Intrusion Detection Using an Ensemble Voting Classifier for Internet of Things

Farooqi,

Akhtar,

Rahman

et al. 2023

Sensors

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“…The RF method can be used for both classification and regression problems because of these characteristics. It is effective for processing large numbers of data, avoids the overfitting problem that deepens the noise of the model, and improves the accuracy of the model to reduce the variability of the prediction [8].…”

Section: Rf Characteristicsmentioning

confidence: 99%

Malicious File Detection Method Using Machine Learning and Interworking with MITRE ATT&CK Framework

et al. 2022

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With advances in cyber threats and increased intelligence, incidents continue to occur related to new ways of using new technologies. In addition, as intelligent and advanced cyberattack technologies gradually increase, the limit of inefficient malicious code detection and analysis has been reached, and inaccurate detection rates for unknown malicious codes are increasing. Thus, this study used a machine learning algorithm to achieve a malicious file detection accuracy of more than 99%, along with a method for visualizing data for the detection of malicious files using the dynamic-analysis-based MITRE ATT&CK framework. The PE malware dataset was classified into Random Forest, Adaboost, and Gradient Boosting models. These models achieved accuracies of 99.3%, 98.4%, and 98.8%, respectively, and malicious file analysis results were derived through visualization by applying the MITRE ATT&CK matrix.

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“…The random-forest algorithm is applied to many variables with hard-to-analyze relationships www.ijacsa.thesai.org [29][30][31]. Random Forest were applied by recent studies in detecting intrusion attacks [32][33][34][35].…”

Section: B Machine Learningmentioning

confidence: 99%

Machine Learning for Securing Traffic in Computer Networks

BaniMustafa¹,

Baklizi²,

Khatatneh³

2022

IJACSA

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Computer network attacks are among the most significant and common threats against computer-wired and wireless communications. Intrusion detection technology is used to secure computer networks by monitoring network traffic and identifying attacks. In this paper, we investigate and evaluate the application of four machine learning classification algorithms for identifying attacks that target computer networks: DDoS, Brute Force Web, and SQL Injection attacks, in addition to Benign Traffic. A public dataset of 80 features was used to build four machine learning models using Random Forest, Logistic Regression, CN2, and Neural Networks. The constructed models were evaluated based on 10-fold cross-validation using Classification Accuracy (CA), Area under the Curve (AUC), F1, Recall, Specificity, and Sensitivity metrics in addition to Confusion Matrix, Calibration, Lift, and ROC plots. The Random Forest model achieved 98% in the CA score and 99% in the AUC score, while the Logistic regression achieved 90% in the CA score and 98% in the AUC score.

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Intrusion detection system combined enhanced random forest with SMOTE algorithm

Cited by 41 publications

References 20 publications

Enhancing Network Intrusion Detection Using an Ensemble Voting Classifier for Internet of Things

Enhancing Network Intrusion Detection Using an Ensemble Voting Classifier for Internet of Things

Malicious File Detection Method Using Machine Learning and Interworking with MITRE ATT&CK Framework

Machine Learning for Securing Traffic in Computer Networks

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