Visualization and Processing of Information Security Events Based on Cicids Data 17

Zelichenok, I.U.

doi:10.31854/2307-1303-2021-9-4-49-55

Cited by 2 publications

(1 citation statement)

References 4 publications

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“…Another critical step is to examine the data for anomalies or outliers that can skew the work's findings. This might comprise looking for patterns or anomalies in the data visually or using statistical methods to identify outliers [44]. To guarantee that all characteristics are on the same scale and increase the model's accuracy, the data must be normalized.…”

Section: Data Pre-processingmentioning

confidence: 99%

Random Forest-Based NIDS: Advancing Network Threat Detection

Darweesh,

Abdelaziz,

Radwan

et al. 2024

Preprint

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Network Intrusion Detection Systems (NIDS) are critical for protecting computer networks from unauthorized activities. Traditional NIDS rely on rule-based signatures, which can be limiting in detecting emerging threats. This study investigates the effectiveness of the random forest classifier in advancing NIDS capabilities through machine learning. Using the CICIDS-2017 dataset, the data is preprocessed to enhance its quality by removing redundancies. The methodology involves rigorous testing and analysis of the random forest classifier's performance, focusing on accuracy and detection rates compared to other machine learning models. Results demonstrate that by optimizing class weights and leveraging 15 key features, the random forest classifier achieves an outstanding 99.8% accuracy across various attack types. This research highlights the potential of machine learning to significantly enhance NIDS effectiveness, offering a robust defense mechanism against evolving cybersecurity threats in modern networks.

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Section: Data Pre-processingmentioning

confidence: 99%

Random Forest-Based NIDS: Advancing Network Threat Detection

Darweesh,

Abdelaziz,

Radwan

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

Enhancing Network Threat Detection with Random Forest-Based NIDS and Permutation Feature Importance

Abdelaziz,

Radwan,

Mamdouh

et al. 2024

J Netw Syst Manage

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Network Intrusion Detection Systems (NIDS) are critical for protecting computer networks from unauthorized activities. Traditional NIDS rely on rule-based signatures, which can be limiting in detecting emerging threats. This study investigates the effectiveness of the random forest classifier in advancing NIDS capabilities through machine learning. Using the CICIDS-2017 dataset, the data are preprocessed to enhance their quality by removing redundancies. feature selection and permutation importance were employed to identify the most relevant features. The methodology involves rigorous testing and analysis of the random forest classifier’s performance, focusing on f1-score rates compared to other machine learning models. Results demonstrate that by optimizing class weights, applying a custom prediction function and leveraging 26 key features, the random forest classifier achieves an outstanding 99.8% in the weighted f1-score and 93.31% in the macro f1-score in various attack types. This research highlights the potential of machine learning to significantly enhance NIDS effectiveness, offering a robust defense mechanism against evolving cybersecurity threats in modern networks.

show abstract

Visualization and Processing of Information Security Events Based on Cicids Data 17

Cited by 2 publications

References 4 publications

Random Forest-Based NIDS: Advancing Network Threat Detection

Random Forest-Based NIDS: Advancing Network Threat Detection

Enhancing Network Threat Detection with Random Forest-Based NIDS and Permutation Feature Importance

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