网络入侵检测和分类系统:有监督的机器学习方法

K. A. Akintoye
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摘要

摘要:入侵检测系统(IDS)对计算机安全至关重要,因为它们能识别和打击计算机网络中的恶意活动。具体来说,基于异常的 IDS 使用根据历史数据训练的分类模型来检测这些有害活动。本文利用决策树、高斯奈夫贝叶斯、K-近邻、逻辑回归、随机森林和支持向量机,提出了一种基于机器学习模型的三级训练和测试、特征选择、重采样和归一化的增强型 IDS。在第一阶段,使用预处理后的原始数据集对六个模型进行训练和评估。在第二阶段,使用合成少数群体过度采样技术(SMOTE),用重新采样的数据集建立和测试这些模型。在第三阶段,使用重新采样并使用标准缩放方法归一化的数据集来训练和测试模型。我们采用随机森林模型的特征重要性技术,从 NSL-KDD 和 UNSW-NB15 数据集中选择基本特征。我们的研究结果超越了之前的相关研究,决策树在 UNSW-NB15 数据集上的准确度、精确度、召回率和 F1 分数均达到 99.99%。此外,决策树在 NSL-KDD 数据集上的准确率为 99.98%,精确率为 99.97%,召回率为 99.97%,F1 得分为 99.99%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Network Intrusion Detection and Classification System: A Supervised Machine Learning Approach
Abstract: Intrusion detection systems (IDSs) are crucial for computer security, as they identify and counteract malicious activities within computer networks. Anomaly-based IDSs, specifically, use classification models trained on historical data to detect these harmful activities. This paper proposes an enhanced IDS based on 3-level training and testing of machine learning models, feature selection, resampling, and normalization using Decision Tree, Gaussian Naïve Bayes, K-Nearest Neighbours, Logistic Regression, Random Forest, and Support Vector Machine. In the first stage, the six models are trained and evaluated using the original datasets after pre-processing. In the second stage, the models are built and tested with a resampled version of the dataset using the Synthetic Minority Oversampling Technique (SMOTE). In the third stage, the models are trained and tested with a dataset that has been both resampled and normalized using the standard scaling method. We employ the feature importance technique using the random forest model to select the essential features from NSL-KDD and UNSW-NB15 datasets. The results of our study surpass previous related research, with the decision tree achieving an accuracy, precision, recall, and F1 score of 99.99% on the UNSW-NB15 dataset. Additionally, the decision tree recorded an accuracy of 99.98%, precision of 99.97%, recall of 99.97%, and F1 score of 99.99% on the NSL-KDD dataset.
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