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首页> 外文期刊>Informatica: An International Journal of Computing and Informatics >Detection and Recognition of Abnormal Data Caused by Network Intrusion Using Deep Learning
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Detection and Recognition of Abnormal Data Caused by Network Intrusion Using Deep Learning

机译：利用深度学习检测和识别网络侵扰引起的异常数据

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Based on deep learning, this study combines sparse autoencoder (SAE) with extreme learning machine (ELM) to design an SAE-ELM method to reduce the dimension of data features and realize the classification of different types of data. Experiments were carried out on NSL-KDD and UNSW-NB2015 data sets. The results show that, compared to the K-means algorithm and the SVM algorithm, the proposed method has higher performance. On the NSL-KDD data set, the average accuracy rate of the SAE-ELM method was 98.93%, the false alarm rate was 0.17%, and the missing report rate was 5.36%. , The accuracy rate of the SAE-ELM method on the UNSW-NB2015 data set was 98.88%, the false alarm rate was 0.12%, and the missing report rate was 4.31%. The results show that the SAE-ELM method is effective in the detection and recognition of abnormal data and can be popularized and applied.
机译：基于深度学习，本研究结合了稀疏的AutoEncoder（SAE）与极端学习机（ELM）来设计SAE-ELM方法，以减少数据特征的维度，并实现不同类型数据的分类。 在NSL-KDD和UNSW-NB2015数据集上进行实验。 结果表明，与K均值算法和SVM算法相比，所提出的方法具有更高的性能。 在NSL-KDD数据集上，SAE-ELM方法的平均精度率为98.93％，误报率为0.17％，缺失的报告率为5.36％。 ，SAE-ELM方法对UNSW-NB2015数据集的精度率为98.88％，误报率为0.12％，缺失的报告率为4.31％。 结果表明，SAE-ELM方法在检测和识别异常数据中是有效的，并且可以普及和应用。

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