An Apriori-based Data Analysis on Suspicious Network Event Recognition
An Apriori-based Data Analysis on Suspicious Network Event Recognition
复制标题
基于Apriori的可疑网络事件识别数据分析
DOI：
10.1109/bigdata47090.2019.9006420
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发表时间：
2019
期刊：
影响因子：
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通讯作者：
M. H. Hassan
中科院分区：
文献类型：
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作者：
Zhiwen Jian;H. Sakai;J. Watada;Arunava Roy;M. H. Hassan
基于Apriori的规则生成器由DIS-Apriori算法和NIS-Apriori算法提供支持，用于分析IEEE BigData 2019 Cup：可疑网络事件识别中可用的数据集。然后，利用得到的规则对测试数据集中的每个缺失值进行判定。我们的基于规则的模型的优点是，所获得的规则是非常容易理解的，与其他“黑箱”机器学习模型相比。此外，两个算法保持了逻辑属性“完整性”，因此它们生成的规则没有多余和不足。在评估中，AUC测量似乎对我们的模型不利，因此我们对训练数据集进行了3倍交叉验证，我们获得了94%的平均得分。这一结果保证了模型的有效性。我们在这个实验中报告了几个有意义的结果，以及缺失值的估计。
Apriori-based rule generators, which are powered by the DIS-Apriori algorithm and the NIS-Apriori algorithm, are applied to analyze the data sets available in the IEEE BigData 2019 Cup: Suspicious Network Event Recognition. Then, each missing value in the test data set is decided by using the obtained rules. The advantage of our rule-based model is that the obtained rules are very easy to understand in comparison with other ”black-box” machine learning models. Furthermore, two algorithms preserve the logical property ”completeness,” so they generate rules without excess and deficiency. In evaluation, the AUC measure seems unfavorable to our model, so we employed 3-fold cross-validation for the training data set, and we obtained a 94% mean score. This result ensures the validity of our model. We report several meaningful results in this experiment, as well as the estimation of missing values.