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首页> 外文期刊>Quality Control, Transactions >Evaluation of Cybersecurity Data Set Characteristics for Their Applicability to Neural Networks Algorithms Detecting Cybersecurity Anomalies
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Evaluation of Cybersecurity Data Set Characteristics for Their Applicability to Neural Networks Algorithms Detecting Cybersecurity Anomalies

机译：网络安全数据集特征对神经网络算法的适用性检测网络安全异常的特征

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摘要

Artificial intelligence algorithms have a leading role in the field of cybersecurity and attack detection, being able to present better results in some scenarios than classic intrusion detection systems such as Snort or Suricata. In this sense, this research focuses on the evaluation of characteristics for different well-established Machine Leaning algorithms commonly applied to IDS scenarios. To do this, a categorization for cybersecurity data sets that groups its records into several groups is first considered. Making use of this division, this work seeks to determine which neural network model (multilayer or recurrent), activation function, and learning algorithm yield higher accuracy values, depending on the group of data. Finally, the results are used to determine which group of data from a cybersecurity data set are more relevant and representative for the intrusion detection, and the most suitable configuration of Machine Learning algorithm to decrease the computational load of the system.
机译：人工智能算法在网络安全和攻击检测领域具有主导作用，能够在某些情况下提供比经典入侵检测系统（例如Snort或Suricata）的效果更好。从这个意义上讲，这项研究侧重于对常用于IDS场景的不同既定机器倾斜算法的特征评估。为此，首先考虑将其记录分组为几个组的网络安全数据集的分类。利用本司，这项工作寻求确定哪种神经网络模型（多层或复发性），激活功能和学习算法产生更高的精度值，具体取决于数据组。最后，结果用于确定来自网络安全数据集的哪一组数据对于入侵检测，以及机器学习算法的最合适配置来降低系统的计算负荷。

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