Integrating intelligence into intrusion detection tools has received much attention in the last years. The goal is to improve the detection capability within SIEM and IDS systems in order to cope with the increasing number of attacks using sophisticated and complex methods to infiltrate systems. Current SIEM and IDS systems have many processes involved, which work together to collect, analyze, detect, and send notification of failures in real time. Event normalization, for example, requires significant processing power to handle network events. So, adding heavy deep learning models will invoke additional resources for the SIEM or IDS tool. This paper presents a majority system based on reliability approach that combines simple feedforward neural networks, as weak learners, and produces high detection capability with low computation resources. The experimental results show that the model is very suitable for modeling a classification model with high accuracy and that its performance is superior to that of complex resource-intensive deep learning models.

中文翻译：

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SIEM / IDS系统中基于神经网络的高容量，低计算量的入侵检测投票系统

近年来，将智能集成到入侵检测工具中已引起广泛关注。目的是提高SIEM和IDS系统中的检测能力，以便使用复杂的复杂方法渗透系统来应对不断增长的攻击。当前的SIEM和IDS系统涉及许多过程，这些过程协同工作以实时收集，分析，检测和发送故障通知。例如，事件规范化需要大量处理能力来处理网络事件。因此，添加繁重的深度学习模型将为SIEM或IDS工具调用其他资源。本文提出了一种基于可靠性方法的多数系统，该系统结合了简单的前馈神经网络（作为弱学习者），并以较低的计算资源产生了较高的检测能力。