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Deep IDS : A deep learning approach for Intrusion detection based on IDS 2018

机译:深度IDS:基于IDS 2018的入侵检测深入学习方法

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Intrusion Detection is one of the fields network security important for industry 4.0. Applying deep learning models opened a new scope in this field. But availability of latest data set and volume makes it often harder to apply latest techniques. Moreover emergence of new machine learning algorithms always hold scope to improve over the existing ones. In this paper, the effectiveness of attention mechanism over the existing deep learning techniques for Intrusion detection is being proposed and a novel attention based CNN-LSTM model has been proposed based on IDS 2018 data set. A detail performance evaluation on IDS 2018 has been elaborated to establish the claim.
机译:入侵检测是工业4.0重要的网络安全领域之一。应用深度学习模型在这一领域开辟了一个新的范围。但是最新数据集和卷的可用性使得它通常更难应用最新技术。此外,新机器学习算法的出现始终保持范围以改善现有的范围。在本文中,提出了对用于入侵检测的现有深层学习技术的关注机制的有效性,并基于IDS 2018数据集提出了一种基于CNN-LSTM模型的新型关注。详细介绍了IDS 2018的绩效评估,以建立索赔。

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