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

被引:12
|
作者
Dey, Arunavo [1 ]
机构
[1] Bangladesh Univ Business & Technol, Dept Comp Sci & Engn, Dhaka, Bangladesh
关键词
Intrusion Detection; Anomaly Detection; Attention Mechanism; Neural Network; LSTM; CNN;
D O I
10.1109/STI50764.2020.9350411
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
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.
引用
收藏
页数:5
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