A CNN-LSTM Model for Intrusion Detection System from High Dimensional Data

Network protection is an essential part of attack detection. Machine learning
algorithms play an important role in the current Intrusion Detection. However, these
algorithms are suffering with low accuracy and detection rate. Deep learning is another
sophisticated technique to solve these challenges because intrusion detection
performance is not strong in traditional machine learning systems. This article examines
network intrusion detection using a Convolutional Neural Network (CNN) and LSTM.
The integrated folding and grouping operations are used to derive the relationship of the
features between the results. The model should automatically determine the efficient
properties of the intrusion samples so that the intrusion samples can be classified
accurately. Experimental tests with KDD99 data sets suggest that the proposed model
will significantly increase intrusion detection performance.