A CNN-LSTM Model for Intrusion Detection System from High Dimensional Data
K. Prasanna · Zenodo (CERN European Organization for Nuclear Research) · 2020
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.