ANN Based Intrusion Detection Model
Anomaly based Intrusion Detection Systems (IDSs) are known to achieve high accuracy and detection rate. However, a significant computational overhead is incurred in training and deploying them. In this paper, we aim to address this issue by proposing a simple Artificial Neural Network (ANN) based IDS model. The ANN based IDS model uses the feed forward and the back propagation algorithms along with various other optimization techniques to minimize the overall computational overhead, while at the same time maintain a high performance level. Experimental results on the benchmark CICIDS2017 dataset shows that the performance (i.e., detection accuracy) of the ANN based IDS model. Owing to its high performance and low computational overhead, the ANN with Adam optimizer based IDS model is a suitable candidate for real time deployment and intrusion detection analysis.
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. NRF-2017R1C1B5017556).
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