Context and rationale
Intrusion Detection, the ability to detect malware and other attacks, is a crucial aspect to ensure cybersecurity. So is the ability to identify this myriad of attacks.
Artificial Neural Networks (as well as other machine learning bio-inspired approaches) are an established and proven method of accurate classification. ANNs are extremely versatile – a wide range of setups can achieve significantly different classification results. The main objective and contribution of this paper is the evaluation of the way the hyperparameters can influence the final classification result.
Method and results
In this paper, a wide range of ANN setups is put to comparison. We have performed our experiments on two benchmark datasets, namely NSL-KDD and CICIDS2017.
The most effective arrangement achieves the multi-class classification accuracy of 99.909% on an established benchmark dataset.
- Michał Choraś
FernUniversität in Hagen | UTP University of Science and Technology
Hagen, Germany | Bydgoszcz, Poland
- Marek Pawlicki
ITTI Sp. z o.o. | UTP University of Science and Technology
Poznań, Poland | Bydgoszcz, Poland