TY  - JOUR
TI  - Performance analysis and feature selection for network-based intrusion detection
with deep learning
AB  - An intrusion detection system is an automated monitoring tool that analyzes network traffic and detects
malicious activities by looking out either for known patterns of attacks or for an anomaly. In this study, intrusion
detection and classification performances of different deep learning based systems are examined. For this purpose, 24
deep neural networks with four different architectures are trained and evaluated on CICIDS2017 dataset. Furthermore,
the best performing model is utilized to inspect raw network traffic features and rank them with respect to their
contributions to success rates. By selecting features with respect to their ranks, sets of varying size from 3 to 77 are
assessed in terms of classification accuracy and time efficiency. The results show that recurrent neural networks with a
certain level of complexity can achieve comparable success rates with state-of-the-art systems using a small feature set
of size 9; while the average time required to classify a test sample is halved compared to the complete set.
AU  - ERDOĞMUŞ, Nesli
AU  - ERTEN, Y. Murat
AU  - CANER, Serhat
DO  - 10.3906/elk-2104-50
PY  - 2022
JO  - Turkish Journal of Electrical Engineering and Computer Sciences
VL  - 30
IS  - 3
SN  - 1300-0632
SP  - 629
EP  - 643
DB  - TRDizin
UR  - http://search/yayin/detay/528806
ER  -