Caner, Serhat

Profile URL
https://hdl.handle.net/11147/15892
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Main Affiliation
01. Izmir Institute of Technology
Status
External
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Scholarly Output

2

Articles

2

Views / Downloads

1294/513

Supervised MSc Theses

0

Supervised PhD Theses

0

WoS Citation Count

0

Scopus Citation Count

1

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0

Projects

0

WoS Citations per Publication

0.00

Scopus Citations per Publication

0.50

Open Access Source

2

Supervised Theses

0

JournalCount
Turkish Journal of Electrical Engineering and Computer Sciences2
Current Page: 1 / 1

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Author of Publication: search.filters.isAuthorOfPublication.f022e678-be05-4615-9f5b-99ef42a7d310

Scholarly Output Search Results

Now showing 1 - 2 of 2
  • Article
    Performance Analysis and Feature Selection for Network-Based Intrusion Detection\rwith Deep Learning
    (2022) Caner, Serhat; Erten, Yusuf Murat; Erdoğmuş, Nesli; Caner, Serhat; Erten, Y. Murat; Erdoğmuş, Nesli; 03.04. Department of Computer Engineering; 01. Izmir Institute of Technology; 03. Faculty of Engineering
    An intrusion detection system is an automated monitoring tool that analyzes network traffic and detects\rmalicious activities by looking out either for known patterns of attacks or for an anomaly. In this study, intrusion\rdetection and classification performances of different deep learning based systems are examined. For this purpose, 24\rdeep neural networks with four different architectures are trained and evaluated on CICIDS2017 dataset. Furthermore,\rthe best performing model is utilized to inspect raw network traffic features and rank them with respect to their\rcontributions to success rates. By selecting features with respect to their ranks, sets of varying size from 3 to 77 are\rassessed in terms of classification accuracy and time efficiency. The results show that recurrent neural networks with a\rcertain level of complexity can achieve comparable success rates with state-of-the-art systems using a small feature set\rof size 9; while the average time required to classify a test sample is halved compared to the complete set.
  • Article
    Citation - Scopus: 1
    Performance Analysis and Feature Selection for Network-Based Intrusion Detection With Deep Learning
    (Türkiye Klinikleri, 2022) Caner, Serhat; Erdoğmuş, Nesli; Erdoğmuş, Nesli; Caner, Serhat; Erten, Yusuf Murat; 03.04. Department of Computer Engineering; 01. Izmir Institute of Technology; 03. Faculty of Engineering
    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.