@inproceedings{b9c7648a80614f7592e2f6c18226f922,
title = "Deep Learning-Based Detection of Cyberattacks in Software-Defined Networks",
abstract = "This paper presents deep learning models for binary and multiclass intrusion classification problems in Software-defined-networks (SDN). The induced models are evaluated by the state-of-the-art dataset, InSDN. We applied Convolutional Autoencoder (CNN-AE) for high-level feature extraction, and Multi-Layer Perceptron (MLP) for classification that delivers high-performance metrics of F1-score, accuracy and recall compared to similar studies. Highly imbalanced datasets such as InSDN underperform in detecting the instances belonging to the minority class. We use Synthetic Minority Oversampling Technique (SMOTE) to address dataset imbalance and observe a significant detection enhancement in the detection of minority classes.",
keywords = "Dataset Balancing, Deep Learning, Intrusion Detection, Software-Defined Network",
author = "{Hadi Mirsadeghi}, {Seyed Mohammad} and Hayretdin Bahsi and Wissem Inbouli",
note = "Publisher Copyright: {\textcopyright} ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2023.; 13th EAI International Conference on Digital Forensics and Cyber Crime, ICDF2C 2022 ; Conference date: 16-11-2022 Through 18-11-2022",
year = "2023",
doi = "10.1007/978-3-031-36574-4_20",
language = "English (US)",
isbn = "9783031365737",
series = "Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "341--354",
editor = "Sanjay Goel and Akatyev Nikolay and Daryl Johnson and Pavel Gladyshev and George Markowsky",
booktitle = "Digital Forensics and Cyber Crime - 13th EAI International Conference, ICDF2C 2022, Proceedings",
address = "Germany",
}