Improved DDoS Detection Utilizing Deep Neural Networks and Feedforward Neural Networks as Autoencoder

Ahmed Latif Yaser (), Hamdy M. Mousa and Mahmoud Hussein
Additional contact information
Ahmed Latif Yaser: Computer Science Department, Faculty of Computers and Information, Menoufia University, Shebin Elkom 32511, Egypt
Hamdy M. Mousa: Computer Science Department, Faculty of Computers and Information, Menoufia University, Shebin Elkom 32511, Egypt
Mahmoud Hussein: Computer Science Department, Faculty of Computers and Information, Menoufia University, Shebin Elkom 32511, Egypt

Future Internet, 2022, vol. 14, issue 8, 1-18

Abstract: Software-defined networking (SDN) is an innovative network paradigm, offering substantial control of network operation through a network’s architecture. SDN is an ideal platform for implementing projects involving distributed applications, security solutions, and decentralized network administration in a multitenant data center environment due to its programmability. As its usage rapidly expands, network security threats are becoming more frequent, leading SDN security to be of significant concern. Machine-learning (ML) techniques for intrusion detection of DDoS attacks in SDN networks utilize standard datasets and fail to cover all classification aspects, resulting in under-coverage of attack diversity. This paper proposes a hybrid technique to recognize denial-of-service (DDoS) attacks that combine deep learning and feedforward neural networks as autoencoders. Two datasets were analyzed for the training and testing model, first statically and then iteratively. The auto-encoding model is constructed by stacking the input layer and hidden layer of self-encoding models’ layer by layer, with each self-encoding model using a hidden layer. To evaluate our model, we use a three-part data split (train, test, and validate) rather than the common two-part split (train and test). The resulting proposed model achieved a higher accuracy for the static dataset, where for ISCX-IDS-2012 dataset, accuracy reached a high of 99.35% in training, 99.3% in validation and 99.99% in precision, recall, and F1-score. for the UNSW2018 dataset, the accuracy reached a high of 99.95% in training, 0.99.94% in validation, and 99.99% in precision, recall, and F1-score. In addition, the model achieved great results with a dynamic dataset (using an emulator), reaching a high of 97.68% in accuracy.

Keywords: autoencoder; denial-of-service (DDoS); deep neural network; DDoS detection; software-defined network (SDN) (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
https://www.mdpi.com/1999-5903/14/8/240/pdf (application/pdf)
https://www.mdpi.com/1999-5903/14/8/240/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jftint:v:14:y:2022:i:8:p:240-:d:887251

Access Statistics for this article

Future Internet is currently edited by Ms. Grace You

More articles in Future Internet from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().