Defending SDN against packet injection attacks using deep learning

- Phu, Anh, Li, Bo, Ullah, Faheem, Ul Huque, Tanvir, Naha, Ranesh, Babar, Muhammad, Nguyen, Hung

Title
Defending SDN against packet injection attacks using deep learning
Creator
Phu, Anh; Li, Bo; Ullah, Faheem; Ul Huque, Tanvir; Naha, Ranesh; Babar, Muhammad; Nguyen, Hung
Date
2023
Type
Text; Journal article
Identifier
http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/198319
Identifier
vital:19050
Identifier
https://doi.org/10.1016/j.comnet.2023.109935
Identifier
ISSN:1389-1286 (ISSN)
Abstract
The (logically) centralized architecture of software-defined networks makes them an easy target for packet injection attacks. In these attacks, the attacker injects malicious packets into the SDN network to affect the services and performance of the SDN controller and overflows the capacity of the SDN switches. Such attacks have been shown to ultimately stop the network functioning in real-time, leading to network breakdowns. There have been significant works on detecting and defending against similar DoS attacks in non-SDN networks, but detection and protection techniques for SDN against packet injection attacks are still in their infancy. Furthermore, many of the proposed solutions have been shown to be easily bypassed by simple modifications to the attacking packets or by altering the attacking profile. In this paper, we develop novel Graph Convolutional Neural Network models and algorithms for grouping network nodes/users into security classes by learning from network data. We start with two simple classes — nodes that engage in suspicious packet injection attacks and nodes that are not. From these classes, we then partition the network into separate segments with different security policies using distributed Ryu controllers in an SDN network. We show in experiments on an emulated SDN that our detection solution outperforms alternative approaches with above 99% detection accuracy for various types (both old and new) of injection attacks. More importantly, our mitigation solution maintains continuous functions of non-compromised nodes while isolating compromised/suspicious nodes in real-time. All code and data are publicly available for the reproducibility of our results. © 2023 The Author(s)
Publisher
Elsevier B.V.
Relation
Computer Networks Vol. 234, no. (2023), p.
Rights
All metadata describing materials held in, or linked to, the repository is freely available under a CC0 licence
Rights
http://creativecommons.org/licenses/by/4.0/
Rights
Copyright © 2023 The Author(s)
Rights
Open Access
Subject
40 Engineering; 46 Information and computing sciences; Attack detection; Network security; Neural network; Packet injection attack; Software-defined networking
Full Text
Reviewed
Funder
Tanvir ul Huque acknowledges the support of the Commonwealth of Australia and Cybersecurity Research Centre Limited, Australia
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