Research Article
Shoal: A Network Level Moving Target Defense Engine with Software Defined Networking
@ARTICLE{10.4108/eai.1-6-2021.170011, author={Li Wang}, title={Shoal: A Network Level Moving Target Defense Engine with Software Defined Networking}, journal={EAI Endorsed Transactions on Security and Safety}, volume={7}, number={25}, publisher={EAI}, journal_a={SESA}, year={2021}, month={6}, keywords={Moving Target Defense, SDN, security strategy, network security}, doi={10.4108/eai.1-6-2021.170011} }
- Li Wang
Year: 2021
Shoal: A Network Level Moving Target Defense Engine with Software Defined Networking
SESA
EAI
DOI: 10.4108/eai.1-6-2021.170011
Abstract
Moving Target Defense (MTD) was proposed as a promising defense paradigm to introduce various uncertainties into computer systems, which can greatly raise the bar for the attackers. Currently, there are two classes of MTD research over computer system, system level MTD and network level MTD. System level MTD research introduces uncertainties to various aspects of computer systems; while network level MTD research brings unpredictability of network properties to the target network. A lot of network level MTD research has been proposed, which covers various aspects of computer network. However, the existing MTD approaches usually target on one aspect of computer network, and most of them are designed against a certain network security threat. They can hardly defend against complex attacks or provide complicated protections. In this paper, we propose Shoal, a Moving Target Defense engine with multiple MTD strategies over SDN networks.By applying hybrid and multiple network level MTD methods, Shoal is capable of providing complicated protections and defending advanced attacks. We evaluate Shoal in two advanced protection scenarios, moving target surface and Crossfire attack. The evaluation results, in term of security effectiveness and performance cost, show the protection provided by Shoal’s hybrid MTD methods is effective and the performance cost is relatively low.
Copyright © 2021 Li Wang, licensed to EAI. This is an open access article distributed under the terms of the Creative Commons Attribution license (http://creativecommons.org/licenses/by/3.0/), which permits unlimited use, distribution and reproduction in any medium so long as the original work is properly cited.