Skip to main content

An Architecture for Resilient Intrusion Detection in IoT Networks.

Qurashi, M.A., Angelopoulos, C.M. and Katos, V., 2020. An Architecture for Resilient Intrusion Detection in IoT Networks. In: IEEE International Conference on Communications (ICC), 7-11 Jun 2020, Dublin (Online).

Full text available as:

[img]
Preview
PDF
ICC_2020.pdf - Published Version
Available under License Creative Commons Attribution Non-commercial.

220kB

Official URL: https://icc2020.ieee-icc.org/

DOI: 10.1109/ICC40277.2020.9148868

Abstract

We introduce a lightweight architecture of Intrusion Detection Systems (IDS) for ad-hoc IoT networks. Current state-of-the-art IDS have been designed based on assumptions holding from conventional computer networks, and therefore, do not properly address the nature of IoT networks. In this work, we first identify the correlation between the communication overheads and the placement of an IDS (as captured by proper placement of active IDS agents in the network). We model such networks as Random Geometric Graphs. We then introduce a novel IDS architectural approach by having only a minimum subset of the nodes acting as IDS agents. These nodes are able to monitor the network and detect attacks at the networking layer in a collaborative manner by monitoring 1-hop network information provided by routing protocols such as RPL. Conducted experiments show that our proposed IDS architecture is resilient and robust against frequent topology changes due to node failures. Our detailed experimental evaluation demonstrates significant performance gains in terms of communication overhead and energy dissipation while maintaining high detection rates.

Item Type:Conference or Workshop Item (Paper)
ISSN:1550-3607
Group:Faculty of Science & Technology
ID Code:36305
Deposited By: Unnamed user with email symplectic@symplectic
Deposited On:29 Nov 2021 10:57
Last Modified:29 Nov 2021 10:57

Downloads

Downloads per month over past year

More statistics for this item...
Repository Staff Only -