ABBA: A quasi-deterministic Intrusion Detection System for the Internet of Things
This addresses security risks in automated processes like automotive and healthcare, but appears incremental as it builds on existing detection techniques.
The paper tackles the problem of detecting data tampering in IoT networks without relying on cryptographic keys or accurate network modeling, proposing a new mechanism called ABBA and analyzing its mathematical structure and algorithm for implementation.
An increasing amount of processes are becoming automated for increased efficiency and safety. Common examples are in automotive, industrial control systems or healthcare. Automation usually relies on a network of sensors to provide key data to control systems. One potential risk to these automated processes comes from fraudulent data injected in the network by malicious actors. In this article we propose a new mechanism of data tampering detection that does not depend on secret cryptographic keys - that can be lost or stolen - or accurate modelling of the network as is the case with existing machine learning based techniques. We define and analyse the mathematical structure of the proposed technique called ABBA and propose an algorithm for implementation.