  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.
