ABBA: A quasi-deterministic Intrusion Detection System for the Internet of Things

TL;DR

ABBA is a novel intrusion detection system for IoT networks that detects data tampering without relying on cryptographic keys or detailed network models, enhancing security in automated systems.

Contribution

The paper introduces ABBA, a new quasi-deterministic method for detecting data tampering in IoT, independent of secret keys and complex network modeling.

Findings

ABBA effectively detects data tampering in IoT networks.

The method does not depend on cryptographic keys, reducing key management issues.

ABBA's mathematical foundation enables reliable detection without extensive network training.

Abstract

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.