Biblio

Cyber-physical systems (CPS) consist of sensors, actuators, and controllers all communicating over a network; if any subset becomes compromised, an attacker could cause significant damage. With access to data logs and a model of the CPS, the physical effects of an attack could potentially be detected before any damage is done. Manually building a model that is accurate enough in practice, however, is extremely difficult. In this paper, we propose a novel approach for constructing models of CPS automatically, by applying supervised machine learning to data traces obtained after systematically seeding their software components with faults ("mutants"). We demonstrate the efficacy of this approach on the simulator of a real-world water purification plant, presenting a framework that automatically generates mutants, collects data traces, and learns an SVM-based model. Using cross-validation and statistical model checking, we show that the learnt model characterises an invariant physical property of the system. Furthermore, we demonstrate the usefulness of the invariant by subjecting the system to 55 network and code-modification attacks, and showing that it can detect 85% of them from the data logs generated at runtime.

This study has built a simulation of a smart home system by the Alibaba ECS. The architecture of hardware was based on edge computing technology. The whole method would design a clear classifier to find the boundary between regular and mutation codes. It could be applied in the detection of the mutation code of network. The project has used the dataset vector to divide them into positive and negative type, and the final result has shown the RBF-function SVM method perform best in this mission. This research has got a good network security detection in the IoT systems and increased the applications of machine learning.