Study on Poisoning Attacks: Application Through an IoT Temperature Dataset
Floribert Katembo Vuseghesa, Mohamed‐Lamine Messai · 2023
The past decade presents a massive adoption of machine learning in divers domains. This fact has been greatly facilitated by cloud computing, which has made high-performance computing capabilities and data storage accessible to organizations of all sizes. This article provides an in-depth examination of data poisoning attacks and their impact on the security of machine learning methods. To achieve our objective, four types of attacks were tested on a dataset obtained from connected devices in an Internet of Things (IoT) application that collects temperature readings. These attacks include data modification, data deletion, label flipping and sponge poisoning attack. These four types of attacks primarily aim to compromise the integrity and availability of the learning models. In this study, we create a Python script that randomly selects a set of nodes from the IoT network as nodes controlled by an attacker. These compromised nodes send corrupted data to initiate a poisoning attack. The results of the model trained on the attacked data were then compared with those of the model trained on normal data assumed to be unharmed.