This module explores IoT security through the lens of machine learning and data analysis. Smart devices and IoT systems generate vast amounts of data — this project investigates how that data can be used to predict user behaviour, detect threats, and protect critical infrastructure from adversarial exploitation using neural networks and ML algorithms.
As a cybersecurity specialist, understanding how IoT data is collected and processed is critical for protecting individuals and nations from data theft and manipulation. This project uses an anonymous IoT dataset to build machine learning models — including a Neural Network and Convolutional Neural Network — that predict user actions from IoT device data. The work demonstrates practical application of ML to IoT security challenges including threat detection and resilience against adversarial attacks.
Neural Network and CNN models built to predict user behaviour from IoT device data.
Evaluation of how adversaries exploit IoT data including Trojan apps and device manipulation.
GPS and IoT datasets processed and analysed to extract actionable security insights.
Assessment of IoT risks to critical infrastructure and recommendations for resilience.
Full academic documentation including research, implementation details, testing methodology, and evaluation.
Download Word Document →GPS Dataset (CSV)
Download GPS Dataset →Neural Network (Python)
Download Neural Network →Convolutional Neural Network (Python)
Download CNN ML →