  The popularity of IoT smart things is rising, due to the automation they
provide and its effects on productivity. However, it has been proven that IoT
devices are vulnerable to both well established and new IoT-specific attack
vectors. In this paper, we propose the Particle Deep Framework, a new network
forensic framework for IoT networks that utilised Particle Swarm Optimisation
to tune the hyperparameters of a deep MLP model and improve its performance.
The PDF is trained and validated using Bot-IoT dataset, a contemporary
network-traffic dataset that combines normal IoT and non-IoT traffic, with well
known botnet-related attacks. Through experimentation, we show that the
performance of a deep MLP model is vastly improved, achieving an accuracy of
99.9% and false alarm rate of close to 0%.
