Enhancing network forensics with particle swarm and deep learning: The particle deep framework
This addresses network forensics for IoT devices, which are vulnerable to attacks, but it is incremental as it combines existing optimization and deep learning methods.
The paper tackles the problem of IoT network security by proposing the Particle Deep Framework, which uses Particle Swarm Optimization to tune hyperparameters of a deep MLP model, achieving 99.9% accuracy and near 0% false alarm rate on the Bot-IoT dataset.
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%.