Generating Synthetic Data for Real World Detection of DoS Attacks in the IoT
This addresses the challenge of data scarcity for IoT security, enabling more efficient training of detection systems, though it appears incremental as it builds on existing synthetic data generation methods.
The paper tackles the problem of detecting denial-of-service attacks in IoT networks by proposing a model-hybrid approach to generate synthetic data for training network intrusion detection systems, resulting in more predictive datasets created in a fraction of the time compared to standard techniques.
Denial of service attacks are especially pertinent to the internet of things as devices have less computing power, memory and security mechanisms to defend against them. The task of mitigating these attacks must therefore be redirected from the device onto a network monitor. Network intrusion detection systems can be used as an effective and efficient technique in internet of things systems to offload computation from the devices and detect denial of service attacks before they can cause harm. However the solution of implementing a network intrusion detection system for internet of things networks is not without challenges due to the variability of these systems and specifically the difficulty in collecting data. We propose a model-hybrid approach to model the scale of the internet of things system and effectively train network intrusion detection systems. Through bespoke datasets generated by the model, the IDS is able to predict a wide spectrum of real-world attacks, and as demonstrated by an experiment construct more predictive datasets at a fraction of the time of other more standard techniques.