Stuart Millar

Stuart Millar

The use of IoT in society is perhaps already ubiquitous, with a vast attack surface offering multiple opportunities for malicious actors. This short paper first presents an introduction to IoT and its security issues, including an overview of IoT layer models and topologies, IoT standardisation efforts and protocols. The focus then moves to IoT vulnerabilities and specific suggestions for mitigations. This work's intended audience are those relatively new to IoT though with existing network-related knowledge. It is concluded that device resource constraints and a lack of IoT standards are significant issues. Research opportunities exist to develop efficient IoT IDS and energy-saving cryptography techniques lightweight enough to reasonably deploy. The need for standardised protocols and channel-based security solutions is clear, underpinned by legislative directives to ensure high standards that prevent cost-cutting on the device manufacturing side.

Roberto Doriguzzi-Corin, Stuart Millar, Sandra Scott-Hayward et al.

Distributed Denial of Service (DDoS) attacks are one of the most harmful threats in today's Internet, disrupting the availability of essential services. The challenge of DDoS detection is the combination of attack approaches coupled with the volume of live traffic to be analysed. In this paper, we present a practical, lightweight deep learning DDoS detection system called LUCID, which exploits the properties of Convolutional Neural Networks (CNNs) to classify traffic flows as either malicious or benign. We make four main contributions; (1) an innovative application of a CNN to detect DDoS traffic with low processing overhead, (2) a dataset-agnostic preprocessing mechanism to produce traffic observations for online attack detection, (3) an activation analysis to explain LUCID's DDoS classification, and (4) an empirical validation of the solution on a resource-constrained hardware platform. Using the latest datasets, LUCID matches existing state-of-the-art detection accuracy whilst presenting a 40x reduction in processing time, as compared to the state-of-the-art. With our evaluation results, we prove that the proposed approach is suitable for effective DDoS detection in resource-constrained operational environments.