Evaluating hybrid quantum-classical deep learning for cybersecurity botnet DGA detection

Hybrid quantum machine learning (QML) algorithms have potentials for current quantum computing technologies since only part of the model is computed by a quantum device. This paper proposes a novel hybrid quantum-classical deep learning model for cybersecurity application: domain generation algorithms (DGA)-based botnet detection. We analyzed our hybrid model's performance compared with the classical model counterpart to investigate the quantum circuit's effectivity as a layer in a deep learning model. We used four features of the Botnet DGA dataset: MinREBotnets, CharLength, TreeNewFeature, and nGramReputation_Alexa. The hybrid model's quantum circuit is a combination of Pennylane's embedding (Angle Embedding and IQP Embedding) and layers circuit (Basic Entangler Layers, Random Layers, and Strongly Entangling Layers). Also, we implemented noise models for assessing the applicability of the model for current Noisy Intermediate Scale Quantum (NISQ) technology. We found that in some cases, the hybrid model reached high performance (maximum accuracy up to 94,7% using n=100; 93,9% using n=1,000). We discovered that the combination of Angle Embedding and Strongly Entangled delivers high accuracy, superior to the classical deep learning model in n=100 experiments). However, the overall performance is still inferior for the rest cases compared to the classical deep learning model counterpart.