Quantum-Assisted Activation for Supervised Learning in Healthcare-based Intrusion Detection Systems

Intrusion detection systems (IDS) are amongst the most important automated defense mechanisms in modern industry. It is guarding against many attack vectors, especially healthcare, where sensitive information (patient’s medical history, prescriptions, electronic health records, bills/debts, and other data points) open to compromise from adversaries. In big era, classical machine learning has been applied train IDS. However, IDS tend be complex: either using several hidden layers susceptible over-fitting on training or overly complex architectures such as convolutional neural networks (CNNs), long-short term memory (LSTMs), recurrent (RNNs). This paper explored combination of principles quantum mechanics A hybrid classical-quantum architecture proposed with a quantum-assisted activation function that successfully captures patterns dataset while having less architectural footprint than solutions. The experimental results demonstrated popular KDD99 comparing our solution models.