Quantum-Assisted activation for supervised learning in healthcare-based intrusion detection systems

Intrusion detection systems (IDSs) are amongst the most important automated defense mechanisms in modern industry. It is guarding against many attack vectors, especially in healthcare, where sensitive information (patients medical history, prescriptions, electronic health records, medical bills/debts, and many other sensitive data points) is open to compromise from adversaries. In the big data era, classical machine learning has been applied to train IDS. However, classical IDS tend to be complex: either using several hidden layers susceptible to overfitting on training data or using overly complex architectures such as convolutional neural networks, long-short term memory systems, and recurrent neural networks. This article explored the combination of principles of quantum mechanics and neural networks to train IDS. A hybrid classical-quantum neural architecture is proposed with a quantum-Assisted activation function that successfully captures patterns in the dataset while having less architectural memory footprint than classical solutions. The experimental results are demonstrated on the popular KDD99 dataset while comparing our solution to other classical models. Impact Statement Intrusion detection systems are dynamic defenses against network breach attacks. Lately, machine learning has been leveraged to perform automated intrusion detection. However, classical machine learning needs a large amount of data and overly complex architectures to "learn" patterns from data. In this work, we leverage machine learning concepts to derive a novel quantum activation function that greatly simplifies neural architectural complexity while achieving same level of accuracy and performance. Our architecture is much simpler than state-ofthe-Art based classical systems, simpler to train, and easy to handle.