Retracted: Quantum-Assisted Activation for Supervised Learning in Healthcare-Based Intrusion Detection Systems
Why is this work in the frame?
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Post-publication record
OpenAlex flags this work as retracted, but it carries no matching Retraction Watch record in this frame.
Abstract
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 (patient’s 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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
The record
- Venue
- IEEE Transactions on Artificial Intelligence
- Topic
- Network Security and Intrusion Detection
- Field
- Computer Science
- Canadian institutions
- École de Technologie Supérieure
- Funders
- —
- Keywords
- Intrusion detection systemComputer scienceHealth careQuantumArtificial intelligencePhysicsPolitical science
- Has abstract in OpenAlex
- yes