| Autor: |
Nikhil Laxminarayana, Nimish Mishra, Prayag Tiwari, Sahil Garg, Bikash K. Behera, Ahmed Farouk |
| Přispěvatelé: |
Indian Institute of Information Technology Allahabad, Department of Computer Science, École de technologie supérieure, Bikash's Quantum (OPC) Private Limited, South Valley University, Aalto-yliopisto, Aalto University |
| Rok vydání: |
2022 |
| Předmět: |
|
| Zdroj: |
IEEE Transactions on Artificial Intelligence. :1-8 |
| ISSN: |
2691-4581 |
| Popis: |
Intrusion detection systems (IDS) 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 over-fitting on training data or using overly complex architectures such as convolutional neural networks (CNNs), long-short term memory systems (LSTMs), and recurrent neural networks (RNNs). This paper 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. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
|
