计算机科学
入侵检测系统
卷积神经网络
人工智能
循环神经网络
人工神经网络
量子
深度学习
机器学习
物理
量子力学
作者
Nikhil Laxminarayana,Nimish Mishra,Prayag Tiwari,Sahil Garg,Bikash K. Behera,Ahmed Farouk
出处
期刊:IEEE transactions on artificial intelligence
[Institute of Electrical and Electronics Engineers]
日期:2022-01-01
卷期号:: 1-8
被引量:11
标识
DOI:10.1109/tai.2022.3187676
摘要
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.
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