Hybrid Quantum-Classical Neural Network for Cloud-supported In-Vehicle Cyberattack Detection

@article{Islam2021HybridQN,
  title={Hybrid Quantum-Classical Neural Network for Cloud-supported In-Vehicle Cyberattack Detection},
  author={Mhafuzul Islam and Mashrur A. Chowdhury and Zadid Khan and Sakib Mahmud Khan},
  journal={ArXiv},
  year={2021},
  volume={abs/2110.07467}
}
A classical computer works with ones and zeros, whereas a quantum computer uses ones, zeros, and superpositions of ones and zeros, which enables quantum computers to perform a vast number of calculations simultaneously compared to classical computers. In a cloud-supported cyber-physical system environment, running a machine learning application in quantum computers is often difficult, due to the existing limitations of the current quantum devices. However, with the combination of quantum… 

Figures and Tables from this paper

References

SHOWING 1-10 OF 10 REFERENCES
Classification with Quantum Neural Networks on Near Term Processors
TLDR
A quantum neural network, QNN, that can represent labeled data, classical or quantum, and be trained by supervised learning, is introduced and it is shown through classical simulation that parameters can be found that allow the QNN to learn to correctly distinguish the two data sets.
TensorFlow Quantum: A Software Framework for Quantum Machine Learning
TLDR
This framework offers high-level abstractions for the design and training of both discriminative and generative quantum models under TensorFlow and supports high-performance quantum circuit simulators.
Quantum Computing Methods for Supervised Learning
TLDR
This paper provides a background and summarize key results of quantum computing before exploring its application to supervised machine learning problems, and aims to make this introduction accessible to data scientists, machine learning practitioners, and researchers from across disciplines.
In-vehicle network intrusion detection using deep convolutional neural network
TLDR
This paper proposes an intrusion detection system (IDS) based on a deep convolutional neural network (DCNN) to protect the CAN bus of the vehicle and demonstrates that the proposed IDS has significantly low false negative rates and error rates when compared to the conventional machine-learning algorithms.
OTIDS: A Novel Intrusion Detection System for In-vehicle Network by Using Remote Frame
TLDR
This paper proposes an intrusion detection method based on the analysis of the offset ratio and time interval between request and response messages in CAN that allows quick intrusion detection with high accuracy.
Long Short-Term Memory Neural Network-Based Attack Detection Model for In-Vehicle Network Security
TLDR
A long short-term memory (LSTM) neural-network-based model for detecting replay attack and amplitude-shift attack is developed and shows improvement in accuracy, precision, recall, and AUPRC over the baseline detection models considered.
Commercial Cloud Computing for Connected Vehicle Applications in Transportation Cyberphysical Systems: A Case Study
TLDR
Through real-world experiments, it is demonstrated how commercial cloud services, along with a serverless cloud architecture, can advance the transportation digital infrastructure for supporting CV mobility applications in a TCPS environment.
Integration of big data for querying CAN bus data from connected car
TLDR
Design steps to take in consideration when implementing MapReduce patterns to analyses CAN bus data in order to produce useful data that is hosted in the cloud are studied and experiment results show that Map Reduce join algorithm is highly scalable and optimized for distributed computing than Statistical Analysis System (SAS) framework and HiveQL declarative language.
Transfer learning in hybrid classical-quantum neural networks
TLDR
This work uses the cross-platform software library PennyLane to experimentally test a high-resolution image classifier with two different quantum computers, respectively provided by IBM and Rigetti, to propose different implementations of hybrid transfer learning.
GitHub -commaai/opendbc: democratize access to car decoder rings