Reference no: EM133784115

Question: Please use the UAV CAN dataset

Use the following steps in Quantum Neural Networks on this dataset: Extract temporal patterns, such as message intervals, frequency, and sequence patterns, from CAN messages.

You may need to normalize and standardize these features to prepare for embedding. Choose a suitable quantum feature map, such as amplitude encoding or angle encoding, to represent the temporal features.

This step will help us to map the classical data into a quantum state space. Transform the encoded features into quantum states. For instance, use angle encoding by representing each temporal feature as a rotation angle on qubits. We can use this step to create a high-dimensional representation of temporal patterns that QNNs can learn from. Build a quantum kernel function, enabling the QNN to calculate similarities between embedded states.

Set up the QNN with a variational quantum circuit, trained on labeled CAN data patterns (normal vs. attack). Test the QNN on unseen CAN data to assess its ability to generalize and accurately detect anomalies based on temporal patterns.