Calculating EER with anomaly detection using LSTM in python

Calculating Equal Error Rate (EER) with anomaly detection using Long Short-Term Memory (LSTM) in Python involves implementing a model that can classify normal and anomalous sequences and then determining the point at which the False Acceptance Rate (FAR) equals the False Rejection Rate (FRR). In this tutorial, we'll walk through the process step by step, and provide a Python code example using the TensorFlow and Keras libraries.
Make sure you have the necessary libraries installed. You can install them using the following commands:
Assume you have a dataset with normal and anomalous sequences. Load the data and preprocess it:
Note: Ensure your dataset is representative and that you adjust parameters, such as sequence length and percentage of anomalous sequences, based on your specific use case. Additionally, you might want to experiment with different architectures and hyperparameters for the LSTM model to achieve better results.
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