Build LSTM Model for Detecting Malicious Nodes in Proof-of-Stake Blockchain

[Karm-Dave]

Issue Title: Develop LSTM Model for Malicious Node Detection in Proof-of-Stake Blockchain Dataset

Description:
We are using a dataset of over 10,000 entries that contains node data from a Proof-of-Stake (PoS) blockchain. The dataset was initially developed with 303 semi-synthetic entries and later augmented using Python to simulate more data. It includes both malicious and non-malicious nodes, making it ideal for detecting abnormal node behavior on a PoS blockchain.

Objective:
The task is to implement a Long Short-Term Memory (LSTM) model to detect malicious nodes in the dataset. The LSTM model will analyze node behavior over time and classify nodes as either malicious or non-malicious based on the sequential data provided.

Key Requirements:

• LSTM Model: Design and train an LSTM model to classify nodes as malicious or non-malicious based on blockchain node data.
• Data Preprocessing: Ensure the dataset is properly prepared for time series modeling, including normalization and reshaping of data for LSTM input.
• Model Training: Train the LSTM model on the augmented dataset, splitting it into training and testing sets.
• Evaluation: Use performance metrics such as accuracy, precision, recall, and F1-score to evaluate the model’s effectiveness in detecting malicious nodes.
• Visualization: Provide visualizations for the training process (e.g., accuracy and loss curves) and classification performance (e.g., confusion matrix).

Tech Stack:

• Python
• TensorFlow/Keras (for LSTM implementation)
• pandas and numpy (for data preprocessing)
• matplotlib or seaborn (for visualization)

This is a crucial project for blockchain security research, especially for analyzing and improving the behavior of nodes in PoS blockchains. Please assign this issue to me for development under GSSOC Extd 2024.

[github-actions]

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[github-actions]

Thanks for raising this issue! However, we believe a similar issue already exists. Kindly go through all the open issues and ask to be assigned to that issue.

[UppuluriKalyani]

@Karm-Dave Assigned