ChainGuard: Anomaly & Botnet Detection in Blockchain Networks

ChainGuard is an advanced detection system aimed at identifying various anomalies within blockchain transactions, including sybil botnets, suspicious wallet behaviors, and abnormal transaction patterns. By leveraging behavior analysis, statistical modeling, and machine learning techniques, ChainGuard flags irregular activities, such as unusually high transaction volumes or repetitive small transfers, which could indicate manipulation or fraud. The project aims to enhance the security and transparency of blockchain ecosystems by detecting and mitigating threats in real-time.

Project is under development

Here’s a step-by-step project plan to achieve ChainGuard: Anomaly & Botnet Detection in Blockchain Networks project:

Step 1: Define Scope & Objectives

1. Goal: Identify transaction anomalies and sybil botnets on blockchain networks (e.g., TON network).

2. Focus Areas:

Suspicious wallet behavior (unusual transaction patterns).

Detection of sybil botnets (coordinated attacks or manipulations).

General anomalies like abnormal volumes, unusual timing of transactions, or wallet clusters.

Step 2: Data Collection

1. API Setup: Use blockchain APIs to gather transaction data.

Utilize Arkham's or TON’s API to extract wallet activity, transaction history, and other metadata.

Choose the relevant API for either mainnet or testnet based on your experiment.

2. Key Parameters to Fetch:

Wallet addresses and corresponding transactions.

Transaction time, volume, and frequency.

Metadata, such as sender and receiver details.

3. Tools:

Use Python for scripting the data extraction.

Save fetched data in CSV or JSON format for easier analysis.

Step 3: Data Cleaning & Preprocessing

1. Filter Data:

Exclude normal, low-risk transactions to reduce noise.

Identify repetitive or cyclical transactions that suggest sybil or botnet activities.

2. Format Data:

Convert data into structured formats like Pandas DataFrames (Python).

Create new fields such as time between transactions, transaction size ratio, etc.

Step 4: Implement Anomaly Detection Algorithms

1. Define Anomalies:

Sudden spikes in transaction volumes.

Unusually high frequency of small transactions.

Multiple wallets interacting within the same time window (indicates a botnet).

2. Techniques:

Statistical Models: Identify outliers using basic statistical methods (mean, standard deviation, z-scores).

Machine Learning Models:

Clustering (e.g., K-means): Group wallets based on transaction behaviors.

Anomaly Detection Algorithms: Use models like Isolation Forests or Autoencoders to identify anomalies.

3. Example:

Use libraries such as scikit-learn for outlier detection.

Step 5: Sybil Botnet Detection

1. Behavioral Clustering:

Use clustering techniques to detect groups of wallets with highly similar transaction patterns, which might suggest a sybil botnet.

Focus on new wallets interacting with a common set of addresses.

2. Graph Analysis:

Represent wallet interactions as graphs (nodes = wallets, edges = transactions).

Use graph-based algorithms (e.g., PageRank, community detection) to detect wallet clusters that could represent botnets.

Step 6: Build a Dashboard for Visualization

1. Interface:

Use frameworks like Streamlit or Flask to create a simple web interface where you can visualize transaction flows and anomaly alerts.

2. Data Visualization:

Implement charts for transaction volumes, clusters of wallets, anomaly scores, and timeline-based analyses.

Use libraries like Matplotlib, Plotly, or NetworkX for visualizing wallet interactions.

Step 7: Testing and Iteration

1. Test on the Testnet: Start by running your anomaly detection on the TON testnet to ensure your script works without issues.

2. Refine Your Models: Based on the results from the testnet, fine-tune your anomaly detection model and address any gaps or false positives.

Step 8: Final Submission

1. Prepare Documentation:

Write a detailed report on your approach, including data collection, preprocessing, anomaly detection models, and results.

2. Submit Project:

Package your code, along with a demo of the results, and submit it as per the competition guidelines.

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Timeline (based on your 3-hour daily capacity):

Week 1: Data collection and cleaning.

Week 2: Develop anomaly detection models.

Week 3: Sybil botnet detection.

Week 4: Visualization, testing, and documentation.

If anyone want me join on this project please comment below and our team will reach you.