Real-Time Criminal Network Analysis and Actionable Intelligence System. Python Centric model.

Python script designed for law enforcement to analyze communications among criminal stakeholders. The script will:

1. Connect to a database containing communication records and stakeholder information.

2. Analyze communications to detect patterns, hierarchies, and key actors (using network analysis).

3. Identify organizational structure (organigram) using graph analysis.

4. Generate actionable insights to help plan legal actions with a general attorney or judge.

5. Visualize the network for better understanding.

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Requirements

Install necessary Python packages:

pip install mysql-connector-python pandas networkx matplotlib

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Python Script

import mysql.connector

import pandas as pd

import networkx as nx

import matplotlib.pyplot as plt

# Database connection parameters

DB_CONFIG = {

    'host': 'localhost',

    'user': 'your_username',

    'password': 'your_password',

    'database': 'criminal_network'

}

# Connect to the database

def connect_to_db():

    try:

        conn = mysql.connector.connect(**DB_CONFIG)

        if conn.is_connected():

            print("Connected to the database")

            return conn

    except mysql.connector.Error as e:

        print(f"Error: {e}")

        return None

# Fetch communication data

def fetch_data(conn):

    query = """

    SELECT 

        sender_id,

        receiver_id,

        message_type,

        timestamp

    FROM 

        Communications;

    """

    

    stakeholders_query = """

    SELECT 

        stakeholder_id,

        full_name,

        role,

        risk_level

    FROM 

        Stakeholders;

    """

    

    communications = pd.read_sql(query, conn)

    stakeholders = pd.read_sql(stakeholders_query, conn)

    

    return communications, stakeholders

# Create and analyze communication network

def build_communication_network(communications, stakeholders):

    G = nx.DiGraph()

    # Add nodes (stakeholders)

    for _, row in stakeholders.iterrows():

        G.add_node(

            row['stakeholder_id'],

            full_name=row['full_name'],

            role=row['role'],

            risk_level=row['risk_level']

        )

    # Add edges (communications)

    for _, row in communications.iterrows():

        G.add_edge(

            row['sender_id'], 

            row['receiver_id'], 

            message_type=row['message_type'],

            timestamp=row['timestamp']

        )

    

    # Centrality analysis to identify key players

    centrality = nx.degree_centrality(G)

    top_actors = sorted(centrality.items(), key=lambda x: x[1], reverse=True)[:5]

    hierarchy = nx.pagerank(G)

    hierarchy_sorted = sorted(hierarchy.items(), key=lambda x: x[1], reverse=True)

    key_actors = []

    for actor in top_actors:

        actor_data = stakeholders[stakeholders['stakeholder_id'] == actor[0]].iloc[0]

        key_actors.append(f"{actor_data['full_name']} (Role: {actor_data['role']}, Risk: {actor_data['risk_level']})")

    return G, key_actors, hierarchy_sorted

# Visualize the communication network

def plot_network(G):

    pos = nx.spring_layout(G)

    risk_colors = {'Low': 'green', 'Medium': 'orange', 'High': 'red', 'Critical': 'darkred'}

    

    node_colors = [

        risk_colors[G.nodes[n]['risk_level']] 

        for n in G.nodes

    ]

    plt.figure(figsize=(12, 8))

    nx.draw(

        G, pos, with_labels=True, 

        node_color=node_colors, 

        node_size=800, 

        font_size=10, 

        font_color='white', 

        edge_color='#999999'

    )

    

    plt.title("Criminal Communication Network")

    plt.show()

# Generate action plan for legal strategy

def generate_action_plan(key_actors, hierarchy):

    print("\n--- Action Plan ---\n")

    

    # Identify top leaders

    print("🔹 Top Criminal Leaders:")

    for actor in hierarchy[:3]:

        print(f" - {actor[0]} (Influence score: {actor[1]:.2f})")

    # Identify key actors for legal action

    print("\n🔸 Key Actors for Legal Action:")

    for actor in key_actors:

        print(f" - {actor}")

    

    # Suggested actions

    print("\n✅ Suggested Actions:")

    print(" 1. Interrogate top leaders and high-risk actors.")

    print(" 2. Target key communicators to disrupt network operations.")

    print(" 3. Coordinate with the general attorney to issue warrants.")

    print(" 4. Monitor communication for signs of retaliation.")

# Main execution

def main():

    conn = connect_to_db()

    if conn:

        try:

            communications, stakeholders = fetch_data(conn)

            G, key_actors, hierarchy = build_communication_network(communications, stakeholders)

            plot_network(G)

            generate_action_plan(key_actors, hierarchy)

        finally:

            conn.close()

if __name__ == "__main__":

    main()

---

Database Schema Example

Here’s a sample schema that the script expects:

Stakeholders Table

CREATE TABLE Stakeholders (

    stakeholder_id INT PRIMARY KEY AUTO_INCREMENT,

    full_name VARCHAR(255),

    role VARCHAR(100),

    risk_level ENUM('Low', 'Medium', 'High', 'Critical')

);

Communications Table

CREATE TABLE Communications (

    communication_id INT PRIMARY KEY AUTO_INCREMENT,

    sender_id INT,

    receiver_id INT,

    message_type VARCHAR(100),

    timestamp TIMESTAMP,

    FOREIGN KEY (sender_id) REFERENCES Stakeholders(stakeholder_id),

    FOREIGN KEY (receiver_id) REFERENCES Stakeholders(stakeholder_id)

);

---

How It Works

1. Connects to the database and pulls communication and stakeholder data.

2. Builds a communication graph with networkx for hierarchy and centrality analysis.

3. Identifies key actors based on centrality and importance.

4. Generates an action plan based on the analysis.

5. Visualizes the network using matplotlib to show connections and hierarchy.

---

Example Output

--- Action Plan ---

🔹 Top Criminal Leaders:

  - Leader A (Influence score: 0.85)

  - Operator B (Influence score: 0.75)

  - Coordinator C (Influence score: 0.72)

🔸 Key Actors for Legal Action:

  - John Doe (Role: Leader, Risk: Critical)

  - Jane Smith (Role: Operator, Risk: High)

✅ Suggested Actions:

  1. Interrogate top leaders and high-risk actors.

  2. Target key communicators to disrupt network operations.

  3. Coordinate with the general attorney to issue warrants.

  4. Monitor communication for signs of retaliation.

---

Why This Script Works

✅ Uses network analysis to detect hierarchies and key actors

✅ Identifies high-risk connections and vulnerabilities

✅ Provides a clear action plan to present to legal authorities

✅ Generates visuals to simplify complex networks

🚀 Next Steps

Add more complex analysis (e.g., clustering, hidden nodes)

Integrate real-time monitoring for ongoing operations

Expand the action plan based on specific law enforcement protocols

Enhanced Python script that includes real-time monitoring, complex cluster analysis, hidden node detection, and law enforcement protocols. This version will:

1. Connect to the database and monitor real-time communications.

2. Use network clustering to identify sub-networks and operational cells.

3. Apply hidden node detection to spot indirect influencers.

4. Flag anomalies and potential security threats.

5. Include law enforcement and criminal justice protocols for actionable intelligence and legal compliance.

---

Requirements

Install additional libraries:

pip install mysql-connector-python pandas networkx matplotlib scikit-learn

---

Python Script

import mysql.connector

import pandas as pd

import networkx as nx

import matplotlib.pyplot as plt

from sklearn.cluster import KMeans

import time

import threading

# Database connection parameters

DB_CONFIG = {

    'host': 'localhost',

    'user': 'your_username',

    'password': 'your_password',

    'database': 'criminal_network'

}

# Connect to the database

def connect_to_db():

    try:

        conn = mysql.connector.connect(**DB_CONFIG)

        if conn.is_connected():

            print("Connected to the database")

            return conn

    except mysql.connector.Error as e:

        print(f"Error: {e}")

        return None

# Fetch communication and stakeholder data

def fetch_data(conn):

    query = """

    SELECT sender_id, receiver_id, message_type, timestamp

    FROM Communications;

    """

    

    stakeholders_query = """

    SELECT stakeholder_id, full_name, role, risk_level

    FROM Stakeholders;

    """

    

    communications = pd.read_sql(query, conn)

    stakeholders = pd.read_sql(stakeholders_query, conn)

    

    return communications, stakeholders

# Build and analyze communication network

def build_network(communications, stakeholders):

    G = nx.DiGraph()

    # Add nodes (stakeholders)

    for _, row in stakeholders.iterrows():

        G.add_node(

            row['stakeholder_id'],

            full_name=row['full_name'],

            role=row['role'],

            risk_level=row['risk_level']

        )

    # Add edges (communications)

    for _, row in communications.iterrows():

        G.add_edge(

            row['sender_id'],

            row['receiver_id'],

            message_type=row['message_type'],

            timestamp=row['timestamp']

        )

    

    # Centrality analysis to identify key players

    centrality = nx.degree_centrality(G)

    key_actors = sorted(centrality.items(), key=lambda x: x[1], reverse=True)[:5]

    # Cluster analysis to detect hidden networks

    cluster_analysis(G)

    # Hidden node detection

    hidden_nodes = detect_hidden_nodes(G)

    return G, key_actors, hidden_nodes

# Cluster analysis using KMeans

def cluster_analysis(G):

    adjacency_matrix = nx.to_numpy_matrix(G)

    n_clusters = 3

    

    kmeans = KMeans(n_clusters=n_clusters, random_state=42)

    labels = kmeans.fit_predict(adjacency_matrix)

    for i, node in enumerate(G.nodes):

        G.nodes[node]['cluster'] = labels[i]

    print("\n🔍 Detected Clusters:")

    for i in range(n_clusters):

        cluster_members = [n for n in G.nodes if G.nodes[n]['cluster'] == i]

        print(f" - Cluster {i + 1}: {cluster_members}")

# Detect hidden nodes through indirect connections

def detect_hidden_nodes(G):

    hidden_nodes = []

    

    for node in G.nodes:

        successors = set(G.successors(node))

        predecessors = set(G.predecessors(node))

        

        # If node has many indirect connections, it's likely a hidden node

        if len(successors) > 3 and len(predecessors) > 3:

            hidden_nodes.append(node)

    if hidden_nodes:

        print("\n⚠️ Hidden Nodes Detected:")

        for node in hidden_nodes:

            print(f" - {G.nodes[node]['full_name']} (Potential Operator)")

    else:

        print("\n✅ No Hidden Nodes Detected.")

    return hidden_nodes

# Real-time monitoring function

def monitor_communications():

    conn = connect_to_db()

    if not conn:

        return

    

    try:

        last_checked = None

        while True:

            # Fetch latest communications

            communications, stakeholders = fetch_data(conn)

            if last_checked:

                new_data = communications[communications['timestamp'] > last_checked]

                if not new_data.empty:

                    print("\n🚨 New Communication Detected:")

                    for _, row in new_data.iterrows():

                        sender = stakeholders[stakeholders['stakeholder_id'] == row['sender_id']].iloc[0]

                        receiver = stakeholders[stakeholders['stakeholder_id'] == row['receiver_id']].iloc[0]

                        print(f" - {sender['full_name']} → {receiver['full_name']} ({row['message_type']})")

                    G, key_actors, hidden_nodes = build_network(communications, stakeholders)

                    plot_network(G)

                    generate_action_plan(G, key_actors, hidden_nodes)

                    

            last_checked = communications['timestamp'].max()

            time.sleep(10) # Check every 10 seconds

            

    finally:

        conn.close()

# Action plan with legal protocols

def generate_action_plan(G, key_actors, hidden_nodes):

    print("\n--- Action Plan ---\n")

    

    # Identify key actors for targeted action

    print("🔹 Key Actors for Legal Action:")

    for actor in key_actors:

        node = G.nodes[actor[0]]

        print(f" - {node['full_name']} (Role: {node['role']}, Risk: {node['risk_level']})")

    if hidden_nodes:

        print("\n⚠️ Hidden Nodes for Surveillance:")

        for node in hidden_nodes:

            data = G.nodes[node]

            print(f" - {data['full_name']} (Role: {data['role']})")

    print("\n✅ Recommended Actions:")

    print(" 1. Secure judicial authorization for surveillance and wiretapping.")

    print(" 2. Conduct targeted interrogations and surveillance.")

    print(" 3. Coordinate with the attorney general to approve warrants.")

    print(" 4. Monitor hidden nodes and high-risk clusters.")

# Plot communication network

def plot_network(G):

    pos = nx.spring_layout(G)

    risk_colors = {'Low': 'green', 'Medium': 'orange', 'High': 'red', 'Critical': 'darkred'}

    

    node_colors = [risk_colors[G.nodes[n]['risk_level']] for n in G.nodes]

    plt.figure(figsize=(12, 8))

    nx.draw(G, pos, with_labels=True, node_color=node_colors, node_size=800, font_size=10, font_color='white')

    plt.title("Criminal Communication Network")

    plt.show()

# Main execution with real-time monitoring

def main():

    thread = threading.Thread(target=monitor_communications)

    thread.start()

if __name__ == "__main__":

    main()

---

How It Works

✅ Real-time monitoring checks for new communications every 10 seconds.

✅ Uses KMeans clustering to detect operational cells.

✅ Detects hidden nodes based on indirect connectivity patterns.

✅ Identifies key actors and hierarchical structure using centrality analysis.

✅ Provides an action plan based on criminal justice protocols.

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Legal Protocols Included

✔️ Judicial Authorization – Ensure warrants are obtained for surveillance.

✔️ Attorney General Coordination – Plan actions with legal authorities.

✔️ Compliance with Due Process – No illegal searches or wiretaps.

✔️ Surveillance Oversight – Establish proper oversight mechanisms.

---

Example Output

🚨 New Communication Detected:

  - John Doe → Jane Smith (Encrypted Message)

🔹 Key Actors for Legal Action:

  - John Doe (Role: Leader, Risk: Critical)

✅ Recommended Actions:

  1. Secure judicial authorization for surveillance and wiretapping.

  2. Conduct targeted interrogations and surveillance.

  3. Coordinate with the attorney general to approve warrants.

🚀 Next Steps

Add machine learning to detect behavior patterns.

Expand clustering to detect multi-layered cells.

Integrate with external intelligence feeds.