Identity Theft: Detection & Prevention in Python, Pandas, Tableau, & SQL

This is an online tutorial on how to use python, tableau, pandas and sql to detect identity theft which is a very severe form of fraud.

One of the ways of preventing identity theft is to often monitor your credit and financial statements with a view to detecting any unusual activity. This process can also be automated using Python and Pandas where credit report and financial data for respective institutions are accessed via their API’s. We will use SQL to query the data, as well as run queries for any irregular activities that might come out. Then we shall use Tableau to graphically represent them.

Another way of detecting identity theft entails using python and pandas to search for patterns in you financial data. Writing a script can help you to scan your transaction history in order to flag out transactions which appear odd such as transactions that occur outside normal hours/locations, unusually huge transactions etc.

Learning about identity theft education is an important part of cyber security, while Python, Pandas, Tableau, and SQL are tools that may help one learn more on this issue.

1. Data Analysis: Data analysis associated with identity theft can also be performed using Pandas and SQL. These datasets may include transaction logs, credit card statements, and phishing emails. Such data can be looked at for patterns and abnormalities that indicate fraud and theft.

2. Machine Learning: Using Python’s machine learning libraries, you can develop models for detecting and even predicting identity theft. Models designed using machine learning techniques like logistic regression, decision trees, and artificial neural networks that classify information on identity theft.

3. Simulation: For instance, you can use Python to simulate scenarios like the hacking scamming and stealing identities and all using the same word ‘using.’ Such simulations can be of great value in understanding how attackers operate and developing defensive techniques.

4. Ethical Hacking: The ability to learn ethical hack tricks as well as use python, help in development of instruments for prevention of theft of identities. For example, you may develop programs or codes that target vulnerable sites on networks or applications or develop tools that understand phishing attacks.

It is crucial to understand the concept of identity theft so as to shield yourself and other people from cyber fraud. Before undertaking identity theft prevention and detection by using Python and Panda, you should know how to enable access to necessary API’s information sources.

Python, Pandas, and SQL can be used for identity theft education:

Data analysis with pandas:

import pandas as pd

# Load transaction log dataset
transaction_log = pd.read_csv('transaction_log.csv')

# Identify fraudulent transactions
fraudulent_transactions = transaction_log[transaction_log['amount'] > 1000]

# Visualize transaction patterns
transaction_log.plot(x='timestamp', y='amount')

Review the transaction log dataset and identify transactions exceeding $1000 as possible forgeries, then display chronologically so as to point out patterns.

Machine learning with scikit-learn:

from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split

# Load credit card dataset
credit_card_data = pd.read_csv('credit_card_data.csv')

# Split data into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(credit_card_data.drop('fraudulent', axis=1), credit_card_data['fraudulent'])

# Train a logistic regression model
model = LogisticRegression()
model.fit(X_train, y_train)

# Evaluate the model's performance
accuracy = model.score(X_test, y_test)

Training a logistic regression model using a credit card dataset with the aim of spotting fraudulent transactions. The set is divided into training and test, the model is fed and the accuracy is evaluated.

SQL queries:

import sqlite3

# Connect to database
conn = sqlite3.connect('identity_theft.db')

# Query for suspicious transactions
suspicious_transactions = pd.read_sql_query('SELECT * FROM transactions WHERE amount > 1000', conn)

Access a database and using SQL query any transaction worth over one thousand dollars that is labeled suspicious. The procedures and instruments used depend on the goals of education and available data sets.

Simulation with Python:

import random

# Simulate a phishing email
sender = 'john.doe@fakebank.com'
recipient = 'jane.doe@gmail.com'
subject = 'Urgent: Your account has been compromised!'
body = 'Dear Jane,\n\nWe have detected suspicious activity on your account. Please click the link below to verify your identity.\n\nhttps://fakebank.com/verify?id=12345\n\nThank you for your cooperation.\n\nSincerely,\nJohn'

# Simulate user response
response = random.choice(['click', 'ignore', 'report'])

# Analyze response and update user's security profile
if response == 'click':
    # User falls for phishing scam
    print('User clicked link and entered personal information')
    update_security_profile(recipient, 'low')
elif response == 'ignore':
    # User recognizes phishing attempt and ignores email
    print('User ignored phishing email')
    update_security_profile(recipient, 'high')
else:
    # User reports phishing attempt to IT department
    print('User reported phishing email')
    update_security_profile(recipient, 'medium')

The user receives an email that simulates a phishing one, whose answer helps update the user’s security profile. The responder is chosen randomly to measure the respondents’ security score.

Ethical hacking with Python:

import requests

# Scan for vulnerabilities in web application
response = requests.get('https://example.com/login')
if response.status_code == 200:
    # Website is vulnerable to SQL injection attack
    print('Website is vulnerable to SQL injection attack')
    exploit_sql_injection('https://example.com/login')
else:
    # Website is secure
    print('Website is secure')

It scans through a website for vulnerabilities by trying to log in to the login page and evaluating the response code. By entering the code into the website, it penetrates the system in case the website is vulnerable to a SQL injection attack and gets the required information from the DBMS.

Data visualization with Matplotlib:

import matplotlib.pyplot as plt

# Load identity theft data
identity_theft_data = pd.read_csv('identity_theft_data.csv')

# Create a pie chart of identity theft by type
identity_theft_data.groupby('type')['count'].sum().plot(kind='pie', autopct='%1.1f%%', startangle=90)

# Add title and legend
plt.title('Identity Theft by Type')
plt.legend()

# Display chart
plt.show()

This software loads loads identity theft data, it groups it by type and makes a pie chart showing the share of identity theft cases by type. A title and a legend are added to the chart and it is finally shown to use Matplotlib.

Data cleaning with Pandas:

# Load customer data
customer_data = pd.read_csv('customer_data.csv')

# Remove duplicate records
customer_data = customer_data.drop_duplicates()

# Convert date strings to datetime objects
customer_data['last_login'] = pd.to_datetime(customer_data['last_login'])

# Fill missing values
customer_data['age'].fillna(customer_data['age'].median(), inplace=True)

# Remove outliers
customer_data = customer_data[(customer_data['age'] >= 18) & (customer_data['age'] <= 65)]

Loads customer data, drops all duplicates, converts date strings to datetime objects, fills missing values with the median age, and filters rows where age < 18 or age > 65.

One of the useful tools for identifying patterns and trends associated with identity theft includes Tableau. Users can connect to a datasource, design a chart or table, and format the output for their specific purposes using the sequential steps of connecting to a data source, creating a visualization, and formatting the output. Identification of patterns, trends and other factors relating to identification theft are possible through assisting users to understand their data.

1. Data preparation: Information about identity theft like annual number of identity theft incidents, type of personal information usually stolen, most vulnerable industries and methods of stealing personal information. This information is available from entities such as the Federal Trade Commission, the Identity Theft Resource Center, and various other newspapers.

2. Data cleaning: Verify and refine raw data to confirm its integrity and conformity. For instance, reformat it to the common format when the data is in varied formats. Discard duplicated or unfinished data. Classify the data by relevant categories including type of personal information stolen.

3. Data analysis: Examine the data and see how it can identify trends, or patterns, in relation to identity fraud. To analyze the data you can use Tableau’s data analysis tools like filtering, sorting, or grouping. In this respect, one can classify the data by ear to tell whether or not there is a pattern on increasing identity theft numbers per specific year. Similarly, you may classify your data by an industry and find out the specific industries that thieves mostly target.

4. Visualization: After analyzing the data generate a chart or map in tableau showing what the data is saying. Select a visualization method which captures the data’s essence like the trend of identity theft cases over the years through the use of line-chart or the frequency of these cases based on the states for example heat map. For instance, you will be in a position to bring more information onto the visualizations by taking advantage of the colors and tool tips offered in Tableau.

5. Interpretation: Infer the visualized graph on basis of identity theft trends and patterns. Another example could be that there is a growth in identity theft cases over the period while the financial sector remains the prime target. These insights can be used to create prevention strategies for identity theft and measures for protecting personal information.

Generating visual data through tableaus will aid in recognizing identity theft patterns and trends which in turn helps developing improved preventive methods. A mix of technology with no technology approach would be utilized in preventing identity theft and detecting. Hence, a thorough strategy that combines several ways and tools is required for total prevention of identity fraud.

You have learned how to use of python, pandas, tableau and sql for identify theft protection and detection purposes and also why data security is important and a list of needed technologies. In addition, you were taught on setting of measures that safeguard the data against unauthorized access and fraud such as identity theft.

GitHub: github.com/mariahrucker
LinkedIn: linkedin.com/in/mariahrucker
Instagram: instagram.com/techmariah
Other: linktr.ee/mariahrucker