Malware analysis basics in Cybersecurity - Mini Project: Build & Apply

Malware Analysis Basics

📖 Scenario: You are a cybersecurity analyst tasked with understanding basic malware characteristics to help protect your company's network. You will create a simple data structure to represent malware samples and analyze their properties step-by-step.

🎯 Goal: Build a structured overview of malware samples with their types and behaviors, then filter and summarize this data to understand common malware traits.

📋 What You'll Learn

Create a dictionary named malware_samples with exact malware names and their types

Add a variable high_risk_types listing malware types considered high risk

Use a dictionary comprehension to create high_risk_samples containing only malware of high risk types

Add a final summary variable summary that counts how many high risk samples were found

💡 Why This Matters

🌍 Real World

Cybersecurity analysts often need to organize and analyze malware data to understand threats and protect networks.

💼 Career

This project teaches foundational skills in data organization and filtering, which are essential for malware analysis and threat intelligence roles.

1

Create the malware samples dictionary

Create a dictionary called malware_samples with these exact entries: 'TrojanX': 'Trojan', 'WormY': 'Worm', 'SpyZ': 'Spyware', 'AdwareA': 'Adware', 'RansomB': 'Ransomware'

Cybersecurity

# Create the malware_samples dictionary with given entries
# Your code here

Need a hint?

Use curly braces {} to create a dictionary with the exact malware names as keys and their types as values.

2

Define high risk malware types

Create a list called high_risk_types containing these exact strings: 'Trojan', 'Ransomware', and 'Worm'

Cybersecurity

malware_samples = {
    'TrojanX': 'Trojan',
    'WormY': 'Worm',
    'SpyZ': 'Spyware',
    'AdwareA': 'Adware',
    'RansomB': 'Ransomware'
}
# Create the high_risk_types list with given malware types
# Your code here

Need a hint?

Use square brackets [] to create a list with the exact malware types as strings.

3

Filter high risk malware samples

Use a dictionary comprehension to create a dictionary called high_risk_samples that includes only the entries from malware_samples where the malware type is in high_risk_types

Cybersecurity

malware_samples = {
    'TrojanX': 'Trojan',
    'WormY': 'Worm',
    'SpyZ': 'Spyware',
    'AdwareA': 'Adware',
    'RansomB': 'Ransomware'
}
high_risk_types = ['Trojan', 'Ransomware', 'Worm']
# Create high_risk_samples dictionary using comprehension
# Your code here

Need a hint?

Use {name: mtype for name, mtype in malware_samples.items() if mtype in high_risk_types} to filter the dictionary.

4

Summarize high risk malware count

Create a variable called summary and set it to a string that says exactly: "Number of high risk malware samples: X" where X is the number of items in high_risk_samples

Cybersecurity

malware_samples = {
    'TrojanX': 'Trojan',
    'WormY': 'Worm',
    'SpyZ': 'Spyware',
    'AdwareA': 'Adware',
    'RansomB': 'Ransomware'
}
high_risk_types = ['Trojan', 'Ransomware', 'Worm']
high_risk_samples = {name: mtype for name, mtype in malware_samples.items() if mtype in high_risk_types}
# Create summary string with count of high risk samples
# Your code here

Need a hint?

Use an f-string to include the length of high_risk_samples in the summary string.