The Cybersecurity SQL Adventure Lab offers hands-on experience in using SQL to detect potential security threats within a company’s database. This lab simulates real-world scenarios where participants analyze login attempts, investigate after-hours access, and identify potential insider threats. The lab covers essential cybersecurity tasks such as detecting failed login attempts, identifying unusual access patterns, and cross-referencing login data with employee information. Participants learn to create comprehensive reports of login activities, helping them spot patterns and correlations that could indicate security risks. By working through these exercises, users develop critical skills in database querying and log analysis, which are fundamental in modern cybersecurity practices. The lab emphasizes the importance of asking the right questions and interpreting data effectively, preparing participants for real-world challenges in identifying and preventing security breaches. Hands-On: Building Your Own Cybersecurity SQL Lab Let’s explore the nuts and bolts of our Cybersecurity SQL Adventure Lab. I’ll show you how to set up your own tables and throw in some sample data. Then, we’ll run through a few queries that’ll make you feel like a real cyber detective! Setting Up Your Database First, let’s create two tables: log_in_attempts and employees. Here's the SQL to make it happen: CREATE TABLE log_in_attempts (
    event_id INT AUTO_INCREMENT PRIMARY KEY,
    username VARCHAR(50) NOT NULL,
    login_date DATE NOT NULL,
    login_time TIME NOT NULL,
    country VARCHAR(50) NOT NULL,
    ip_address VARCHAR(15) NOT NULL,
    success BOOLEAN NOT NULL
); CREATE TABLE employees (
    employee_id INT AUTO_INCREMENT PRIMARY KEY,
    device_id VARCHAR(50) NOT NULL,
    username VARCHAR(50) NOT NULL,
    department VARCHAR(50) NOT NULL,
    office VARCHAR(50) NOT NULL
); Populating with Sample Data Now, let’s add some relevant sample data: INSERT INTO log_in_attempts (username, login_date, login_time, country, ip_address, success) VALUES
('jsmith', '2022-05-08', '17:30:00', 'USA', '192.168.1.1', TRUE),
('ajonson', '2022-05-09', '19:15:00', 'MEX', '192.168.1.2', FALSE),
('mjohnson', '2022-05-09', '20:30:00', 'CAN', '192.168.1.3', FALSE),
('jdoe', '2022-05-10', '08:45:00', 'USA', '192.168.1.4', TRUE),
('sbrown', '2022-05-10', '22:15:00', 'UK', '192.168.1.5', FALSE); INSERT INTO employees (device_id, username, department, office) VALUES
('D001', 'jsmith', 'Marketing', 'East-170'),
('D002', 'ajonson', 'Sales', 'North-110'),
('D003', 'mjohnson', 'Finance', 'West-223'),
('D004', 'jdoe', 'IT', 'South-045'),
('D005', 'sbrown', 'HR', 'East-180'); Sleuthing with SQL Queries Now for the fun part — let’s run some queries and see what we can uncover! Hunting for Failed Login Attempts: SELECT * FROM log_in_attempts WHERE success = FALSE; This query is like shining a flashlight on all the failed break-in attempts.
Catching After-Hours Activity: SELECT * FROM log_in_attempts WHERE TIME(login_time) < '09:00:00' OR TIME(login_time) > '17:00:00'; Who’s burning the midnight oil… or up to no good?
Identifying the IT Crowd: SELECT * FROM employees WHERE department = 'IT'; These are your digital gatekeepers — worth keeping an eye on!
Cross-Referencing Failed Logins with Employee Info: SELECT l.username, l.login_date, l.login_time, l.country, e.department FROM log_in_attempts l JOIN employees e ON l.username = e.username WHERE l.success = FALSE; This query is like connecting the dots between suspicious activity and who might be behind it.
The Grand Overview: SELECT l.event_id, l.username, e.department, e.office, l.login_date, l.login_time, l.country, l.ip_address, CASE WHEN l.success THEN 'Success' ELSE 'Failure' END AS login_result FROM log_in_attempts l LEFT JOIN employees e ON l.username = e.username ORDER BY l.login_date, l.login_time; This gives you a bird’s-eye view of all the action — successes, failures, and everything in between. There you have it! With these tables, data, and queries, you’re all set to start your own cybersecurity investigation. Feel free to tweak the data or come up with your own queries. Who knows what security vulnerabilities you might uncover? Happy investigating! Conclusion These SQL skills aren’t just for practice — they’re essential tools in real-world cybersecurity. As cyber threats evolve, the ability to quickly query and analyze database logs can be the difference between detecting a breach early and discovering it too late. Whether you’re securing a small business or a large corporation, mastering these techniques will make you a more effective defender in the ongoing battle for digital security. The Cybersecurity SQL Adventure Lab offers hands-on experience in using SQL to detect potential security threats within a company’s database. This lab simulates real-world scenarios where participants analyze login attempts, investigate after-hours access, and identify potential insider threats. The lab covers essential cybersecurity tasks such as detecting failed login attempts, identifying unusual access patterns, and cross-referencing login data with employee information. Participants learn to create comprehensive reports of login activities, helping them spot patterns and correlations that could indicate security risks. By working through these exercises, users develop critical skills in database querying and log analysis, which are fundamental in modern cybersecurity practices. The lab emphasizes the importance of asking the right questions and interpreting data effectively, preparing participants for real-world challenges in identifying and preventing security breaches. Hands-On: Building Your Own Cybersecurity SQL Lab Let’s explore the nuts and bolts of our Cybersecurity SQL Adventure Lab. I’ll show you how to set up your own tables and throw in some sample data. Then, we’ll run through a few queries that’ll make you feel like a real cyber detective! Setting Up Your Database First, let’s create two tables: log_in_attempts and employees . Here's the SQL to make it happen: log_in_attempts employees CREATE TABLE log_in_attempts (
    event_id INT AUTO_INCREMENT PRIMARY KEY,
    username VARCHAR(50) NOT NULL,
    login_date DATE NOT NULL,
    login_time TIME NOT NULL,
    country VARCHAR(50) NOT NULL,
    ip_address VARCHAR(15) NOT NULL,
    success BOOLEAN NOT NULL
); CREATE TABLE log_in_attempts (
    event_id INT AUTO_INCREMENT PRIMARY KEY,
    username VARCHAR(50) NOT NULL,
    login_date DATE NOT NULL,
    login_time TIME NOT NULL,
    country VARCHAR(50) NOT NULL,
    ip_address VARCHAR(15) NOT NULL,
    success BOOLEAN NOT NULL
); CREATE TABLE employees (
    employee_id INT AUTO_INCREMENT PRIMARY KEY,
    device_id VARCHAR(50) NOT NULL,
    username VARCHAR(50) NOT NULL,
    department VARCHAR(50) NOT NULL,
    office VARCHAR(50) NOT NULL
); CREATE TABLE employees (
    employee_id INT AUTO_INCREMENT PRIMARY KEY,
    device_id VARCHAR(50) NOT NULL,
    username VARCHAR(50) NOT NULL,
    department VARCHAR(50) NOT NULL,
    office VARCHAR(50) NOT NULL
); Populating with Sample Data Now, let’s add some relevant sample data: INSERT INTO log_in_attempts (username, login_date, login_time, country, ip_address, success) VALUES
('jsmith', '2022-05-08', '17:30:00', 'USA', '192.168.1.1', TRUE),
('ajonson', '2022-05-09', '19:15:00', 'MEX', '192.168.1.2', FALSE),
('mjohnson', '2022-05-09', '20:30:00', 'CAN', '192.168.1.3', FALSE),
('jdoe', '2022-05-10', '08:45:00', 'USA', '192.168.1.4', TRUE),
('sbrown', '2022-05-10', '22:15:00', 'UK', '192.168.1.5', FALSE); INSERT INTO log_in_attempts (username, login_date, login_time, country, ip_address, success) VALUES
('jsmith', '2022-05-08', '17:30:00', 'USA', '192.168.1.1', TRUE),
('ajonson', '2022-05-09', '19:15:00', 'MEX', '192.168.1.2', FALSE),
('mjohnson', '2022-05-09', '20:30:00', 'CAN', '192.168.1.3', FALSE),
('jdoe', '2022-05-10', '08:45:00', 'USA', '192.168.1.4', TRUE),
('sbrown', '2022-05-10', '22:15:00', 'UK', '192.168.1.5', FALSE); INSERT INTO employees (device_id, username, department, office) VALUES
('D001', 'jsmith', 'Marketing', 'East-170'),
('D002', 'ajonson', 'Sales', 'North-110'),
('D003', 'mjohnson', 'Finance', 'West-223'),
('D004', 'jdoe', 'IT', 'South-045'),
('D005', 'sbrown', 'HR', 'East-180'); INSERT INTO employees (device_id, username, department, office) VALUES
('D001', 'jsmith', 'Marketing', 'East-170'),
('D002', 'ajonson', 'Sales', 'North-110'),
('D003', 'mjohnson', 'Finance', 'West-223'),
('D004', 'jdoe', 'IT', 'South-045'),
('D005', 'sbrown', 'HR', 'East-180'); Sleuthing with SQL Queries Now for the fun part — let’s run some queries and see what we can uncover! Hunting for Failed Login Attempts: Hunting for Failed Login Attempts: SELECT * FROM log_in_attempts WHERE success = FALSE; SELECT * FROM log_in_attempts WHERE success = FALSE; SELECT * FROM log_in_attempts WHERE success = FALSE; This query is like shining a flashlight on all the failed break-in attempts. Catching After-Hours Activity: This query is like shining a flashlight on all the failed break-in attempts. Catching After-Hours Activity: SELECT * FROM log_in_attempts WHERE TIME(login_time) < '09:00:00' OR TIME(login_time) > '17:00:00'; SELECT * FROM log_in_attempts WHERE TIME(login_time) < '09:00:00' OR TIME(login_time) > '17:00:00'; SELECT * FROM log_in_attempts WHERE TIME(login_time) < '09:00:00' OR TIME(login_time) > '17:00:00'; Who’s burning the midnight oil… or up to no good? Identifying the IT Crowd: Who’s burning the midnight oil… or up to no good? Identifying the IT Crowd: SELECT * FROM employees WHERE department = 'IT'; SELECT * FROM employees WHERE department = 'IT'; SELECT * FROM employees WHERE department = 'IT'; These are your digital gatekeepers — worth keeping an eye on! Cross-Referencing Failed Logins with Employee Info: These are your digital gatekeepers — worth keeping an eye on! Cross-Referencing Failed Logins with Employee Info: SELECT l.username, l.login_date, l.login_time, l.country, e.department FROM log_in_attempts l JOIN employees e ON l.username = e.username WHERE l.success = FALSE; SELECT l.username, l.login_date, l.login_time, l.country, e.department FROM log_in_attempts l JOIN employees e ON l.username = e.username WHERE l.success = FALSE; SELECT l.username, l.login_date, l.login_time, l.country, e.department FROM log_in_attempts l JOIN employees e ON l.username = e.username WHERE l.success = FALSE; This query is like connecting the dots between suspicious activity and who might be behind it. The Grand Overview: This query is like connecting the dots between suspicious activity and who might be behind it. The Grand Overview: SELECT l.event_id, l.username, e.department, e.office, l.login_date, l.login_time, l.country, l.ip_address, CASE WHEN l.success THEN 'Success' ELSE 'Failure' END AS login_result FROM log_in_attempts l LEFT JOIN employees e ON l.username = e.username ORDER BY l.login_date, l.login_time; SELECT l.event_id, l.username, e.department, e.office, l.login_date, l.login_time, l.country, l.ip_address, CASE WHEN l.success THEN 'Success' ELSE 'Failure' END AS login_result FROM log_in_attempts l LEFT JOIN employees e ON l.username = e.username ORDER BY l.login_date, l.login_time; SELECT l.event_id, l.username, e.department, e.office, l.login_date, l.login_time, l.country, l.ip_address, CASE WHEN l.success THEN 'Success' ELSE 'Failure' END AS login_result FROM log_in_attempts l LEFT JOIN employees e ON l.username = e.username ORDER BY l.login_date, l.login_time; This gives you a bird’s-eye view of all the action — successes, failures, and everything in between. This gives you a bird’s-eye view of all the action — successes, failures, and everything in between. This gives you a bird’s-eye view of all the action — successes, failures, and everything in between. There you have it! With these tables, data, and queries, you’re all set to start your own cybersecurity investigation. Feel free to tweak the data or come up with your own queries. Who knows what security vulnerabilities you might uncover? Happy investigating! Conclusion These SQL skills aren’t just for practice — they’re essential tools in real-world cybersecurity. As cyber threats evolve, the ability to quickly query and analyze database logs can be the difference between detecting a breach early and discovering it too late. Whether you’re securing a small business or a large corporation, mastering these techniques will make you a more effective defender in the ongoing battle for digital security.

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How to Detect Cybersecurity Vulnerabilities with SQL

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