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Enhancing System Security Through Advanced SQL Filtering Techniques – Cybersecurity Portfolio


Introduction

In the context of fortifying our organizational systems against security vulnerabilities, my role encompasses a comprehensive review and upgrade of security measures. This includes conducting thorough investigations into potential security breaches and ensuring that employee workstations are equipped with the latest protections. An essential tool in our security strategy is the utilization of SQL (Structured Query Language) queries, enriched with specific filters to extract pertinent security-related data.

Identifying Failed Login Attempts After Hours

Query Example:

SELECT *
FROM log_in_attempts
WHERE login_time > ’18:00′ AND success = FALSE;

Explanation: This SQL query is designed to fetch every column from the log_in_attempts table, applying filters to isolate incidents of failed login attempts that occurred after standard working hours. The inclusion of both time and success status criteria allows us to precisely target the records of interest, aiding in the identification of potential unauthorized access attempts.

Filtering Login Attempts by Date

Query Example:

SELECT *
FROM log_in_attempts
WHERE login_date = ‘2022-05-08’ OR login_date = ‘2022-05-09’;

Explanation: Here, the objective is to retrieve all records from the log_in_attempts table for login attempts made on two specific dates. The use of the OR operator enables the selection of records matching either of the specified dates, facilitating a focused analysis of login activities during that period.

Excluding Login Attempts Originating from Mexico

Query Example:

SELECT *
FROM log_in_attempts
WHERE NOT country LIKE ‘MEX%’;

Explanation: This query extracts all records from the log_in_attempts table while excluding those originating from Mexico. The combination of NOT and LIKE operators serves to filter out login attempts based on the specified pattern, thus concentrating on analyzing login attempts from other geographical regions.

Retrieving Marketing Department Employees in Eastern Offices

Query Example:

SELECT *
FROM employees
WHERE department = ‘MARKETING’ AND office LIKE ‘EAST%’;

Explanation: The query aims to select all data from the employees table for individuals working in the Marketing department and located in Eastern offices. The criteria combine departmental affiliation and office location, enabling a targeted retrieval of employee records.

Identifying Employees in Finance or Sales Departments

Query Example:

SELECT *
FROM employees
WHERE department = ‘FINANCE’ OR department = ‘SALES’;

Explanation: In this instance, the goal is to gather data on employees belonging to either the Finance or Sales departments. The OR operator is instrumental in fetching records that meet either condition, thereby encompassing a broader segment of the workforce.

Excluding IT Department Employees

Query Example:

SELECT *
FROM employees
WHERE NOT department = ‘Information Technology’;

Explanation: The focus of this query is to retrieve records for all employees outside the Information Technology department. Utilizing the NOT operator enables the exclusion of a specific department from the search results.

Conclusion

Through strategic application of SQL filtering techniques across two primary tables—log_in_attempts and employees—we’ve demonstrated the capacity to extract precise information pertinent to our security analysis. The utilization of SQL operators such as AND, OR, NOT, and LIKE, in conjunction with wildcard characters, underscores the flexibility and power of SQL in navigating and analyzing complex datasets to bolster system security.

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