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Spotlight
PAGE: https://alison.com/course/the-complete-guide-to-microsoft-access-2021
PROMISE: Learn to structure transaction data to spot rapid-fire checkout patterns.
WHO: Security analysts and business owners reviewing historical checkout data for security insights.
WHYNOW: Modernizing data review practices allows for more efficient detection of past security anomalies.
DESC: Learn how to use MS Access 2021 in this data management course that explains how to create, edit, link and share databases for analysis and presentation.
BEFORE: Access logs remain fragmented in raw text files that do not interact with each other.
BEFORE: Rapid-fire submission patterns are buried in high-volume data without clear structural grouping.
BEFORE: Manual review of hundreds of entries is time-consuming and prone to oversight.
AFTER: Transaction data is consolidated into a managed database where logs share a common structure.
AFTER: Specific attack sequences are isolated using primary keys and indexed fields for faster navigation.
AFTER: Filters and sorting functions separate normal traffic from anomalous bursts for targeted analysis.
GAIN: A unified database structure that consolidates fragmented access logs for easier analysis.
GAIN: Clear identification of transactional records using primary keys to track specific attempts.
GAIN: The ability to filter and sort data to distinguish rapid-fire attacks from standard user behavior.
STEP: It is unclear which user attempts correspond to the same card test sequence. || Identify index fields, primary keys and numeric fields || The reader structures records using unique identifiers to isolate specific attack vectors.
STEP: The volume of normal traffic hides the abnormal burst of test submissions. || Explain how to design, sort, filter and modify data in ‘Datasheet’ view || The reader isolates suspicious intervals by sorting timestamps and filtering by IP range.
NOTFOR: Users seeking real-time blocking of active card testing attacks without log analysis.
NOTFOR: Readers requiring advanced network security tools or WAF configuration outside of database software.
FACT: Average duration: 1.5-3 Hours (Contains Video)
FACT: CPD-accredited certificate available
FACT: Advanced level
COVERS: Explain how to acquire MS Access 2021
COVERS: Describe how to use various database templates to create, save and open databases
COVERS: Describe how to create tables and fields and set data validation rules
COVERS: Identify index fields, primary keys and numeric fields
COVERS: Discuss how to import data in MS Access 2021
COVERS: Explain how to design, sort, filter and modify data in ‘Datasheet’ view
COVERS: Outline how to create ‘relationships’ between multiple tables in MS Access 2021
COVERS: Microsoft Office
COVERS: Microsoft Access
COVERS: Data Analysis
COVERS: Microsoft
MODULE: Get Started With Access 2021 || Introduction to Access 2021 | Get Started With Access 2021 | Working With Tables | Importing Data | Datasheet View | Creating Relationships
What you are probably seeing
The checkout page logs show a high volume of failed payment attempts from the same source within a short window of time.
The user agent strings and IP addresses associated with these failures appear clustered or identical across many distinct transactions.
Why it happens
Bots scan for valid card numbers by sending thousands of low-value test transactions to the gateway.
These attacks exploit standard checkout endpoints that do not inherently verify the intent of the submission.
What changed recently
Step by step
1. Export the last 24 hours of web server access logs to a local file for analysis.
2. Filter the logs for HTTP 400 or 403 responses originating from the checkout URL.
3. Count the frequency of failed requests per IP address to identify outliers.
4. Check for identical user agent strings across different IP addresses to spot bot networks.
5. Add the identified malicious IPs and user agents to the server block list or firewall.
Ways to go further
Start here: The Complete Guide to Microsoft Access 2021 For when you are trying to understand how the attacker generated the rapid-fire test submissions. Open the free course on Alison ↗
Better fit: Anticipating Patterns and Statistical Inferences in Statistics For when you need to visualize the spike in failed checkout attempts before writing the block rule. Open the free course on Alison ↗
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