TOP SECRET: FRAUD DETECTION WITH ELASTIC

Preliminary Disclaimer: The title “top secret” can be interpreted in various ways. To clarify, we will not be sharing any confidential information from clients or prospects. Instead, we will highlight a number of exceptional and highly valuable use cases that can also be achieved with Elastic. Since most people are not aware that all this can be done with Elastic, we use the title “top secret.”

INTRODUCTION

Many companies and organizations use the Elastic (ELK) stack to monitor applications. To do this, a lot of different logs are ingested, such as access logs and application transaction logs. These logs are not only useful for Application Performance Monitoring (APM) but they are also valuable sources of information for detecting various types of fraud. Here are some examples of fraud or abnormal behavior that can be identified through careful analysis using Machine Learning in the Elastic (ELK) stack.

DATA EXFILTRATION OR DATA SCRAPING

Example: A large number of data download requests within a short period can indicate that someone is attempting to steal (sensitive) data. Unusual patterns in access logs, such as repetitive and systematic requests to specific pages or APIs, may indicate automated scripts or bots scraping data from your site.

Detection: Monitor for unusually high volumes of data access or download activity.

ANOMALOUS BEHAVIOR

Example: Users accessing parts of the system or information they have never visited before or accessing at unusual times or locations might indicate fraud or at least abnormal behavior.

Detection: Implement anomaly detection systems that flag deviations from a user’s normal behavior.

PAYMENT FRAUD

Access logs from e-commerce platforms might show suspicious patterns such as multiple failed payment attempts in a short timeframe or unusual transactions, indicating potential payment fraud attempts. Suspicious transactions can also be prevented and detected in a similar way in the banking sector.

Detection: Implement anomaly detection systems that flag deviations from normal payment behavior.

These were just a few examples; a lot more is possible.

CONCLUSION

Organizations can effectively detect and mitigate fraudulent activities using access and application logs with the Elastic (ELK) stack. If you are already an Elastic customer, it is a matter of setting up the right machine learning jobs to detect the anomalies that correspond to deviant behavior. If you are not yet an Elastic customer, it is definitely time to consider the Elastic (ELK) stack since it can serve different use cases with the same data:

Infrastructure monitoring
Application monitoring
Log analytics
Security
Fraud detection
…