How AWS WAF threat intelligence features help protect the player experience for betting and gaming customers

The betting and gaming industry has grown into a data-rich landscape that presents an enticing target for sophisticated bots. The sensitive personally identifiable information (PII) that is collected and the financial data involved in betting and in-game economies is especially valuable. Microtransactions and in-game purchases are frequently  targeted, making them an ideal case for safeguarding with AWS WAF.

In this blog post, we’ll explore some of these threats in more detail and explain how a layered bot mitigation strategy that uses AWS WAF can minimize the risk and impact of bot activity.

Understanding common automated threats

Automations deployed by threat actors can perform web scraping, perform betting arbitrage to gain an unfair advantage, and use automated techniques to undermine fair competition. Aggressive web scraping can also lead to application overload, service disruptions, and degraded user experience. At AWS, we routinely identify and mitigate automated threats for betting and gaming customers. Some of the common tactics we see in this space include the following:

Scraping tactics

Scraper bots often use fake accounts or compromised credentials to systematically harvest betting odds and other competitive data from multiple sites. A common example of scraping is arbitrage betting, where the scraped data is used to place simultaneous bets in different venues in order to make profits from tiny differences in the asset’s listed price. There are also competitive scraper bots that use this data to improve their betting applications.

Account-related tactics

Account creation fraud aims at claiming sign-up bonuses or other incentives at scale by using bot-generated accounts. Account takeover fraud aims at logging into user accounts to change account details, make purchases, withdraw funds, steal personal information or loyalty points, or use this data to access other accounts on different websites. A common form of this tactic is automated brute force login techniques, such as credential stuffing.

Denial-of-service tactics

Volumetric floods can cause betting and gaming sites to experience slow page-loads, downtime, and damaged brand reputation. DDoS attacks are another common security concern for many customers.

In-game tactics

In-game bots can use automated cheating or expediting techniques to manipulate resources and gain unfair advantages. These bots typically manipulate client applications and make malicious API requests.

AWS WAF intelligent threat mitigation features

To help protect customers from such automated tactics, AWS WAF offers the following intelligent threat mitigation features.

AWS WAF Common Bot Control managed rule group

The AWS WAF Common Bot Control managed rule group uses static analysis to identify web requests and header information that is correlated with known good bots and bad bots. These techniques are helpful in detecting a variety of self-identifying bots, such as web scraping frameworks, search engines, and automated browsers. Using these predetermined patterns and signatures can help gaming customers to identify and block known bot behaviors.

CAPTCHA and challenge rule actions

CAPTCHA rule action – Configured rules in AWS WAF can have a CAPTCHA action. When a rule is configured with a CAPTCHA action, users are required to solve a puzzle to prove that a human being is sending the request. When a user successfully solves a CAPTCHA challenge, a token is placed on their browser so it won’t challenge future requests, using a configurable immunity time. Learn about best practices for configuring CAPTCHA.

Challenge rule action – Challenge scripts run a silent challenge that requires the client session to verify that it’s a browser and not a bot. The verification runs in the background without involving the end user. Challenge-based bot detection can check each visitor’s ability to run JavaScript and store cookies. When a challenge is solved correctly, AWS WAF vends out an AWS WAF token, as seen in Figure 1, which allows bot control to track user activity across sessions. A reduced ability to process these challenges is a sign of bot traffic. The challenge action is a good option for verifying clients that you suspect of being invalid. You can use this feature by setting a selected AWS WAF rule action to CHALLENGE or by using a targeted bot control managed rule group. To learn more about protecting against bots with the AWS WAF challenge and CAPTCHA actions, see this blog post.

Figure 1: A sequence diagram explaining the flow of requests when Challenge is set as a rule action for an AWS WAF rule

Client application integration

AWS WAF provides the following levels of integration.

Intelligent threat integration

To improve the user experience and reduce latency for mobile and API-driven applications, AWS WAF provides client-side application APIs to integrate with your application. These integrations help verify that the client applications that send web requests to your protected resources are the intended clients and that your end users are human beings. This functionality is available for JavaScript and for Android and iOS mobile applications. As shown in Figure 2, the token acquisition process is similar to a challenge action, but slightly different. The basic approach for using the SDK is to create a token provider by using a configuration object, then to use the token provider to retrieve tokens from AWS WAF. By default, the token provider includes the retrieved tokens in your web requests to your protected resource. The intelligent threat integration APIs work with web access control lists (ACLs) that use the intelligent threat rule groups to enable the full functionality of these advanced managed rule groups. You can use the AWS WAF mobile SDKs to implement AWS WAF intelligent threat integration SDKs for Android and iOS mobile applications.

Figure 2: A sequence diagram explaining the flow of requests when AWS WAF intelligent threat mitigation SDKs are configured

CAPTCHA JavaScript integration

You can also verify end users by making them solve customized CAPTCHA puzzles that you manage in your application. This is similar to the functionality provided by the AWS WAF CAPTCHA rule action, but with added control over the puzzle placement and behavior. This integration uses the JavaScript intelligent threat integration to run silent challenges and provide AWS WAF tokens to the customer’s page.

AWS WAF Targeted Bot Control

The AWS WAF Targeted Bot Control tier includes the common-level protections described earlier and adds targeted detection for sophisticated bots that don’t self-identify. Targeted protections mitigate bot activity by using a combination of rate limiting and CAPTCHA and background browser challenges. Targeted protections use detection techniques such as the following:

• Implementing browser fingerprinting – Browser fingerprinting is a powerful tracking and identification technique employed by online gaming sites to gain deep insights into their players’ computing setups. This technique involves probing the unique characteristics and configuration of each gamer’s browser. By collecting dozens of browser data points, a fingerprint can be generated that allows the requests coming from that specific browser to be identified and tracked across gaming sessions. Even if players try to randomize or spoof some browser attributes, perhaps in an attempt to bypass certain restrictions or gain an unfair advantage, the overall fingerprint still allows detection of such attempts. For example, if the user agent claims to be Chrome on Windows but other fingerprint attributes indicate Linux and Firefox, that suggests an attempted spoofing by the player, which can then be flagged by the gaming site’s security measures.

  By using browser fingerprinting and looking for discrepancies, gaming and betting sites gain tools to help detect and block sophisticated bots even when the bots try to mask their true identity and intent. AWS WAF uses tokens for detecting browser inconsistencies, such as when the characteristics of a browser do not match the user agent. The AWS Targeted Bot Control rule group offers this functionality by emitting labels like TGT_SignalBrowserInconsistency, and the recommended mitigation action for inconsistent browsers is to serve a CAPTCHA puzzle.

• Detecting browser automation – Many threat actors who operate automated programs use scripting languages to carry out their tasks, such as data scraping or launching exploits. They often employ tools that mimic the behavior of a web browser to bypass security measures. To address these challenges, AWS WAF Bot Control offers solutions to help detect and block automated software that simulates browser activity. It uses specific rules like TGT_SignalAutomatedBrowser to examine requests for signs that suggest the browser is not operated by a human, helping to identify and mitigate potential threats from automated systems.
• Understand normal volumetric activity with unique browser ID tracking – AWS WAF Targeted Bot Control monitors application visitors by assigning each one a unique browser ID (UBID) embedded in a token. It establishes baselines for the number of requests a client sends within a five-minute session and sets three thresholds per device: high, medium, and low. The system identifies clients that exceed normal request rates and challenges them with a CAPTCHA puzzle using the TGT_VolumetricSession rule. For verified bots, the rule group takes no action but labels the traffic with awswaf:managed:aws:bot-control:bot:verified.
• Using real-time machine learning models for clustering and behavior analysis – Traditional solutions to fight advanced bots faced limitations: handling massive amounts of player traffic, accurately identifying bots without labeling every request (ground truth), and staying cost-effective. To address these challenges, the AWS WAF team created a machine learning model. This model finds hidden bot networks by analyzing patterns in website traffic. It automatically analyzes traffic statistics to identify suspicious activity that suggests coordinated bot activity.

  The model aggregates data at different levels, including the client, session, and behavioral cluster levels. It uses features like session statistics, behavioral cluster information (derived from clustering), and relative entropy to identify suspicious behavior. This feature analyzes web traffic every few minutes and optimizes the analysis for the detection of low intensity, long-duration bots that are distributed across many IP addresses. AWS WAF emits the labels TGT_ML_CoordinatedActivityMedium and TGT_ML_CoordinatedActivityHigh, based on the confidence level of the detection.

  This machine learning capability is included by default in the AWS WAF Targeted Bot Control rules, but you can choose to disable it if needed.

AWS WAF Fraud Control: Account creation fraud prevention

Fraudulent account creation involves the creation of fake accounts for activities such as bonus abuse, impersonation, and phishing. These fake accounts can damage your reputation and expose you to financial fraud. To help prevent account creation fraud, we recommend using the AWS WAF Fraud Control account creation fraud prevention (ACFP) feature. This feature is available in the AWS Managed Rules rule group AWSManagedRulesACFPRuleSet, along with companion application integration SDKs. By integrating this feature into your system, you can effectively monitor and control account creation attempts, helping to provide a safer and more secure environment for your customers.

AWS WAF Fraud Control: Account takeover prevention

Threat actors might try to gain unauthorized access to a player’s account by using stolen credentials, guessing passwords through brute-force exploits, or other means. After they gain access, they can steal money, information, or services, or even pose as the victim to gain access to other accounts. This can lead to financial loss and damage to your reputation. To help prevent account takeovers, we recommend using the AWS WAF Fraud Control account takeover prevention (ATP) feature. This feature is available in the AWS Managed Rules rule group AWSManagedRulesATPRuleSet, along with companion application integration SDKs.

Conclusion

Bot management involves choosing controls to identify traffic coming from bots, and then blocking undesired traffic. The more threat actors are motivated to target a web application, the more they will invest in detection evasion techniques, requiring more advanced mitigation capabilities. We recommend that you adopt a layered approach to managing bots, with differentiated tooling that is adapted to specific bot tactics.

Ready to start putting the tools in place to protect your gaming or betting application from sophisticated bot threats? Check out our solution overview guide for AWS WAF and the Implementing a bot control strategy on AWS whitepaper to learn more about deploying a layered bot mitigation strategy on AWS. You can also sign up for an AWS Activation Day to work directly with our experts on implementing capabilities like AWS WAF, AWS WAF Bot Control, and AWS Shield for your specific use case. For hands-on experience, try our bot mitigation workshops—you can enable managed rule groups like Bot Control in just a few steps. Start your proof-of-concept by contacting your AWS account representative today.

 
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