In its broadest definition, a bot is an autonomous program designed to perform specific tasks online. Initially created for simple functions, bots have evolved to handle more complex tasks, which can have positive and negative impacts.

What are bots in mobile fraud?

In mobile fraud, bots are automated programs that can operate on real mobile devices or servers, mimicking legitimate user actions such as ad clicks, installs, and in-app engagement. This simulation aims to deceive systems into recognizing fraudulent activities as genuine.

Another type of fraud bot is malware/mobile malware installed on a user’s device. These malware bots generate fake ad impressions, fraudulent clicks, and in-app engagement, and can even initiate fake in-app purchases, all without the user’s consent or awareness.

How to block mobile fraud bots

• Closed-source SDKs – ensure your attribution provider uses closed-source SDK technology. Unlike open-source SDKs, closed-source codes are significantly harder for fraudsters to unpack and simulate, as the code is not publicly exposed for review and reverse engineering. Review all SDKs in your app, particularly attribution SDKs, and avoid those that use open-source technology to prevent security breaches.
• SDK security measures – implement hashing or unique tokens to block bot activity in real time. Always use the latest SDK version from your attribution provider to benefit from the most recent security updates and defenses against known bot tactics.
  • Hashing – this process transforms data into a fixed-size hash value, ensuring that sensitive information remains secure during transmission. Hashing helps to verify data integrity and detect any unauthorized changes.
  • Unique tokens – these are dynamically generated, single-use tokens that verify the authenticity of each request. By using unique tokens, you can ensure that each interaction is legitimate and prevent replay attacks where bots attempt to reuse old tokens to gain unauthorized access.
  • Encrypted communication – ensuring that all data transmitted between the app and the server is encrypted adds an additional layer of security, making it more difficult for bots to intercept and manipulate data.
  • Certificate pinning – this technique involves associating a host with their expected X.509 certificate or public key. By doing so, it prevents man-in-the-middle attacks, ensuring that the app communicates only with trusted servers.
  • Dynamic key generation – implementing dynamic keys that change with each session makes it harder for bots to crack the security measures in place, as they would need to break the encryption for each session individually.
  • Rate limiting and throttling – these measures help to control the number of requests a client can make to the server within a certain time frame. By setting these limits, you can prevent bots from overwhelming your system with requests, making it easier to detect and block suspicious activity.
• Behavioral analysis and anomaly detection – monitoring active user behavior and identifying patterns that deviate from normal activities can help detect bot activities. Advanced solutions like Protect360 use proprietary behavioral anomaly detection to identify and block sources generating non-human traffic automatically.
• Bot signatures – fraud solutions maintain a real-time database of bot signatures, automatically blacklisting and blocking activities from known fraudulent sources. These signatures include patterns of behavior, known IP addresses, device identifiers, and other unique markers that are characteristic of bot activities. By continuously updating this database with new signatures, fraud solutions can swiftly block any traffic that matches these patterns, effectively preventing bots from causing harm.
• Behavioral anomalies – identify unusual behavior patterns, such as a high density of installs that follow identical, non-human actions. Solutions like Protect360 use proprietary behavioral anomaly detection to block sources generating such traffic automatically. This detection system monitors user interactions and flags behaviors that deviate significantly from typical human patterns, such as extremely rapid clicks, uniform time intervals between actions, or consistent usage patterns across multiple devices. By analyzing these anomalies, the system can distinguish between genuine user activity and automated bot behavior, ensuring that only legitimate interactions are allowed through. This process involves sophisticated machine learning algorithms that continuously learn and adapt to new bot behaviors, providing a robust defense against evolving threats.