mFaaS

mFaaS uses machine learning algorithms to analyze traffic patterns and identify anomalies that are indicative of fraud. These models can learn and adapt to new fraud tactics, making the detection system more effective over time.

mFaaS can identify traffic coming from device farms (collections of devices used for fraudulent activities) and IP address pools often associated with VPNs or proxies. This helps detect sophisticated fraud attempts that try to mask their origin.

mFaaS analyzes the reputation of IP addresses and dissects traffic patterns to identify suspicious activity. This involves looking at factors like the history of the IP address, the volume of traffic coming from it, and the types of requests being made.

This involves generating fake clicks on ads to artificially inflate click counts and ad revenue. Spamming refers to automated clicks, while injection involves inserting clicks into legitimate traffic.

Bots are automated programs that can simulate human activity, while VPNs can mask the true IP address of a user. Both are used to perpetrate various types of ad fraud.

This involves manipulating device identifiers to create fake installs, generate fraudulent clicks, or perform other types of fraud.

This refers to fraudulent or malicious APK (Android application package) files that are distributed to trick users into installing them. These apps might generate fake clicks, steal user data, or perform other harmful actions.

This occurs when traffic is falsely attributed to a different geographic location, often to take advantage of higher ad rates in certain regions.

This involves mixing incentivized traffic (users who are paid to perform actions) with organic traffic, making it difficult to accurately assess the performance of ad campaigns.

This refers to inconsistencies between the Mobile Network Operator (MNO) and Mobile Network Code (MNC) reported, which can indicate fraudulent activity.

mFaaS analyzes traffic patterns over time to identify unusual spikes or dips that might suggest fraudulent activity. This includes looking at both device-level and network-level data.

Similar to hourly distribution patterns, this involves a broader analysis of traffic patterns to detect anomalies and suspicious behavior.

This involves tracking how often users interact with ads or apps to identify unusual patterns that might suggest bot activity or other forms of fraud.

This involves manipulating the Software Development Kit (SDK) used to track app installs and events, leading to inaccurate data and fraudulent attribution.

This refers to generating fake in-app events, such as purchases or registrations, to inflate app metrics and defraud advertisers.

mFaaS provides various tools and settings to fine-tune how traffic is blocked, allowing users to balance protection with legitimate traffic flow.

Users can set custom rules for blocking traffic at different levels of the advertising ecosystem, giving them granular control over their fraud prevention strategy.

Fraudulent traffic sources can be blocked in real time, preventing further fraudulent activity and minimizing losses.

mFaaS uses both global blacklists (lists of known fraudulent IPs and devices) and local blacklists (custom lists created by the user) to provide comprehensive protection.