8. A method for outputting data based on anomaly detection, comprising: receiving a shared binary anomaly detection model; comparing, using a hardware processor, the shared binary anomaly detection model with a local anomaly detection model; combining, using the hardware processor, the shared binary anomaly detection model with the local anomaly detection model to form a new binary anomaly detection model; using the new binary anomaly detection model to determine whether an input dataset contains an anomaly, wherein the new binary anomaly detection model is used to determine whether the input dataset contains an anomaly by checking the new binary anomaly detection model for an n-gram in the input dataset; and outputting, using the hardware processor, the input dataset based on whether input dataset contains an anomaly.
8. A method for outputting data based on anomaly detection, comprising: receiving a shared binary anomaly detection model; comparing, using a hardware processor, the shared binary anomaly detection model with a local anomaly detection model; combining, using the hardware processor, the shared binary anomaly detection model with the local anomaly detection model to form a new binary anomaly detection model; using the new binary anomaly detection model to determine whether an input dataset contains an anomaly, wherein the new binary anomaly detection model is used to determine whether the input dataset contains an anomaly by checking the new binary anomaly detection model for an n-gram in the input dataset; and outputting, using the hardware processor, the input dataset based on whether input dataset contains an anomaly. 12. The method of claim 8 , wherein the combining is performed using a bitwise OR operation.