Abstract: Systems and methods may detect alert conditions in data networks. An electronic surveillance system may learn filter parameters during a learning phase and apply the filter parameters to detect the alert conditions during a detecting phase. In the learning phase, the electronic surveillance system may learn filter parameters based on patterns in historical datasets. The electronic surveillance system may learn and evaluate the input dataset against one or more filter parameters to determine whether the input dataset should trigger an alert condition. For example, the electronic surveillance system may learn one or more filter parameters through statistical analyses, which may include machine-learning techniques. The electronic surveillance system may further match text in a dictionary filter parameter with communications, which may relate to the input datasets, to determine whether to trigger an alert condition.