Benchmarking genetic programming in dynamic insider threat detection.

In real world applications, variation in deployment environments, such as changes in data collection techniques, can affect the effectiveness and/or efficiency of machine learning (ML) systems. In this work, we investigate techniques to allow a previously trained population of Linear Genetic Programming (LGP) insider threat detectors to adapt to an expanded feature space. Experiments show that appropriate methods can be adopted to enable LGP to incorporate the new data efficiently, hence reducing computation requirements and expediting deployment under the new conditions.