Anomaly detection, which is used in a variety of applications, attracts attention both in industry and academia. Among numerous methods for anomaly detection, the Isolation Forest algorithm, whose characteristics include high efficiency, sound detection accuracy, has wide realworld applications. However, the conventional Isolation forest algorithm can hardly deal with largescale data sets. To break this limitation, we propose a cloud computing platform based algorithm. Specifically, we design and implement a parallel algorithm for anomaly detection based on Isolation Forest, named PIFH,using the Hadoop distributed storage system and the MapReduce distributed computational framework. By parallelizing the processes of detection model construction and anomaly evaluation, its efficiency is improved, and the application range is also extended. Experiments using realworld data sets demonstrate that the proposed algorithm is efficient and scalable.