DeepiForest: a deep anomaly detection framework with hashing based isolation forest

With the great success of deep neural networks (DNNs) in a variety of fields, deep learning gains a pioneering development in anomaly detection. Although deep learning achieves good accuracy in anomaly detection, it is troubled with long execution time and high memory consumption. These problems are associated with the inherent drawbacks of deep learning, such as too many parameters and deep training layers. To remedy the above drawbacks, we try to explore an unsupervised non-neural network deep model for anomaly detection based on the experience of the deep forest. In this paper, we propose a deep anomaly detection framework with hashing based isolation forest (DeepiForest) to achieve effective and robust anomaly detection. Specifically, DeepiForest utilizes hashing based isolation forest and tree-embedding scheme to provide enhanced features and apply multi-layer cascaded architecture to establish a deep framework. DeepiForest inherits the advantages of deep forests, i.e., the framework holds fewer hyper-parameters and smaller model complexity than DNNs, simultaneously producing robust accuracy on anomaly detection. Extensive experiments on different-scale datasets illustrate the efficiency of DeepiForest and its comparable effectiveness to the state-of-the-art deep anomaly detection (DAD) methods.