Optimized Deep Isolation Forest

Abstract

Anomaly detection and the identification of elements that do not fit the data characteristics are increasingly used in information systems, both for data cleaning and for finding unusual elements. Unsupervised anomaly detection methods are particularly useful in this context. This paper introduces the Optimized Deep Isolation Forest (ODIF) as an optimized version of the Deep Isolation Forest (DIF) algorithm. The training of DIF is subjected to an optimization of the operations performed, which leads to a reduction of the computational and memory complexity. In a series of experiments, both DIF and ODIF are implemented, and their effectiveness is evaluated using Area Under the Precision-Recall Curve (PR AUC). The proposed method demonstrates significantly better detection performance compared to the baseline Isolation Forest and competitive techniques. Additionally, the execution times of the training phase are measured for both the CPU and GPU stages, as well as memory usage, including RAM and VRAM. The results unequivocally indicate a much faster execution of the ODIF algorithm compared to DIF, with average CPU stage and GPU stage times being over one and a half times and nearly 150 times shorter, respectively. Similarly, memory usage is significantly reduced for ODIF in comparison to DIF, with RAM consumption lowered by approximately 18% and VRAM by over 55%.