Random Partitioning Forest for Point-Wise and Collective Anomaly Detection -- Application to Intrusion Detection

TL;DR

This paper introduces DiFF-RF, an ensemble of random partitioning trees that effectively detects point-wise and collective anomalies, outperforming existing methods in intrusion detection and small data scenarios.

Contribution

DiFF-RF is a novel semi-supervised ensemble method that improves anomaly detection by incorporating distance-based leaf analysis and frequency counting, addressing limitations of the isolation forest.

Findings

DiFF-RF outperforms isolation forest and other baselines in anomaly detection tasks.

Effective in small data environments where deep learning models struggle.

Computationally efficient and easily parallelizable.

Abstract

In this paper, we propose DiFF-RF, an ensemble approach composed of random partitioning binary trees to detect point-wise and collective (as well as contextual) anomalies. Thanks to a distance-based paradigm used at the leaves of the trees, this semi-supervised approach solves a drawback that has been identified in the isolation forest (IF) algorithm. Moreover, taking into account the frequencies of visits in the leaves of the random trees allows to significantly improve the performance of DiFF-RF when considering the presence of collective anomalies. DiFF-RF is fairly easy to train, and excellent performance can be obtained by using a simple semi-supervised procedure to setup the extra hyper-parameter that is introduced. We first evaluate DiFF-RF on a synthetic data set to i) verify that the limitation of the IF algorithm is overcome, ii) demonstrate how collective anomalies are actually detected and iii) to analyze the effect of the meta-parameters it involves. We assess the DiFF-RF algorithm on a large set of datasets from the UCI repository, as well as two benchmarks related to intrusion detection applications. Our experiments show that DiFF-RF almost systematically outperforms the IF algorithm, but also challenges the one-class SVM baseline and a deep learning variational auto-encoder architecture. Furthermore, our experience shows that DiFF-RF can work well in the presence of small-scale learning data, which is conversely difficult for deep neural architectures. Finally, DiFF-RF is computationally efficient and can be easily parallelized on multi-core architectures.