An incremental and approximate local outlier probability algorithm for intrusion detection and its evaluation. Issue 2 (2nd April 2017) - Hamlet, Connor; Straub, Jeremy; Russell, Matthew; Kerlin, Scott
ABSTRACT: This paper proposes a novel incremental modification to the Local Outlier Probabilities algorithm, which is commonly used for anomaly detection, to allow it to detect outliers nearly instantly in data streams. The proposed incremental algorithm's strength is based on denying the insertion of incremental points into the data set. This precludes the anomaly scores of other points having to update (saving valuable computational time) while resulting in a small amount of error, as compared to an exact approach. This work aims to allow low-resource machines, such as small or older satellites, to perform incremental anomaly detection on large static data sets quickly, trading accuracy impairment for speed of detection.