Addressing the limitations of LOF anomaly detection algorithm

such as with high time and space complexity and insensitivity to cross anomalies and outliers around low-density clusters

this paper proposes isolation-based data extracting LOF (iDELOF) anomaly detection algorithm

which puts the isolation-based K-nearest-neighbor search space extraction (iKSSE) in front of LOF

to efficiently cut out a large amount of useless and interfering data and obtain a more accurate search space. Based on this

the theoretical and four groups of experimental analysis are completed

and in each group of experiments

iDELOF is compared with many typical algorithms such as LOF

iForest and iNNE. The results show that iDELOF improves the detection capabilities of LOF by widening the gap between the local outlier factor of normal and abnormal points

and enhancing the ability to identify cross anomalies and abnormal points around low-density clusters.Additionally

iDELOF has the same obvious superiority as LOF in identifying axis-parallel anomalies. The data subset obtained by iDELOF through iKSSE is significantly smaller than the original dataset and the data volume of most subsets is less than 1% of the original dataset. Therefore

the time and space complexity of iDELOF is significantly reduced

and the larger the amount of data in the original dataset

the more obvious the superiority is. When the amount of data is large enough

the running time of iDELOF will be lower than that of the IF algorithm.