Isolation Forest Based Anomaly Detection: A Systematic Literature Review

Farizi, Wahid Salman Al; Hidayah, Indriana; Rizal, Muhammad Nur

doi:10.1109/icitacee53184.2021.9617498

Cited by 12 publications

(11 citation statements)

References 22 publications

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“…They extract one random characteristic at a time and divide it into homogenous partitions. However, the goal of Isolation Forest [82]- [84] is not to create homogeneous partitions, but rather to create partitions in which each datapoint is isolated (That particular isolation contains only the datapoint). The rationale underlying Isolation Trees is that a regular point is more difficult to isolate than an aberrant one.…”

Section: Isolation Forestmentioning

confidence: 99%

PAACDA: Comprehensive Data Corruption Detection Algorithm

et al. 2023

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With the advent of technology, data and its analysis are no longer just values and attributes strewn across spreadsheets, they are now seen as a stepping stone to bring about revolution in any significant field. Data corruption can be brought about by a variety of unethical and illegal sources, making it crucial to develop a method that is highly effective to identify and appropriately highlight the various corrupted data existing in the dataset. Detection of corrupted data, as well as recovering data from a corrupted dataset, is a challenging problem. This requires utmost importance and if not addressed at earlier stages may pose problems in later stages of data processing with machine or deep learning algorithms. In the following work we begin by introducing the PAACDA: Proximity based Adamic Adar Corruption Detection Algorithm and consolidating the results whilst particularly accentuating the detection of corrupted data rather than outliers. Current state of the art models, such as Isolation forest, DBSCAN also called "Density-Based Spatial Clustering of Applications with Noise" and others, are reliant on fine-tuning parameters to provide high accuracy and recall, but they also have a significant level of uncertainty when factoring the corrupted data. In the present work, the authors look into the most niche performance issues of several unsupervised learning algorithms for linear and clustered corrupted datasets. Also, a novel PAACDA algorithm is proposed which outperforms other unsupervised learning benchmarks on 15 popular baselines including K-means clustering, Isolation forest and LOF (Local Outlier Factor) with an accuracy of 96.35% for clustered data and 99.04% for linear data. This article also conducts a thorough exploration of the relevant literature from the previously stated perspectives. In this research work, we pinpoint all the shortcomings of the present techniques and draw direction for future work in this field.

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Section: Isolation Forestmentioning

confidence: 99%

PAACDA: Comprehensive Data Corruption Detection Algorithm

et al. 2023

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“…A wide set of machine learning tasks include anomaly detection problems. Therefore, many methods and models have been developed to address them [1,2,3,4,5,6,7,8,9,10,11]. One of the tools for solving the anomaly detection problems is the attention mechanism.…”

Section: Related Workmentioning

confidence: 99%

“…iForest [16,17] can be viewed as one of the important and effective methods for solving novelty and anomaly detection problems. Therefore, many modifications of the method have been developed [5] to improve it. A weighted iForest and Siamese Gated Recurrent Unit algorithm architecture which provides a more accurate and efficient method for outlier detection of data is considered in [45].…”

Section: Related Workmentioning

confidence: 99%

“…Due to importance of the anomaly detection problem in many applications, a huge amount of papers covering anomaly detection tasks and studying various aspects of the anomaly detection have been published in the last decades. Many approaches to solving the anomaly detection problem are analyzed in comprehensive survey papers [1,2,3,4,5,6,7,8,9,10,11].…”

Section: Introductionmentioning

confidence: 99%

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Improved Anomaly Detection by Using the Attention-Based Isolation Forest

Utkin¹,

Ageev²,

Konstantinov³

2022

Preprint

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A new modification of Isolation Forest called Attention-Based Isolation Forest (AB-IForest) for solving the anomaly detection problem is proposed. It incorporates the attention mechanism in the form of the Nadaraya-Watson regression into the Isolation Forest for improving solution of the anomaly detection problem. The main idea underlying the modification is to assign attention weights to each path of trees with learnable parameters depending on instances and trees themselves. The Huber's contamination model is proposed to be used for defining the attention weights and their parameters. As a result, the attention weights are linearly depend on the learnable attention parameters which are trained by solving the standard linear or quadratic optimization problem. ABIForest can be viewed as the first modification of Isolation Forest, which incorporates the attention mechanism in a simple way without applying gradient-based algorithms. Numerical experiments with synthetic and real datasets illustrate outperforming results of ABIForest. The code of proposed algorithms is available.

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“…Due to the importance of the anomaly detection problem in many applications, a huge number of papers covering anomaly detection tasks and studying various aspects of anomaly detection have been published in recent decades. Many approaches to solving the anomaly detection problem have been analyzed in comprehensive survey papers [1][2][3][4][5][6][7][8][9][10][11].…”

Section: Introductionmentioning

confidence: 99%

Improved Anomaly Detection by Using the Attention-Based Isolation Forest

et al. 2022

View full text Add to dashboard Cite

A new modification of the isolation forest called the attention-based isolation forest (ABIForest) is proposed for solving the anomaly detection problem. It incorporates an attention mechanism in the form of Nadaraya–Watson regression into the isolation forest to improve the solution of the anomaly detection problem. The main idea underlying the modification is the assignment of attention weights to each path of trees with learnable parameters depending on the instances and trees themselves. Huber’s contamination model is proposed to be used to define the attention weights and their parameters. As a result, the attention weights are linearly dependent on learnable attention parameters that are trained by solving a standard linear or quadratic optimization problem. ABIForest can be viewed as the first modification of the isolation forest to incorporate an attention mechanism in a simple way without applying gradient-based algorithms. Numerical experiments with synthetic and real datasets illustrate that the results of ABIForest outperform those of other methods. The code of the proposed algorithms has been made available.

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Isolation Forest Based Anomaly Detection: A Systematic Literature Review

Cited by 12 publications

References 22 publications

PAACDA: Comprehensive Data Corruption Detection Algorithm

PAACDA: Comprehensive Data Corruption Detection Algorithm

Improved Anomaly Detection by Using the Attention-Based Isolation Forest

Improved Anomaly Detection by Using the Attention-Based Isolation Forest

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