Progress in Outlier Detection Techniques: A Survey

Wang, Hongzhi; Bah, Mohamed Jaward; Hammad, Mohamed

doi:10.1109/access.2019.2932769

Cited by 376 publications

(172 citation statements)

References 180 publications

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“…Due to the long history and diversity of AD research, there exists a wealth of review and survey literature [157]- [176] and books [177]- [179] on the topic. Some very recent surveys focus specifically on deep AD [180]- [182].…”

Section: Introductionmentioning

confidence: 99%

A Unifying Review of Deep and Shallow Anomaly Detection

et al. 2021

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Section: Introductionmentioning

confidence: 99%

A Unifying Review of Deep and Shallow Anomaly Detection

et al. 2021

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“…We surveyed three types of papers, namely review, general purpose RL and RL applied to anomaly detection. Comprehensive surveys on network anomaly detection including algorithms, experiments and analyses were done in [10][11][12][13]. Deep Learning for anomaly detection were surveyed in [14,15].…”

Section: Related Researchesmentioning

confidence: 99%

Application of Improved Asynchronous Advantage Actor Critic Reinforcement Learning Model on Anomaly Detection

Zhou

Wang

et al. 2021

Entropy

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Anomaly detection research was conducted traditionally using mathematical and statistical methods. This topic has been widely applied in many fields. Recently reinforcement learning has achieved exceptional successes in many areas such as the AlphaGo chess playing and video gaming etc. However, there were scarce researches applying reinforcement learning to the field of anomaly detection. This paper therefore aimed at proposing an adaptable asynchronous advantage actor-critic model of reinforcement learning to this field. The performances were evaluated and compared among classical machine learning and the generative adversarial model with variants. Basic principles of the related models were introduced firstly. Then problem definitions, modelling processes and testing were detailed. The proposed model differentiated the sequence and image from other anomalies by proposing appropriate neural networks of attention mechanism and convolutional network for the two kinds of anomalies, respectively. Finally, performances with classical models using public benchmark datasets (NSL-KDD, AWID and CICIDS-2017, DoHBrw-2020) were evaluated and compared. Experiments confirmed the effectiveness of the proposed model with the results indicating higher rewards and lower loss rates on the datasets during training and testing. The metrics of precision, recall rate and F1 score were higher than or at least comparable to the state-of-the-art models. We concluded the proposed model could outperform or at least achieve comparable results with the existing anomaly detection models.

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“…Then, the scoring sample function in the IForest design is replaced by the proposed mass-based local outlier factor to acquire the outlier degree of the individual sample. For a more detailed discussion of outlier detection methods along with their similarities and differences, readers may refer to survey papers such as [5], [12], [24], [25].…”

Section: ) Semi-supervised Outlier Detectionmentioning

confidence: 99%

A Mass-Based Approach for Local Outlier Detection

Hoang

Mau

et al. 2021

IEEE Access

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This paper proposes a new outlier detection approach that measures the degree of outlierness for each instance in a given dataset. The proposed model utilizes a mass-based dissimilarity measure to address the weaknesses of neighbor-based outlier models while detecting local outliers in the dataset within a variety of data point densities. In particular, it first applies a hierarchical partitioning technique to generate a set of tree-like nested structure partitions for the input dataset, and then a mass-based dissimilarity measure is defined to quantify the dissimilarity between two data instances given the generated hierarchical partition structure. After that, for each data instance, a context set is obtained by gathering the neighbors around it with the k lowest mass dissimilarities, and based on those context sets, a mass-based local outlier score model is introduced to compute the outlierness for each individual instance. The proposed approach fundamentally changes the perspective of the outlier model by using the mass-based measurement instead of the distance-based functions used in most neighbor-based methods. A comprehensive experiment conducted on both synthetic and real-world datasets demonstrates that the proposed approach is not only competitive with the existing state-of-the-art outlier detection models but is also an efficient and effective alternative for local outlier detection methods.

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Progress in Outlier Detection Techniques: A Survey

Cited by 376 publications

References 180 publications

A Unifying Review of Deep and Shallow Anomaly Detection

A Unifying Review of Deep and Shallow Anomaly Detection

Application of Improved Asynchronous Advantage Actor Critic Reinforcement Learning Model on Anomaly Detection

A Mass-Based Approach for Local Outlier Detection

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