Outlier Detection

Boukerche, Azzedine; Zheng, Lining; Alfandi, Omar

doi:10.1145/3381028

Cited by 198 publications

(61 citation statements)

References 88 publications

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“…For Offline NS-NMF, we set α � 0.8 and c � 0.2; for Online NS-NMF, we set α � 0.8 and z � 20. In GNMF, we set the neighborhood graph construction Input: Origin data X and the number of anomaly samples N Factorization matrices W i 􏼈 􏼉 and {H i }, i � 1, ..., l Output: e selected N anomaly samples (1) parameter k in kNN as 5. Besides, we use 0-1 weighting as the weighting method.…”

Section: Comparison With the State-of-the-art Methodsmentioning

confidence: 99%

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Graph Regularized Deep Sparse Representation for Unsupervised Anomaly Detection

Lai

Jiang

et al. 2021

Computational Intelligence and Neuroscience

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Anomaly detection (AD) aims to distinguish the data points that are inconsistent with the overall pattern of the data. Recently, unsupervised anomaly detection methods have aroused huge attention. Among these methods, feature representation (FR) plays an important role, which can directly affect the performance of anomaly detection. Sparse representation (SR) can be regarded as one of matrix factorization (MF) methods, which is a powerful tool for FR. However, there are some limitations in the original SR. On the one hand, it just learns the shallow feature representations, which leads to the poor performance for anomaly detection. On the other hand, the local geometry structure information of data is ignored. To address these shortcomings, a graph regularized deep sparse representation (GRDSR) approach is proposed for unsupervised anomaly detection in this work. In GRDSR, a deep representation framework is first designed by extending the single layer MF to a multilayer MF for extracting hierarchical structure from the original data. Next, a graph regularization term is introduced to capture the intrinsic local geometric structure information of the original data during the process of FR, making the deep features preserve the neighborhood relationship well. Then, a L1-norm-based sparsity constraint is added to enhance the discriminant ability of the deep features. Finally, a reconstruction error is applied to distinguish anomalies. In order to demonstrate the effectiveness of the proposed approach, we conduct extensive experiments on ten datasets. Compared with the state-of-the-art methods, the proposed approach can achieve the best performance.

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Section: Comparison With the State-of-the-art Methodsmentioning

confidence: 99%

“…Anomaly detection (AD) aims at finding the part of data that do not conform with the expected behavior [ 1 ]. These data are usually called outliers, anomalies, and so on.…”

Section: Introductionmentioning

confidence: 99%

Graph Regularized Deep Sparse Representation for Unsupervised Anomaly Detection

Lai

Jiang

et al. 2021

Computational Intelligence and Neuroscience

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“…Estevez-Tapiador et al presented a wired-based network intrusion detection based on anomaly detection [41]. Boukerche et al presented an outlier-based classified detection approach using the unsupervised and supervised models [42]. Under the supervised category, a proximity-based technique has been used recently [43].…”

Section: Related Workmentioning

confidence: 99%

An Ensemble of Prediction and Learning Mechanism for Improving Accuracy of Anomaly Detection in Network Intrusion Environments

2021

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The connectivity of our surrounding objects to the internet plays a tremendous role in our daily lives. Many network applications have been developed in every domain of life, including business, healthcare, smart homes, and smart cities, to name a few. As these network applications provide a wide range of services for large user groups, the network intruders are prone to developing intrusion skills for attack and malicious compliance. Therefore, safeguarding network applications and things connected to the internet has always been a point of interest for researchers. Many studies propose solutions for intrusion detection systems and intrusion prevention systems. Network communities have produced benchmark datasets available for researchers to improve the accuracy of intrusion detection systems. The scientific community has presented data mining and machine learning-based mechanisms to detect intrusion with high classification accuracy. This paper presents an intrusion detection system based on the ensemble of prediction and learning mechanisms to improve anomaly detection accuracy in a network intrusion environment. The learning mechanism is based on automated machine learning, and the prediction model is based on the Kalman filter. Performance analysis of the proposed intrusion detection system is evaluated using publicly available intrusion datasets UNSW-NB15 and CICIDS2017. The proposed model-based intrusion detection accuracy for the UNSW-NB15 dataset is 98.801 percent, and the CICIDS2017 dataset is 97.02 percent. The performance comparison results show that the proposed ensemble model-based intrusion detection significantly improves the intrusion detection accuracy.

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“…en, a neighborhood procedure based on rough sets is applied to detect prototype outliers. Interested readers may refer to [17] for more in-depth understanding of outlier detection. Moreover, ANN has been applied to solve real-world problems in various application domains such as medical diagnosis [18], pattern recognition [19], and other related applications.…”

Section: Introductionmentioning

confidence: 99%

Application of Radial Basis Function Neural Network Coupling Particle Swarm Optimization Algorithm to Classification of Saudi Arabia Stock Returns

Rashedi

Ismail

Hamadneh

et al. 2021

Journal of Mathematics

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Artificial intelligence (AI) based business process optimization has a significant impact on a country’s economic development. We argue that the use of artificial neural networks in business processes will help optimize these processes ensuring the necessary level in the functioning and compliance with the foundations of sustainable development. In this paper, we proposed a mathematical model using AI to detect outliers in the daily return of Saudi stock market (Tadawul). An outlier is defined as a data point that deviates too much from the rest of the observations in a data sample. Based on the Engle and Granger Causality test, we selected inflation rate, repo rate, and oil prices as input variables. In order to build the mathematical model, we first used the Tukey method to detect outliers in the stock return data from Tadawul that are collected during the period from October 2011 to December 2019. In this way, we categorized the stock return data into two classes, namely, outliers and nonoutliers. These data are further used to train artificial neural network in conjunction with particle swarm optimization algorithm. In order to assess the performance of the proposed model, we employed the mean squared error function. Our proposed model is signified by the mean squared error value of 0.05. The proposed model is capable of detecting outlier values directly from the inflation rate, repo rate, and oil prices. The proposed model can be helpful in developing and applying intelligent optimization techniques to solve problems in business processes.

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Outlier Detection

Cited by 198 publications

References 88 publications

Graph Regularized Deep Sparse Representation for Unsupervised Anomaly Detection

Graph Regularized Deep Sparse Representation for Unsupervised Anomaly Detection

An Ensemble of Prediction and Learning Mechanism for Improving Accuracy of Anomaly Detection in Network Intrusion Environments

Application of Radial Basis Function Neural Network Coupling Particle Swarm Optimization Algorithm to Classification of Saudi Arabia Stock Returns

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