Efficient anomaly detection on sampled data streams with contaminated phase I data

Sibai, Rayane El; Abdo, Jacques Bou; Jaoude, Chady Abou; Demerjian, Jacques; Assaker, Joseph; Makhoul, Abdallah

doi:10.1002/itl2.205

Cited by 2 publications

(1 citation statement)

References 13 publications

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“…Detecting outliers is a fundamental step towards enhancing the performance of many systems. It can be applied to a wide variety of fields, such as spotting disease outbreaks [9], healthcare [10,11], detecting soil pollution [12], detecting abnormal traffic [13], and analyzing a network's abnormal and suspicious activities [14,15].…”

Section: Introductionmentioning

confidence: 99%

A New User-Based Incentive Strategy for Improving Bike Sharing Systems’ Performance

et al. 2021

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The benefits of having a Bike Sharing System (BSS) in a city are numerous. Among other advantages, it promotes a cleaner environment with less traffic and pollution. One major problem the users of such services encounter is that of full or empty stations, causing user dissatisfaction. The objective of this work is to propose a new user-based incentive method to enhance BSS performance. The proposed method relies on a spatial outlier detection algorithm. It consists of adapting the departure and arrival stations of the users to the BSS state by stimulating the users to change their journeys in view of minimizing the number of full and empty stations. Experiments are carried out to compare our proposed method to some existing methods for enhancing the resource availability of BSSs, and they are performed on a real dataset issued from a well-known BSS called Velib. The results show that the proposed strategy improves the availability of BSS resources, even when the collaboration of users is partial.

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Section: Introductionmentioning

confidence: 99%

A New User-Based Incentive Strategy for Improving Bike Sharing Systems’ Performance

et al. 2021

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SAR-BSO meta-heuristic hybridization for feature selection and classification using DBNover stream data

et al. 2023

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Advancements in cloud technologies have increased the infrastructural needs of data centers due to storage needs and processing of extensive dimensional data. Many service providers envisage anomaly detection criteria to guarantee availability to avoid breakdowns and complexities caused due to large-scale operations. The streaming log data generated is associated with multi-dimensional complexity and thus poses a considerable challenge to detect the anomalies or unusual occurrences in the data. In this research, a hybrid model is proposed that is motivated by deep belief criteria and meta-heuristics. Using Search-and-Rescue—BrainStorm Optimization (SAR-BSO), a hybrid feature selection (FS) and deep belief network classifier is used to localize and detect anomalies for streaming data logs. The significant contribution of the research lies in FS, which is carried out using SAR-BSO which increases the detection power of the model as it selects the most significant variables by minimizing redundant features. The evaluation of accuracy is efficiently improved when compared with the predictable methods, such as Extract Local Outlier Factor (ELOF), Track-plus, Hybrid Distributed Batch Stream (HDBS), IForestASD, DBN, BSO-based Feature Selection with DBN, Genetic Algorithm-Deep Belief Network (GA-DBN), Mutual Information-Deep Belief Network (MI-DBN), information entropy-Deep Belief Network(I + DBN), Flat Field-Deep Belief Network (FF + DBN), African Vulture Optimization Algorithm-Deep Belief Network(AVOA + DBN), Gorilla Troop Optimizer-Deep Belief Network(GTO-DBN), and SARO-based Feature Selection with DBN. Further, the accurate detection of the anomalies in the data stream is established by the Deep Belief Neural Network (DBN) classifier. The model’s efficacy is determined using Apache, Hadoop, HDFS, Spark, and Linux datasets and evaluated against existing similar models. The model efficiency is provided using multiple evaluation metrics and is found effective. From the experimentation, the accuracy of the proposed model is found to be 93.3, 95.4, 93.6, 94.2, and 93.5% respectively for the dataset such as Apache, Hadoop, HDFS, spark, and Linux. This enhancement in accuracy is due to the selection of optimal features by the proposed SAR-BSO algorithm.

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Efficient anomaly detection on sampled data streams with contaminated phase I data

Cited by 2 publications

References 13 publications

A New User-Based Incentive Strategy for Improving Bike Sharing Systems’ Performance

A New User-Based Incentive Strategy for Improving Bike Sharing Systems’ Performance

SAR-BSO meta-heuristic hybridization for feature selection and classification using DBNover stream data

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