Authors : K. Srinarayani; K. Jagadeeswar Reddy; C. Manikanta Reddy; C. Shanmukh Pranav

Volume/Issue : Volume 9 - 2024, Issue 5 - May

Google Scholar : https://tinyurl.com/3mcbap8w

Scribd : https://tinyurl.com/nhh7nya6

DOI : https://doi.org/10.38124/ijisrt/IJISRT24MAY229

Abstract : Outlier detection in real-time from multivariate streaming data is an important research subject in numerous areas. The new presentation of gradual Neighborhood Anomaly Variable (iLOF) and its variations has acquired consideration for their high recognition execution in information streams with evolving circulations. This paper presents a new intelligent exception location framework called include rich intelligent anomaly discovery, which integrates human interaction into the detection process to improve performance and simplify detection. It offers interactive mechanism that allows for instinctive client cooperation during every vital stage of the fundamental anomaly recognition calculation, for example, thick cell choice, area mindful distance thresholding, and last top exception approval. This approach helps resolve the challenge of specifying key parameters like density and distance thresholds in other outlier detection methods. Additionally, the system proposes an innovative optimization method to enhance grid-based space partitioning. The Local Outlier Factor (LOF) measures the level of outlierness of each occasion in light of the conveyance thickness in the dataset. Higher LOF values indicate a higher likelihood of an instance being an outlier. Instances with LOF values above a set threshold are identified as outliers. The calculation of LOF values involves several steps, which are detailed in original articles for further reference.

Keywords : Anomaly Detection, Big Data, Big Data Quality, Data Quality Dimensions, Quality Anomaly Score, Outlier Detection, Isolation Forest, Local Outlier Factor.

References :

1. Elouataoui Widad, Elmendili Saida, Youssef Gahi Quality Anomaly Detection Using Predictive Techniques: An Extensive Big Data Quality Framework for Reliable Data Analysis IEEE Access, 2023
2. Shichao Zhou, Wenzheng Wang, Chentao Gao Learning-Free Hyperspectral Anomaly Detection With Unpredictive Frequency Residual Priors IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2022
3. Joanna Kosinska, Maciej Tobiasz Detection of Cluster Anomalies With ML Techniques IEEE Access, 2022
4. Ata-Ur-Rehman, Sameema Tariq, Haroon Farooq, Abdul Jaleel, Syed Muhammad Wasif Anomaly Detection With Particle Filtering for Online Video Surveillance IEEE Access, 2021
5. Maryam Assafo, Peter Langend Arfer A TOPSIS-Assisted Feature Selection Scheme and SOM-Based Anomaly Detection for Milling Tools Under Different Operating Conditions IEEE Access, 2021
6. Qiqi Zhu,Li Sun Big Data Driven Anomaly Detection for Cellular Networks IEEE Access, 2020
7. Scott Miau,Wei-Hsi Hung River Flooding Forecasting and Anomaly Detection Based on Deep Learning IEEE Access, 2020
8. Tsotsope Daniel Ramotsoela, Gerhard Petrus Hancke, Adnan M. Abu-Mahfouz Behavioural Intrusion Detection in Water Distribution Systems Using Neural Networks IEEE Access, 2020
9. Yumna Zahid, Muhammad Atif Tahir, Nouman M. Durrani, Ahmed Bouridane IBaggedFCNet: An Ensemble framework for anomaly detection IEEE access, 2020
10. Pan Xiong, Cheng Long, Huiyu Zhou, Xuemin Zhang, Xuhui Shen GNSS TEC-Based Earthquake Ionospheric Perturbation Detection Using a Novel Deep Learning Framework IEEE Journal of Selected Topics in Applied Earth observations and Remote sensing, 2022