Reduction of Alert Fatigue using Extended Isolation Forest-Reference-Cited by-同舟云学术

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Reduction of Alert Fatigue using Extended Isolation Forest

Published:2021-12-21 Issue: Volume: Page:
ISSN:
Container-title:2021 International Conference on Forensics, Analytics, Big Data, Security (FABS)
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Author:

Ahmed Tariq¹,Shah Aayush¹,Kolla Morarjee²,Yellasiri Ramadevi³

Affiliation:

1. Chaitanya Bharathi Institute of Technology,B.E. Student, Department of CSE,Hyderabad,India

2. Chaitanya Bharathi Institute of Technology,Department of CSE,Hyderabad,India

3. Chaitanya Bharathi Institute of Technology,Professor, Department of CSE,Hyderabad,India

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9702519/9702543/09702617.pdf?arnumber=9702617

Reference21 articles.

1. Evaluation of Machine Learning-based Anomaly Detection Algorithms on an Industrial Modbus/TCP Data Set

2. Using Classification with K-means Clustering to Investigate Transaction Anomaly;tan;2020 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM) IEEM,2020

3. Healthcare and anomaly detection: using machine learning to predict anomalies in heart rate data;šabi?;AI & Society,2021

4. Network Anomaly Detection for NSL-KDD Dataset Using Deep Learning;kavitha;INFORMATION TECHNOLOGY IN INDUSTRY,2021

5. Which Outlier Detector Should I use?

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. On the Use of AutoML for Combating Alert Fatigue in Security Operations Centers;Lecture Notes in Computer Science;2024

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