Semi-Supervised Alert Filtering for Network Security
Network-based intrusion detection systems play a pivotal role in cybersecurity, but they generate a significant number of alerts. This leads to alert fatigue, a phenomenon where security analysts may miss true alerts hidden among false ones. To address alert fatigue, practical detection systems enab...
Main Authors: | Hyeon gy Shon, Yoonho Lee, MyungKeun Yoon |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2023-11-01
|
Series: | Electronics |
Subjects: | |
Online Access: | https://www.mdpi.com/2079-9292/12/23/4755 |
Similar Items
-
SELID: Selective Event Labeling for Intrusion Detection Datasets
by: Woohyuk Jang, et al.
Published: (2023-07-01) -
AI-Assisted Security Alert Data Analysis with Imbalanced Learning Methods
by: Samuel Ndichu, et al.
Published: (2023-02-01) -
ProMatch: Semi-Supervised Learning with Prototype Consistency
by: Ziyu Cheng, et al.
Published: (2023-08-01) -
Dataset of intrusion detection alerts from a sharing platform
by: Martin Husák, et al.
Published: (2020-12-01) -
HSSDA: Hierarchical relation aided Semi-Supervised Domain Adaptation
by: Xiechao Guo, et al.
Published: (2022-01-01)