Scalable Learning Framework for Detecting New Types of Twitter Spam with Misuse and Anomaly Detection
The growing popularity of social media has engendered the social problem of spam proliferation through this medium. New spam types that evade existing spam detection systems are being developed continually, necessitating corresponding countermeasures. This study proposes an anomaly detection-based f...
Main Authors: | Jaeun Choi, Byunghwan Jeon, Chunmi Jeon |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2024-04-01
|
Series: | Sensors |
Subjects: | |
Online Access: | https://www.mdpi.com/1424-8220/24/7/2263 |
Similar Items
-
Cost-Based Heterogeneous Learning Framework for Real-Time Spam Detection in Social Networks With Expert Decisions
by: Jaeun Choi, et al.
Published: (2021-01-01) -
Statistical Twitter Spam Detection Demystified: Performance, Stability and Scalability
by: Guanjun Lin, et al.
Published: (2017-01-01) -
EGMA: Ensemble Learning-Based Hybrid Model Approach for Spam Detection
by: Yusuf Bilgen, et al.
Published: (2024-10-01) -
Drifted Twitter Spam Classification Using Multiscale Detection Test on K-L Divergence
by: Xuesong Wang, et al.
Published: (2019-01-01) -
A Survey of Attacks Against Twitter Spam Detectors in an Adversarial Environment
by: Niddal H. Imam, et al.
Published: (2019-07-01)