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: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2024-04-01
|
| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/24/7/2263 |
| _version_ | 1797211923390922752 |
|---|---|
| author | Jaeun Choi Byunghwan Jeon Chunmi Jeon |
| author_facet | Jaeun Choi Byunghwan Jeon Chunmi Jeon |
| author_sort | Jaeun Choi |
| collection | DOAJ |
| description | 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 framework to detect new Twitter spam, which works by modeling the characteristics of non-spam tweets and using anomaly detection to classify tweets deviating from this model as anomalies. However, because modeling varied non-spam tweets is challenging, the technique’s spam detection and false positive (FP) rates are low and high, respectively. To overcome this shortcoming, anomaly detection is performed on known spam tweets pre-detected using a trained decision tree while modeling normal tweets. A one-class support vector machine and an autoencoder with high detection rates are used for anomaly detection. The proposed framework exhibits superior detection rates for unknown spam compared to conventional techniques, while maintaining equivalent or improved detection and FP rates for known spam. Furthermore, the framework can be adapted to changes in spam conditions by adjusting the costs of detection errors. |
| first_indexed | 2024-04-24T10:34:12Z |
| format | Article |
| id | doaj.art-0e2487e8420847f4aeed15e3dfbb645a |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-24T10:34:12Z |
| publishDate | 2024-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-0e2487e8420847f4aeed15e3dfbb645a2024-04-12T13:26:37ZengMDPI AGSensors1424-82202024-04-01247226310.3390/s24072263Scalable Learning Framework for Detecting New Types of Twitter Spam with Misuse and Anomaly DetectionJaeun Choi0Byunghwan Jeon1Chunmi Jeon2College of Business, Kwangwoon University, Seoul 01897, Republic of KoreaDivision of Computer Engineering, Hankuk University of Foreign Studies, Yongin 17035, Republic of KoreaCorporate Relations Office, Korea Telecom, Seoul 03155, Republic of KoreaThe 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 framework to detect new Twitter spam, which works by modeling the characteristics of non-spam tweets and using anomaly detection to classify tweets deviating from this model as anomalies. However, because modeling varied non-spam tweets is challenging, the technique’s spam detection and false positive (FP) rates are low and high, respectively. To overcome this shortcoming, anomaly detection is performed on known spam tweets pre-detected using a trained decision tree while modeling normal tweets. A one-class support vector machine and an autoencoder with high detection rates are used for anomaly detection. The proposed framework exhibits superior detection rates for unknown spam compared to conventional techniques, while maintaining equivalent or improved detection and FP rates for known spam. Furthermore, the framework can be adapted to changes in spam conditions by adjusting the costs of detection errors.https://www.mdpi.com/1424-8220/24/7/2263Twitter spamanomaly detectiondecision treeautoencoder |
| spellingShingle | Jaeun Choi Byunghwan Jeon Chunmi Jeon Scalable Learning Framework for Detecting New Types of Twitter Spam with Misuse and Anomaly Detection Sensors Twitter spam anomaly detection decision tree autoencoder |
| title | Scalable Learning Framework for Detecting New Types of Twitter Spam with Misuse and Anomaly Detection |
| title_full | Scalable Learning Framework for Detecting New Types of Twitter Spam with Misuse and Anomaly Detection |
| title_fullStr | Scalable Learning Framework for Detecting New Types of Twitter Spam with Misuse and Anomaly Detection |
| title_full_unstemmed | Scalable Learning Framework for Detecting New Types of Twitter Spam with Misuse and Anomaly Detection |
| title_short | Scalable Learning Framework for Detecting New Types of Twitter Spam with Misuse and Anomaly Detection |
| title_sort | scalable learning framework for detecting new types of twitter spam with misuse and anomaly detection |
| topic | Twitter spam anomaly detection decision tree autoencoder |
| url | https://www.mdpi.com/1424-8220/24/7/2263 |
| work_keys_str_mv | AT jaeunchoi scalablelearningframeworkfordetectingnewtypesoftwitterspamwithmisuseandanomalydetection AT byunghwanjeon scalablelearningframeworkfordetectingnewtypesoftwitterspamwithmisuseandanomalydetection AT chunmijeon scalablelearningframeworkfordetectingnewtypesoftwitterspamwithmisuseandanomalydetection |