IDU-Detector: A Synergistic Framework for Robust Masquerader Attack Detection
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866915012574969856 |
|---|---|
| author | Huang, Zilin Li, Xiulai Cao, Xinyi Chen, Ke Wang, Longjuan Liu, Logan Bo-Yee |
| author_facet | Huang, Zilin Li, Xiulai Cao, Xinyi Chen, Ke Wang, Longjuan Liu, Logan Bo-Yee |
| contents | In the digital age, users store personal data in corporate databases, making data security central to enterprise management. Given the extensive attack surface, assets face challenges like weak authentication, vulnerabilities, and malware. Attackers may exploit vulnerabilities to gain unauthorized access, masquerading as legitimate users. Such attacks can lead to privacy breaches, business disruption, financial losses, and reputational damage. Complex attack vectors blur lines between insider and external threats. To address this, we introduce the IDU-Detector, integrating Intrusion Detection Systems (IDS) with User and Entity Behavior Analytics (UEBA). This integration monitors unauthorized access, bridges system gaps, ensures continuous monitoring, and enhances threat identification. Existing insider threat datasets lack depth and coverage of diverse attack vectors. This hinders detection technologies from addressing complex attack surfaces. We propose new, diverse datasets covering more attack scenarios, enhancing detection technologies. Testing our framework, the IDU-Detector achieved average accuracies of 98.96% and 99.12%. These results show effectiveness in detecting attacks, improving security and response speed, and providing higher asset safety assurance. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_06172 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | IDU-Detector: A Synergistic Framework for Robust Masquerader Attack Detection Huang, Zilin Li, Xiulai Cao, Xinyi Chen, Ke Wang, Longjuan Liu, Logan Bo-Yee Cryptography and Security In the digital age, users store personal data in corporate databases, making data security central to enterprise management. Given the extensive attack surface, assets face challenges like weak authentication, vulnerabilities, and malware. Attackers may exploit vulnerabilities to gain unauthorized access, masquerading as legitimate users. Such attacks can lead to privacy breaches, business disruption, financial losses, and reputational damage. Complex attack vectors blur lines between insider and external threats. To address this, we introduce the IDU-Detector, integrating Intrusion Detection Systems (IDS) with User and Entity Behavior Analytics (UEBA). This integration monitors unauthorized access, bridges system gaps, ensures continuous monitoring, and enhances threat identification. Existing insider threat datasets lack depth and coverage of diverse attack vectors. This hinders detection technologies from addressing complex attack surfaces. We propose new, diverse datasets covering more attack scenarios, enhancing detection technologies. Testing our framework, the IDU-Detector achieved average accuracies of 98.96% and 99.12%. These results show effectiveness in detecting attacks, improving security and response speed, and providing higher asset safety assurance. |
| title | IDU-Detector: A Synergistic Framework for Robust Masquerader Attack Detection |
| topic | Cryptography and Security |
| url | https://arxiv.org/abs/2411.06172 |