TIIS (한국인터넷정보학회)
Current Result Document :
| 한글제목(Korean Title) |
R2NET: Storage and Analysis of Attack Behavior Patterns |
| 영문제목(English Title) |
R2NET: Storage and Analysis of Attack Behavior Patterns |
| 저자(Author) |
M. R. Amal
P. Venkadesh
|
| 원문수록처(Citation) |
VOL 17 NO. 02 PP. 0295 ~ 0311 (2023. 02) |
한글내용
(Korean Abstract) |
|
영문내용
(English Abstract) |
Cloud computing has evolved significantly, intending to provide users with fast, dependable, and low-cost services. With its development, malicious users have become increasingly capable of attacking both its internal and external security. To ensure the security of cloud services, encryption, authorization, firewalls, and intrusion detection systems have been employed. However, these single monitoring agents, are complex, time-consuming, and they do not detect ransomware and zero-day vulnerabilities on their own. An innovative Record and Replay-based hybrid Honeynet (R2NET) system has been developed to address this issue. Combining honeynet with Record and Replay (RR) technology, the system allows fine-grained analysis by delaying time-consuming analysis to the replay step. In addition, a machine learning algorithm is utilized to cluster the logs of attackers and store them in a database. So, the accessing time for analyzing the attack may be reduced which in turn increases the efficiency of the proposed framework. The R2NET framework is compared with existing methods such as EEHH net, HoneyDoc, Honeynet system, and AHDS. The proposed system achieves 7.60%, 9.78%%, 18.47%, and 31.52% more accuracy than EEHH net, HoneyDoc, Honeynet system, and AHDS methods. |
| 키워드(Keyword) |
Cloud computing
Honeynet
Machine learning
R2NET
Zero-day attacks
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| 파일첨부 |
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