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Current Result Document :
ÇѱÛÁ¦¸ñ(Korean Title) |
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¿µ¹®Á¦¸ñ(English Title) |
Real-Time Ransomware Infection Detection System Based on Social Big Data Mining |
ÀúÀÚ(Author) |
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Ji-Sim Kim
MiSoon Park
Kyong Ah Kim
Nammee Moon
Jung-Won Lee
Yoo-Joo Choi
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À±ÁØÇõ
Mihui Kim
Junhyeok Yun
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¿ø¹®¼ö·Ïó(Citation) |
VOL 07 NO. 10 PP. 0251 ~ 0258 (2018. 10) |
Çѱ۳»¿ë (Korean Abstract) |
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¿µ¹®³»¿ë (English Abstract) |
Ransomware, a malicious software that requires a ransom by encrypting a file, is becoming more threatening with its rapid propagation and intelligence. Rapid detection and risk analysis are required, but real-time analysis and reporting are lacking. In this paper, we propose a ransomware infection detection system using social big data mining technology to enable real-time analysis. The system analyzes the twitter stream in real time and crawls tweets with keywords related to ransomware. It also extracts keywords related to ransomware by crawling the news server through the news feed parser and extracts news or statistical data on the servers of the security company or search engine. The collected data is analyzed by data mining algorithms. By comparing the number of related tweets, google trends (statistical information), and articles related wannacry and locky ransomware infection spreading in 2017, we show that our system has the possibility of ransomware infection detection using tweets. Moreover, the performance of proposed system is shown through entropy and chi-square analysis. |
Å°¿öµå(Keyword) |
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Female Students in Engineering College
Education on Computer Engineering
Guideline for Career Guidance
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Ransomware
Infection Detection System
Social Big Data Mining
Entropy
Chi-Square
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