TIIS (한국인터넷정보학회)
Current Result Document :
| 한글제목(Korean Title) |
Automated Analysis Approach for the Detetion of High Survivable Ransomware |
| 영문제목(English Title) |
Automated Analysis Approach for the Detetion of High Survivable Ransomware |
| 저자(Author) |
Yahye Abukar Ahmed
Bar詰 Ko?r
Bander Ali Saleh Al-rimy
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| 원문수록처(Citation) |
VOL 14 NO. 05 PP. 2236 ~ 2257 (2020. 05) |
한글내용
(Korean Abstract) |
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영문내용
(English Abstract) |
Ransomware is malicious software that encrypts the user-related files and data and holds them to ransom. Such attacks have become one of the serious threats to cyberspace. The avoidance techniques that ransomware employs such as obfuscation and/or packing makes it difficult to analyze such programs statically. Although many ransomware detection studies have been conducted, they are limited to a small portion of the attack's characteristics. To this end, this paper proposed a framework for the behavioral-based dynamic analysis of high survivable ransomware (HSR) with integrated valuable feature sets. Term Frequency-Inverse document frequency (TF-IDF) was employed to select the most useful features from the analyzed samples. Support Vector Machine (SVM) and Artificial Neural Network (ANN) were utilized to develop and implement a machine learning-based detection model able to recognize certain behavioral traits of high survivable ransomware attacks. Experimental evaluation indicates that the proposed framework achieved an area under the ROC curve of 0.987 and a few false positive rates 0.007. The experimental results indicate that the proposed framework can detect high survivable ransomware in the early stage accurately
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| 키워드(Keyword) |
Ransomware
supervised machine learning
Support Vector Machine
Artificial Neural Network
Term Frequency-Inverse document frequency
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| 파일첨부 |
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