A study on the implementation of a system providing reliable malware information service

被引:2
作者
Sung K.-S. [1 ]
Na W. [2 ]
机构
[1] Department of Security Business, KB-SYS.Co.Ltd Seoul
[2] Department of Beauty and Health, Namseoul University Cheonan
来源
Na, Wonshik (winner@nsu.ac.kr) | 1600年 / SAGE Publications Inc.卷 / 58期
关键词
cyber-attacks technique; extraction method; malicious acts; Malware; reliable information;
D O I
10.1177/0020720919828982
中图分类号
学科分类号
摘要
Malware can be diagnosed through several ways or can even pass under the radar at times. This can cause confusion among the users, who do not welcome a flood of unorganized information in general. This paper proposes an extraction method that can provide reliable and established results in order to tackle the issue from the users’ point of view. To guarantee the reliability of Anti-virus Software (AVS) and AVS against the malware, this paper tested malware that can actually be found in use. Such processes allow this paper to extract and provide the most reliable information to users. In addition, this paper can help students, who study security, to establish the concept of malicious code. And research subjects are enough to prepare malicious code countermeasures for security graduate students. © The Author(s) 2019.
引用
收藏
页码:517 / 530
页数:13
相关论文
共 10 条
[1]  
Enck W., Et al., TaintDroid: an information flow tracking system for real-time privacy monitoring, on smartphones. Commun ACM, 57, pp. 99-106, (2014)
[2]  
Un Kyung J., A study on similarity comparison for file DNA-based metamorphic malware detection, J Korea Computer Inform Soc, 19, pp. 85-94, (2014)
[3]  
Jong Ha A., (2014)
[4]  
Ji Yeong J., (2015)
[5]  
Jun Seok H., Analysis and countermeasure of malicious code in small businesses, J Korea Assoc Convergence Security, 15, pp. 55-62, (2015)
[6]  
Sam Hong S., A detection model using labeling based on inference and unsupervised learning method, J Korea Internet Inform Soc, 18, pp. 65-75, (2017)
[7]  
Wu D.J., Mao C.H., Wei T.E., Et al., Droidmat: Android malware detection through manifest and API calls tracing, Information Security Asia JCIS, 8, pp. 62-69, (2012)
[8]  
Rampure V., Tiwari A., A rough set based feature selection on KDD CUP 99 data set, Int J Database Theory Appl, 8, pp. 149-156, (2015)
[9]  
Yeon Jin J., (2015)
[10]  
(2013)