DocumentCode :
226902
Title :
An effective framework of behavior detection-advanced static analysis for malware detection
Author :
Louk, Maya ; Hyotaek Lim ; HoonJae Lee ; Atiquzzaman, M.
Author_Institution :
Dept. of Ubiquitous IT, Dongseo Univ., Busan, South Korea
fYear :
2014
fDate :
24-26 Sept. 2014
Firstpage :
361
Lastpage :
365
Abstract :
The recent development of malwares is rapidly progressing and intruders are getting better at concealing malwares to deceive users while researchers are developing security systems. One of the methods which is commonly used is behavior detection. In this paper, a more efficient behavior detection method and the framework of intrusion malware security system is presented. In addition, the implementation of the prototype and the result of the discussion is presented under advanced static analysis which is added PE Header study. Our proposed framework will (1) contribute to improve the security system for malware detection, especially to detect sophisticated malware, (2) show the effectiveness of behavior detection to memory performance, and (3) how advanced static analysis matches the algorithm for malware detection.
Keywords :
invasive software; statistical analysis; advanced static analysis; behavior detection; intrusion malware security system; malware detection; Computers; Educational institutions; Software; Target recognition; Trojan horses; Advanced Static Analysis; Efficient Behavior Detection; Framework; Malware; Sophisticated malware;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Communications and Information Technologies (ISCIT), 2014 14th International Symposium on
Conference_Location :
Incheon
Type :
conf
DOI :
10.1109/ISCIT.2014.7011932
Filename :
7011932
Link To Document :
بازگشت