TY - CHAP
T1 - AndroShow
T2 - A Large Scale Investigation to Identify the Pattern of Obfuscated Android Malware
AU - Russel, Md Omar Faruque Khan
AU - Rahman, Sheikh Shah Mohammad Motiur
AU - Alazab, Mamoun
PY - 2021
Y1 - 2021
N2 - This paper represents a static analysis based research of android’s feature in obfuscated android malware. Android smartphone’s security and privacy of personal information remain threatened because of android based device popularity. It has become a challenging and diverse area to research in information security. Though malware researchers can detect already identified malware, they can not detect many obfuscated malware. Because, malware attackers use different obfuscation techniques, as a result many anti malware engines can not detect obfuscated malware applications. Therefore, it is necessary to identify the obfuscated malware pattern made by attackers. A large-scale investigation has been performed in this paper by developing python scripts, named it AndroShow, to extract pattern of permission, app component, filtered intent, API call and system call from an obfuscated malware dataset named Android PRAGuard Dataset. Finally, the patterns in a matrix form have been found and stored in a Comma Separated Values (CSV) file which will be the base of detecting the obfuscated malware in future.
AB - This paper represents a static analysis based research of android’s feature in obfuscated android malware. Android smartphone’s security and privacy of personal information remain threatened because of android based device popularity. It has become a challenging and diverse area to research in information security. Though malware researchers can detect already identified malware, they can not detect many obfuscated malware. Because, malware attackers use different obfuscation techniques, as a result many anti malware engines can not detect obfuscated malware applications. Therefore, it is necessary to identify the obfuscated malware pattern made by attackers. A large-scale investigation has been performed in this paper by developing python scripts, named it AndroShow, to extract pattern of permission, app component, filtered intent, API call and system call from an obfuscated malware dataset named Android PRAGuard Dataset. Finally, the patterns in a matrix form have been found and stored in a Comma Separated Values (CSV) file which will be the base of detecting the obfuscated malware in future.
KW - Android malware
KW - Obfuscated malware
KW - Obfuscated malware pattern
KW - Obfuscated malware pattern identification
UR - http://www.scopus.com/inward/record.url?scp=85097866115&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-57024-8_8
DO - 10.1007/978-3-030-57024-8_8
M3 - Chapter
AN - SCOPUS:85097866115
SN - 978-3-030-57023-1
T3 - Studies in Computational Intelligence
SP - 191
EP - 216
BT - Machine Intelligence and Big Data Analytics for Cybersecurity Applications
A2 - Maleh, Yassine
A2 - Shojafar, Mohammad
A2 - Alazab, Mamoun
A2 - Baddi, Youssef
PB - Springer Nature Switzerland AG
CY - Cham, Switzerland
ER -