Abstract
Mass-market mobile security threats have increased recently due to the growth of mobile technologies and the popularity of mobile devices. Accordingly, techniques have been introduced for identifying, classifying, and defending against mobile threats utilizing static, dynamic, on-device, and off-device techniques. Static techniques are easy to evade, while dynamic techniques are expensive. On-device techniques are evasion, while off-device techniques need being always online. To address some of those shortcomings, we introduce Andro-profiler, a hybrid behavior based analysis and classification system for mobile malware. Andro-profiler main goals are efficiency, scalability, and accuracy. For that, Andro-profiler classifies malware by exploiting the behavior profiling extracted from the integrated system logs including system calls. Andro-profiler executes a malicious application on an emulator in order to generate the integrated system logs, and creates human-readable behavior profiles by analyzing the integrated system logs. By comparing the behavior profile of malicious application with representative behavior profile for each malware family using a weighted similarity matching technique, Andro-profiler detects and classifies it into malware families. The experiment results demonstrate that Andro-profiler is scalable, performs well in detecting and classifying malware with accuracy greater than 98 %, outperforms the existing state-of-the-art work, and is capable of identifying 0-day mobile malware samples.
| Original language | English |
|---|---|
| Article number | 273 |
| Journal | SpringerPlus |
| Volume | 5 |
| Issue number | 1 |
| DOIs | |
| Publication status | Published - 2016 Dec 1 |
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Keywords
- Android
- Behavior profiling
- Malware
- Similarity
- System call
ASJC Scopus subject areas
- General
Cite this
Detecting and classifying method based on similarity matching of Android malware behavior with profile. / Jang, Jae wook; Yun, Jaesung; Mohaisen, Aziz; Woo, Jiyoung; Kim, Huy Kang.
In: SpringerPlus, Vol. 5, No. 1, 273, 01.12.2016.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Detecting and classifying method based on similarity matching of Android malware behavior with profile
AU - Jang, Jae wook
AU - Yun, Jaesung
AU - Mohaisen, Aziz
AU - Woo, Jiyoung
AU - Kim, Huy Kang
PY - 2016/12/1
Y1 - 2016/12/1
N2 - Mass-market mobile security threats have increased recently due to the growth of mobile technologies and the popularity of mobile devices. Accordingly, techniques have been introduced for identifying, classifying, and defending against mobile threats utilizing static, dynamic, on-device, and off-device techniques. Static techniques are easy to evade, while dynamic techniques are expensive. On-device techniques are evasion, while off-device techniques need being always online. To address some of those shortcomings, we introduce Andro-profiler, a hybrid behavior based analysis and classification system for mobile malware. Andro-profiler main goals are efficiency, scalability, and accuracy. For that, Andro-profiler classifies malware by exploiting the behavior profiling extracted from the integrated system logs including system calls. Andro-profiler executes a malicious application on an emulator in order to generate the integrated system logs, and creates human-readable behavior profiles by analyzing the integrated system logs. By comparing the behavior profile of malicious application with representative behavior profile for each malware family using a weighted similarity matching technique, Andro-profiler detects and classifies it into malware families. The experiment results demonstrate that Andro-profiler is scalable, performs well in detecting and classifying malware with accuracy greater than 98 %, outperforms the existing state-of-the-art work, and is capable of identifying 0-day mobile malware samples.
AB - Mass-market mobile security threats have increased recently due to the growth of mobile technologies and the popularity of mobile devices. Accordingly, techniques have been introduced for identifying, classifying, and defending against mobile threats utilizing static, dynamic, on-device, and off-device techniques. Static techniques are easy to evade, while dynamic techniques are expensive. On-device techniques are evasion, while off-device techniques need being always online. To address some of those shortcomings, we introduce Andro-profiler, a hybrid behavior based analysis and classification system for mobile malware. Andro-profiler main goals are efficiency, scalability, and accuracy. For that, Andro-profiler classifies malware by exploiting the behavior profiling extracted from the integrated system logs including system calls. Andro-profiler executes a malicious application on an emulator in order to generate the integrated system logs, and creates human-readable behavior profiles by analyzing the integrated system logs. By comparing the behavior profile of malicious application with representative behavior profile for each malware family using a weighted similarity matching technique, Andro-profiler detects and classifies it into malware families. The experiment results demonstrate that Andro-profiler is scalable, performs well in detecting and classifying malware with accuracy greater than 98 %, outperforms the existing state-of-the-art work, and is capable of identifying 0-day mobile malware samples.
KW - Android
KW - Behavior profiling
KW - Malware
KW - Similarity
KW - System call
UR - http://www.scopus.com/inward/record.url?scp=84963502457&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84963502457&partnerID=8YFLogxK
U2 - 10.1186/s40064-016-1861-x
DO - 10.1186/s40064-016-1861-x
M3 - Article
AN - SCOPUS:84963502457
VL - 5
JO - SpringerPlus
JF - SpringerPlus
SN - 2193-1801
IS - 1
M1 - 273
ER -