IEICE TRANSACTIONS on Fundamentals
Catching the Behavioral Differences between Multiple Executions for Malware Detection
Summary :
As the number of new malware has increased explosively, traditional malware detection approaches based on pattern matching have been less effective. Therefore, it is important to develop a detection method which relies on not signatures but characteristic behaviors of malware. Recently, malware authors have been embedding functions for countermeasure against malware analyses and detections into malware. Accordingly, modern malware often changes their runtime behaviors in each execution to tolerate against malware analyses and detections. For example, when malware copies itself on a file system, it can randomly determine its file name for avoiding the detections. Another example is that when malware tries to connect its command and control server, it randomly chooses a domain name from a hard-coded domain name list to avoid being blocked by a static blacklist of malicious domain names. We assume that such evasive behaviors are unnecessary for benign software. Therefore the behaviors can be the clues to distinguish malware from benign software. In this paper, we propose a novel behavior-based malware detection method which focuses attention on such characteristics. Our proposed method conducts dynamic analysis on an executable file multiple times in same sandbox environment so as to obtain plural lists of API call sequences and plural traffic logs, and then compares the lists and the logs to find the difference between the multiple executions. In the experiments with 5,697 malware samples and 819 benign software samples, we can detect about 70% malware samples and the false positive rate is about 1%. In addition, we can detect about 50% malware samples which were not detected by each Anti-Virus Software engine. Therefore we confirm the possibility the proposed method may be able to improve the accuracy of malware detection utilizing in combination with other existing methods.
- Publication
- IEICE TRANSACTIONS on Fundamentals Vol.E96-A No.1 pp.225-232
- Publication Date
- 2013/01/01
- Publicized
- Online ISSN
- 1745-1337
- DOI
- 10.1587/transfun.E96.A.225
- Type of Manuscript
- Special Section PAPER (Special Section on Cryptography and Information Security)
- Category
- System Security
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Takahiro KASAMA, Katsunari YOSHIOKA, Daisuke INOUE, Tsutomu MATSUMOTO, "Catching the Behavioral Differences between Multiple Executions for Malware Detection" in IEICE TRANSACTIONS on Fundamentals,
vol. E96-A, no. 1, pp. 225-232, January 2013, doi: 10.1587/transfun.E96.A.225.
Abstract: As the number of new malware has increased explosively, traditional malware detection approaches based on pattern matching have been less effective. Therefore, it is important to develop a detection method which relies on not signatures but characteristic behaviors of malware. Recently, malware authors have been embedding functions for countermeasure against malware analyses and detections into malware. Accordingly, modern malware often changes their runtime behaviors in each execution to tolerate against malware analyses and detections. For example, when malware copies itself on a file system, it can randomly determine its file name for avoiding the detections. Another example is that when malware tries to connect its command and control server, it randomly chooses a domain name from a hard-coded domain name list to avoid being blocked by a static blacklist of malicious domain names. We assume that such evasive behaviors are unnecessary for benign software. Therefore the behaviors can be the clues to distinguish malware from benign software. In this paper, we propose a novel behavior-based malware detection method which focuses attention on such characteristics. Our proposed method conducts dynamic analysis on an executable file multiple times in same sandbox environment so as to obtain plural lists of API call sequences and plural traffic logs, and then compares the lists and the logs to find the difference between the multiple executions. In the experiments with 5,697 malware samples and 819 benign software samples, we can detect about 70% malware samples and the false positive rate is about 1%. In addition, we can detect about 50% malware samples which were not detected by each Anti-Virus Software engine. Therefore we confirm the possibility the proposed method may be able to improve the accuracy of malware detection utilizing in combination with other existing methods.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/transfun.E96.A.225/_p
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@ARTICLE{e96-a_1_225,
author={Takahiro KASAMA, Katsunari YOSHIOKA, Daisuke INOUE, Tsutomu MATSUMOTO, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Catching the Behavioral Differences between Multiple Executions for Malware Detection},
year={2013},
volume={E96-A},
number={1},
pages={225-232},
abstract={As the number of new malware has increased explosively, traditional malware detection approaches based on pattern matching have been less effective. Therefore, it is important to develop a detection method which relies on not signatures but characteristic behaviors of malware. Recently, malware authors have been embedding functions for countermeasure against malware analyses and detections into malware. Accordingly, modern malware often changes their runtime behaviors in each execution to tolerate against malware analyses and detections. For example, when malware copies itself on a file system, it can randomly determine its file name for avoiding the detections. Another example is that when malware tries to connect its command and control server, it randomly chooses a domain name from a hard-coded domain name list to avoid being blocked by a static blacklist of malicious domain names. We assume that such evasive behaviors are unnecessary for benign software. Therefore the behaviors can be the clues to distinguish malware from benign software. In this paper, we propose a novel behavior-based malware detection method which focuses attention on such characteristics. Our proposed method conducts dynamic analysis on an executable file multiple times in same sandbox environment so as to obtain plural lists of API call sequences and plural traffic logs, and then compares the lists and the logs to find the difference between the multiple executions. In the experiments with 5,697 malware samples and 819 benign software samples, we can detect about 70% malware samples and the false positive rate is about 1%. In addition, we can detect about 50% malware samples which were not detected by each Anti-Virus Software engine. Therefore we confirm the possibility the proposed method may be able to improve the accuracy of malware detection utilizing in combination with other existing methods.},
keywords={},
doi={10.1587/transfun.E96.A.225},
ISSN={1745-1337},
month={January},}
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TY - JOUR
TI - Catching the Behavioral Differences between Multiple Executions for Malware Detection
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 225
EP - 232
AU - Takahiro KASAMA
AU - Katsunari YOSHIOKA
AU - Daisuke INOUE
AU - Tsutomu MATSUMOTO
PY - 2013
DO - 10.1587/transfun.E96.A.225
JO - IEICE TRANSACTIONS on Fundamentals
SN - 1745-1337
VL - E96-A
IS - 1
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - January 2013
AB - As the number of new malware has increased explosively, traditional malware detection approaches based on pattern matching have been less effective. Therefore, it is important to develop a detection method which relies on not signatures but characteristic behaviors of malware. Recently, malware authors have been embedding functions for countermeasure against malware analyses and detections into malware. Accordingly, modern malware often changes their runtime behaviors in each execution to tolerate against malware analyses and detections. For example, when malware copies itself on a file system, it can randomly determine its file name for avoiding the detections. Another example is that when malware tries to connect its command and control server, it randomly chooses a domain name from a hard-coded domain name list to avoid being blocked by a static blacklist of malicious domain names. We assume that such evasive behaviors are unnecessary for benign software. Therefore the behaviors can be the clues to distinguish malware from benign software. In this paper, we propose a novel behavior-based malware detection method which focuses attention on such characteristics. Our proposed method conducts dynamic analysis on an executable file multiple times in same sandbox environment so as to obtain plural lists of API call sequences and plural traffic logs, and then compares the lists and the logs to find the difference between the multiple executions. In the experiments with 5,697 malware samples and 819 benign software samples, we can detect about 70% malware samples and the false positive rate is about 1%. In addition, we can detect about 50% malware samples which were not detected by each Anti-Virus Software engine. Therefore we confirm the possibility the proposed method may be able to improve the accuracy of malware detection utilizing in combination with other existing methods.
ER -
English
