TY - GEN
T1 - Should You Consider Adware as Malware in Your Study?
AU - Gao, Jun
AU - Li, Li
AU - Kong, Pingfan
AU - Bissyande, Tegawende F.
AU - Klein, Jacques
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/3/15
Y1 - 2019/3/15
N2 - Empirical validations of research approaches eventually require a curated ground truth. In studies related to Android malware, such a ground truth is built by leveraging Anti-Virus (AV) scanning reports which are often provided free through online services such as VirusTotal. Unfortunately, these reports do not offer precise information for appropriately and uniquely assigning classes to samples in app datasets: AV engines indeed do not have a consensus on specifying information in labels. Furthermore, labels often mix information related to families, types, etc. In particular, the notion of 'adware' is currently blurry when it comes to maliciousness. There is thus a need to thoroughly investigate cases where adware samples can actually be associated with malware (e.g., because they are tagged as adware but could be considered as malware as well).In this work, we present a large-scale analytical study of Android adware samples to quantify to what extent 'adware should be considered as malware'. Our analysis is based on the Androzoo repository of 5 million apps with associated AV labels and leverages a state-of-The-Art label harmonization tool to infer the malicious type of apps before confronting it against the ad families that each adware app is associated with. We found that all adware families include samples that are actually known to implement specific malicious behavior types. Up to 50% of samples in an ad family could be flagged as malicious. Overall the study demonstrates that adware is not necessarily benign.
AB - Empirical validations of research approaches eventually require a curated ground truth. In studies related to Android malware, such a ground truth is built by leveraging Anti-Virus (AV) scanning reports which are often provided free through online services such as VirusTotal. Unfortunately, these reports do not offer precise information for appropriately and uniquely assigning classes to samples in app datasets: AV engines indeed do not have a consensus on specifying information in labels. Furthermore, labels often mix information related to families, types, etc. In particular, the notion of 'adware' is currently blurry when it comes to maliciousness. There is thus a need to thoroughly investigate cases where adware samples can actually be associated with malware (e.g., because they are tagged as adware but could be considered as malware as well).In this work, we present a large-scale analytical study of Android adware samples to quantify to what extent 'adware should be considered as malware'. Our analysis is based on the Androzoo repository of 5 million apps with associated AV labels and leverages a state-of-The-Art label harmonization tool to infer the malicious type of apps before confronting it against the ad families that each adware app is associated with. We found that all adware families include samples that are actually known to implement specific malicious behavior types. Up to 50% of samples in an ad family could be flagged as malicious. Overall the study demonstrates that adware is not necessarily benign.
KW - adware
KW - Android
KW - malware
UR - https://www.scopus.com/pages/publications/85064166595
U2 - 10.1109/SANER.2019.8668010
DO - 10.1109/SANER.2019.8668010
M3 - 会议稿件
AN - SCOPUS:85064166595
T3 - SANER 2019 - Proceedings of the 2019 IEEE 26th International Conference on Software Analysis, Evolution, and Reengineering
SP - 604
EP - 608
BT - SANER 2019 - Proceedings of the 2019 IEEE 26th International Conference on Software Analysis, Evolution, and Reengineering
A2 - Shihab, Emad
A2 - Lo, David
A2 - Wang, Xinyu
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 26th IEEE International Conference on Software Analysis, Evolution, and Reengineering, SANER 2019
Y2 - 24 February 2019 through 27 February 2019
ER -