TY - JOUR
T1 - Android malware detection scheme based on level of SSL server certificate
AU - Kato, Hiroya
AU - Haruta, Shuichiro
AU - Sasase, Iwao
N1 - Funding Information:
This work is partly supported by the Grant in Aid for Scientific Research (No.17K06440) from Japan Society for Promotion of Science (JSPS).
Publisher Copyright:
Copyright © 2020 The Institute of Electronics, Information and Communication Engineers.
PY - 2020
Y1 - 2020
N2 - Detecting Android malwares is imperative. As a promising Android malware detection scheme, we focus on the scheme leveraging the differences of traffic patterns between benign apps and malwares. Those differences can be captured even if the packet is encrypted. However, since such features are just statistic based ones, they cannot identify whether each traffic is malicious. Thus, it is necessary to design the scheme which is applicable to encrypted traffic data and supports identification of malicious traffic. In this paper, we propose an Android malware detection scheme based on level of SSL server certificate. Attackers tend to use an untrusted certificate to encrypt malicious payloads in many cases because passing rigorous examination is required to get a trusted certificate. Thus, we utilize SSL server certificate based features for detection since their certificates tend to be untrusted. Furthermore, in order to obtain the more exact features, we introduce required permission based weight values because malwares inevitably require permissions regarding malicious actions. By computer simulation with real dataset, we show our scheme achieves an accuracy of 92.7%. True positive rate and false positive rate are 5.6% higher and 3.2% lower than the previous scheme, respectively. Our scheme can cope with encrypted malicious payloads and 89 malwares which are not detected by the previous scheme.
AB - Detecting Android malwares is imperative. As a promising Android malware detection scheme, we focus on the scheme leveraging the differences of traffic patterns between benign apps and malwares. Those differences can be captured even if the packet is encrypted. However, since such features are just statistic based ones, they cannot identify whether each traffic is malicious. Thus, it is necessary to design the scheme which is applicable to encrypted traffic data and supports identification of malicious traffic. In this paper, we propose an Android malware detection scheme based on level of SSL server certificate. Attackers tend to use an untrusted certificate to encrypt malicious payloads in many cases because passing rigorous examination is required to get a trusted certificate. Thus, we utilize SSL server certificate based features for detection since their certificates tend to be untrusted. Furthermore, in order to obtain the more exact features, we introduce required permission based weight values because malwares inevitably require permissions regarding malicious actions. By computer simulation with real dataset, we show our scheme achieves an accuracy of 92.7%. True positive rate and false positive rate are 5.6% higher and 3.2% lower than the previous scheme, respectively. Our scheme can cope with encrypted malicious payloads and 89 malwares which are not detected by the previous scheme.
KW - Android malwares
KW - Machine learning
KW - SSL certificate
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U2 - 10.1587/transinf.2019EDP7119
DO - 10.1587/transinf.2019EDP7119
M3 - Article
AN - SCOPUS:85081789890
SN - 0916-8532
VL - E103D
SP - 379
EP - 389
JO - IEICE Transactions on Information and Systems
JF - IEICE Transactions on Information and Systems
IS - 2
ER -