BRIDEMAID: An Hybrid Tool for Accurate Detection of Android Malware

被引：47

作者：

Martinelli, Fabio ^{[1
]}

Mercaldo, Francesco ^{[1
]}

Saracino, Andrea ^{[1
]}

机构：

[1] CNR, Ist Inforrnat & Telemat, Pisa, Italy

来源：

PROCEEDINGS OF THE 2017 ACM ASIA CONFERENCE ON COMPUTER AND COMMUNICATIONS SECURITY (ASIA CCS'17) | 2017年

基金：

欧盟地平线“2020”;

关键词：

D O I：

10.1145/3052973.3055156

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

This paper presents BRIDEMAID, a framework which exploits an approach static and dynamic for accurate detection of Android malware. The static analysis is based on n-grams matching, whilst the dynamic analysis is based on multi-level monitoring of device, app and user behavior. The framework has been tested against 2794 malicious apps reporting a detection accuracy of 99,7% and a negligible false positive rate, tested on a set of 10k genuine apps.

引用

页码：899 / 901

页数：3

共 50 条

[21] Deep Android Malware Detection
McLaughlin, Niall
del Rincon, Jesus Martinez
Kang, BooJoong
Yerima, Suleiman
Miller, Paul
Sezer, Sakir
Safaei, Yeganeh
Trickel, Erik
Zhao, Ziming
Doup, Adam
Ahn, Gail Joon
PROCEEDINGS OF THE SEVENTH ACM CONFERENCE ON DATA AND APPLICATION SECURITY AND PRIVACY (CODASPY'17), 2017, : 301 - 308
[22] Detection of Repackaged Android Malware
Shahriar, Hossain
Clincy, Victor
2014 9TH INTERNATIONAL CONFERENCE FOR INTERNET TECHNOLOGY AND SECURED TRANSACTIONS (ICITST), 2014, : 349 - 354
[23] Smart malware detection on Android
Gheorghe, Laura
Marin, Bogdan
Gibson, Gary
Mogosanu, Lucian
Deaconescu, Razvan
Voiculescu, Valentin-Gabriel
Carabas, Mihai
SECURITY AND COMMUNICATION NETWORKS, 2015, 8 (18) : 4254 - 4272
[24] TRENDS IN ANDROID MALWARE DETECTION
Shaerpour, Kaveh
Dehghantanha, Ali
Mahmod, Ramlan
JOURNAL OF DIGITAL FORENSICS SECURITY AND LAW, 2013, 8 (03) : 21 - 40
[25] Android malware detection model
Yang H.
Na Y.
Xi'an Dianzi Keji Daxue Xuebao/Journal of Xidian University, 2019, 46 (03): : 45 - 51
[26] Android Fragmentation in Malware Detection
Long Nguyen-Vu
Ahn, Jinung
Jung, Souhwan
COMPUTERS & SECURITY, 2019, 87
[27] Android Malware Detection Using Hybrid Analysis and Machine Learning Technique
Yang, Fan
Zhuang, Yi
Wang, Jun
CLOUD COMPUTING AND SECURITY, PT II, 2017, 10603 : 565 - 575
[28] Poster: Android Malware Detection using Hybrid Features and Machine Learning
Kadir, Abdul
Peddoju, Sateesh K.
2024 IEEE 21ST INTERNATIONAL CONFERENCE ON MOBILE AD-HOC AND SMART SYSTEMS, MASS 2024, 2024, : 494 - 495
[29] Hybrid Android Malware Detection: A Review of Heuristic-Based Approach
Yunmar, Rajif Agung
Kusumawardani, Sri Suning
Mohsen, Fadi
IEEE ACCESS, 2024, 12 : 41255 - 41286
[30] On the Evaluation of the Machine Learning Based Hybrid Approach for Android Malware Detection
Ratyal, Natasha Javed
Khadam, Maryam
Aleem, Muhammad
2019 22ND IEEE INTERNATIONAL MULTI TOPIC CONFERENCE (INMIC), 2019, : 100 - 107

← 1 2 3 4 5 →