Malware detection using static analysis in Android: a review of FeCO (features, classification, and obfuscation)

被引:12
|
作者
Jusoh, Rosmalissa [1 ]
Firdaus, Ahmad [1 ]
Anwar, Shahid [2 ]
Osman, Mohd Zamri [1 ]
Darmawan, Mohd Faaizie [3 ]
Ab Razak, Mohd Faizal [1 ]
机构
[1] Univ Malaysia Pahang, Coll Comp & Appl Sci, Fac Comp, Pahang, Malaysia
[2] Natl Skills Univ, Dept Informat Engn Technol, Islamabad, Pakistan
[3] Univ Teknol Mara, Fac Comp & Math Sci, Tapah, Perak, Malaysia
关键词
Android; Review; Static analysis; Machine learning; Features; Malware; FEATURE-SELECTION; OPTIMIZATION; FRAMEWORK; SECURITY; NETWORK; SYSTEM;
D O I
10.7717/peerj-cs.522
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Android is a free open-source operating system (OS), which allows an in-depth understanding of its architecture. Therefore, many manufacturers are utilizing this OS to produce mobile devices (smartphones, smartwatch, and smart glasses) in different brands, including Google Pixel, Motorola, Samsung, and Sony. Notably, the employment of OS leads to a rapid increase in the number of Android users. However, unethical authors tend to develop malware in the devices for wealth, fame, or private purposes. Although practitioners conduct intrusion detection analyses, such as static analysis, there is an inadequate number of review articles discussing the research efforts on this type of analysis. Therefore, this study discusses the articles published from 2009 until 2019 and analyses the steps in the static analysis (reverse engineer, features, and classification) with taxonomy. Following that, the research issue in static analysis is also highlighted. Overall, this study serves as the guidance for novice security practitioners and expert researchers in the proposal of novel research to detect malware through static analysis.
引用
收藏
页码:1 / 54
页数:54
相关论文
共 50 条
  • [1] Sensitivity Analysis of Static Features for Android Malware Detection
    Moghaddam, Samaneh Hosseini
    Abbaspour, Maghsood
    [J]. 2014 22ND IRANIAN CONFERENCE ON ELECTRICAL ENGINEERING (ICEE), 2014, : 920 - 924
  • [2] Impact of Code Obfuscation on Android Malware Detection based on Static and Dynamic Analysis
    Bacci, Alessandro
    Bartoli, Alberto
    Martinelli, Fabio
    Medvet, Eric
    Mercaldo, Francesco
    Visaggio, Corrado Aaron
    [J]. ICISSP: PROCEEDINGS OF THE 4TH INTERNATIONAL CONFERENCE ON INFORMATION SYSTEMS SECURITY AND PRIVACY, 2018, : 379 - 385
  • [3] A Systematic Literature Review of Android Malware Detection Using Static Analysis
    Pan, Ya
    Ge, Xiuting
    Fang, Chunrong
    Fan, Yong
    [J]. IEEE ACCESS, 2020, 8 : 116363 - 116379
  • [4] Malware Detection in Android Apps Using Static Analysis
    Paul, Nishtha
    Bhatt, Arpita Jadhav
    Rizvi, Sakeena
    Shubhangi
    [J]. Journal of Cases on Information Technology, 2021, 24 (03)
  • [5] Android Malware Category and Family Classification Using Static Analysis
    Cong-Danh Nguyen
    Nghi Hoang Khoa
    Khoa Nguyen-Dang Doan
    Nguyen Tan Cam
    [J]. 2023 INTERNATIONAL CONFERENCE ON INFORMATION NETWORKING, ICOIN, 2023, : 162 - 167
  • [6] Android malware detection based on overlapping of static features
    Nezhadkamali, Maryam
    Soltani, Somayeh
    Seno, Seyed Amin Hosseini
    [J]. PROCEEDINGS OF THE 2017 7TH INTERNATIONAL CONFERENCE ON COMPUTER AND KNOWLEDGE ENGINEERING (ICCKE), 2017, : 319 - 325
  • [7] Static Analysis of Android Malware Detection using Deep Learning
    Sandeep, H. R.
    [J]. PROCEEDINGS OF THE 2019 INTERNATIONAL CONFERENCE ON INTELLIGENT COMPUTING AND CONTROL SYSTEMS (ICCS), 2019, : 841 - 845
  • [8] Malware Classification Using Static Analysis Based Features
    Hassen, Mehadi
    Carvalho, Marco M.
    Chan, Philip K.
    [J]. 2017 IEEE SYMPOSIUM SERIES ON COMPUTATIONAL INTELLIGENCE (SSCI), 2017, : 734 - 740
  • [9] ANASTASIA: ANdroid mAlware detection using STAtic analySIs of Applications
    Fereidooni, Hossein
    Conti, Mauro
    Yao, Danfeng
    Sperduti, Alessandro
    [J]. 2016 8TH IFIP INTERNATIONAL CONFERENCE ON NEW TECHNOLOGIES, MOBILITY AND SECURITY (NTMS), 2016,
  • [10] Obfuscation-Resilient Android Malware Analysis Based on Complementary Features
    Gao, Cuiying
    Cai, Minghui
    Yin, Shuijun
    Huang, Gaozhun
    Li, Heng
    Yuan, Wei
    Luo, Xiapu
    [J]. IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY, 2023, 18 : 5056 - 5068