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Android Malware Family Classification and Analysis: Current Status and Future Directions

Fahad Alswaina, Khaled Elleithy
2020 Electronics  
As we investigate, most of the literature reviews focus on surveying malware detection. Characterizing the malware families can improve the detection process and understand the malware patterns.  ...  Finally, we highlight the limitations that we identify in the literature, challenges, and future research directions regarding the Android malware family.  ...  They visualize the characteristics of families using graph mining and PCA. In [31] , the authors extract DFG and CFG. Then, they encode the graphs into a matrix.  ... 
doi:10.3390/electronics9060942 fatcat:ge3jufdgijc6hf3aiwd6cmrhy4

Predicting the Impact of Android Malicious Samples via Machine Learning

Junyang Qiu, Wei Luo, Lei Pan, Yonghang Tai, Jun Zhang, Yang Xiang
2019 IEEE Access  
Most of the existing works concerned only the identification of Android malware or classification of malware into the specific families.  ...  features of a graph structure (for example, API call graphs (ACG) [23] , data flow graphs (DFG) or control flow graphs (CFG)).  ...  His translational research has made significant impact on the real-world applications, such as AI-driven cyber security applications, malware applications, cloud and the IoT security applications, and  ... 
doi:10.1109/access.2019.2914311 fatcat:hnmxjngmnfdyllzeqa2tctf4qm

Brief View and Analysis to Latest Android Security Issues and Approaches [article]

Ruicong Huang
2021 arXiv   pre-print
To keep up with the latest situation, in this paper, we conduct a wide range of analysis, including latest malwares, Android security features, and approaches.  ...  We also provide some finding when we are gathering information and carrying on experiments, which we think is useful for further researches and has not been mentioned in previous works.  ...  New Threats Android malwares has been evolving these years, and malwares of new families (as listed in Table 1 ) keep occurring after 2015.  ... 
arXiv:2109.00805v1 fatcat:vl5cw5kw4fbqvbhsgubfl7vidi

Deep Learning for Android Malware Defenses: a Systematic Literature Review [article]

Yue Liu, Chakkrit Tantithamthavorn, Li Li, Yepang Liu
2022 arXiv   pre-print
However, given the explosive growth of Android malware and the continuous advancement of malicious evasion technologies like obfuscation and reflection, Android malware defense approaches based on manual  ...  This review also discusses research trends, research focuses, challenges, and future research directions in DL-based Android malware defenses.  ...  There are also 13 primary studies (10 percent) using program graphs like Control Flow Graph (CFG) and Data Flow Graph (DFG) to represent an application when analyzing Android malware.  ... 
arXiv:2103.05292v2 fatcat:qruddq4gknfq7jx5wyrk5qu2eu

Android-COCO: Android Malware Detection with Graph Neural Network for Byte- and Native-Code [article]

Peng Xu
2022 arXiv   pre-print
of Android malware.  ...  However, Recent research and our own statistics show that native payloads are commonly used in both benign and malicious apps.  ...  CDGDroid [11] uses the semantics graph representations, that is, control flow graph(CFG), data flow graph(DFG), and their possible combinations, as the features to characterise Android applications.  ... 
arXiv:2112.10038v2 fatcat:5wbiq52wp5hsfo2jlcaxawcpjq

Evolution, Detection and Analysis of Malware for Smart Devices

Guillermo Suarez-Tangil, Juan E. Tapiador, Pedro Peris-Lopez, Arturo Ribagorda
2014 IEEE Communications Surveys and Tutorials  
currently in use by such devices.  ...  This article examines the problem of malware in smart devices and recent progress made in detection techniques.  ...  ACKNOWLEDGEMENTS We thank the anonymous reviewers for valuable suggestions that helped to improve the quality and organization of this paper.  ... 
doi:10.1109/surv.2013.101613.00077 fatcat:u7qjrw4grvcorjjmy3ykddjeda

Constructing Features for Detecting Android Malicious Applications: Issues, Taxonomy and Directions

Wei Wang, Meichen Zhao, Zhenzhen Gao, Guangquan Xu, Hequn Xian, Yuanyuan Li, Xiangliang Zhang
2019 IEEE Access  
INDEX TERMS Android system, IoT, security and privacy, machine learning, malware analysis, malapp detection, survey. 67602 2169-3536  ...  the detection methods used, and the scale of evaluation performed.  ...  Amandroid [23] used inter-component DFG and interprocedural CFG to conduct the flow-sensitive and contextsensitive data flow analysis.  ... 
doi:10.1109/access.2019.2918139 fatcat:iifcbw3v4nbb3efq4eutkttcn4

A Review on Android Malware: Attacks, Countermeasures and Challenges Ahead

ShymalaGowri Selvaganapathy, Sudha Sadasivam, Vinayakumar Ravi
2021 Journal of Cyber Security and Mobility  
This survey converges on Android malware and covers a walkthrough of the various obfuscation attacks deployed during malware analysis phase along with the myriad of adversarial attacks operated at malware  ...  Malware authors have become increasingly sophisticated and are able to evade detection by anti-malware engines. This has led to a constant arms race between malware authors and malware defenders.  ...  DroidSieve performs malware detection and family classification. Dataset utilized involves combination of obfuscated and nonobfuscated malware samples.  ... 
doi:10.13052/jcsm2245-1439.1017 fatcat:mtxfys7pwvb7dastdlyu2s2tzq

apk2vec: Semi-supervised multi-view representation learning for profiling Android applications [article]

Annamalai Narayanan, Charlie Soh, Lihui Chen, Yang Liu, Lipo Wang
2018 arXiv   pre-print
of labels associated with apps (e.g., malware family or app category labels) to build high quality app profiles, and (3) it combines RL and feature hashing which allows it to efficiently build profiles  ...  , familial clustering, app clone detection and app recommendation.  ...  For instance, given a malware family label f, subgraphs that characterize f's behaviors would end up having similar embeddings.  ... 
arXiv:1809.05693v1 fatcat:jt5apyxpi5aotiymc37qubrhiq

Software engineering techniques for statically analyzing mobile apps: research trends, characteristics, and potential for industrial adoption

Marco Autili, Ivano Malavolta, Alexander Perucci, Gian Luca Scoccia, Roberto Verdecchia
2021 Journal of Internet Services and Applications  
We analyzed each primary study according to a rigorously-defined classification framework.  ...  can use the results of this study to (i) identify existing research/technical gaps to target, (ii) understand how approaches developed in academia can be successfully transferred to industry, and (iii  ...  methods and techniques of mobile apps Authors' contributions The authors equally contributed to the elaboration of this survey.  ... 
doi:10.1186/s13174-021-00134-x fatcat:mlzjbkdi7fhezisn3tcv7wzlbi

V-Sandbox For Dynamic Analysis IoT Botnet

Hai-Viet Le, Quoc-Dung Ngo
2020 IEEE Access  
With the increasing use of resource-constrained IoT devices, the number of IoT Botnets has exploded with many variations and ways of penetration.  ...  Nowadays, studies based on machine learning and deep learning have focused on dealing with IoT Botnet with many successes, and these studies have required relevant data during malware execution.  ...  As in [19] , [25] , authors have presented the static analysis method, which allows full control of the control flow (CFG) and data flow (DFG) to detect malicious code by specific analytical techniques  ... 
doi:10.1109/access.2020.3014891 fatcat:jyf7utxqxzdjnpdbtauxxqfv5i

A Survey on Recent Advanced Research of CPS Security

Zhenhua Wang, Wei Xie, Baosheng Wang, Jing Tao, Enze Wang
2021 Applied Sciences  
, smart transportation, smart homes, and general grids); and (3) MADC (Measure, Attack, Defense, and Control) types.  ...  Cyber-physical systems (CPSs) are next-generation intelligent systems that integrate computing, communication, and control. Malicious attacks on CPSs can lead to both property damage and casualties.  ...  By characterizing the traffic generated by unsolicited IoT devices, they discovered new malware families whose target is vulnerable devices.  ... 
doi:10.3390/app11093751 fatcat:fxby2wjzpnchrfshvilxalmptm

Embedded System Security: A Software-based Approach

Ang Cui
We propose two host-based software defense techniques, Symbiote and Autotomic Binary Structure Randomization, that can be practically deployed to a wide spectrum of embedded devices in use today.  ...  and other commercial devices like network-based printers and IP phones.  ...  Given the F et EM for a specific executable, a set of features to be removed {f }, a controlflow graph (CFG) g c and a data-flow graph (DFG) g d , the Autotomy algorithm does the following: 1.  ... 
doi:10.7916/d8ns0tn9 fatcat:bccoer7jgnel3gpba4znbv2igm

Dagstuhl Reports, Volume 6, Issue 10, September 2016, Complete Issue [article]

Online and Adaptive Methods Markus Wagner University of Adelaide, AU We identify the notion of an MCSat-friendly inference system, and define a generic MCSat calculus that is sound and complete for satisfiability  ...  Acknowledgements We would like to take the opportunity to thank and acknowledge our organization team member Ingrid Verbauwhede for her great effort in contributing brilliant ideas and suggestions on the  ...  an additional mechanism for malware detection.  ... 
doi:10.4230/dagrep.6.10 fatcat:wq33g6exi5bzll67no5ztodtoy