Feb 27, 2019 Hence, we propose an automatic Android malware detection approach, named HyGNN-Mal. It analyzes the Android applications at source code level by exploiting the sequence and structure...
Jun 16, 2020 Based on the features of applications, we first divide static analysis in Android malware detection into four categories, which include Android characteristic-based method, opcode-based...
Jun 29, 2022 To overcome the research gaps, this paper provides a broad review of current Android security concerns, security implementation enhancements, significant malware detected during 20172021,...
Nov 2, 2017 Malware Research. Chapter PDF Available. Malware Analysis and Detection on Android: The Big Challenge. November 2017. DOI: 10.5772/intechopen.69695. License. CC BY 3.0. In book:...
Jul 20, 2021 This work examines the current status of Android malware detection methods, with an emphasis on Machine Learning-based classifiers for detecting malicious software on Android devices.
Mar 23, 2021 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...
The overall idea of the Topspeed Test2 malware application is go gain access to the android system and give the user private information to remote attackers. 9. CONCLUSION AND FUTURE SCOPE 9.1 ...
Apr 8, 2024 In this schema, each Android application in the MalDroid-2020 data set is executed on a real device. It allows us to analyze the activity of malware by monitoring the system calls made during its ...
Nov 17, 2018 Application of Machine Learning Algorithms for Android Malware Detection. November 2018. DOI: 10.1145/3293475.3293489. Conference: the 2018 International Conference. Authors: Mohsen Kakavand....
Jul 28, 2022 Malware Analysis in IoT & Android Systems with Defensive Mechanism. July 2022. Electronics 11 (15):2354. DOI: 10.3390/electronics11152354. License. CC BY 4.0. Authors: Chandra Shekhar Yadav....
Jul 5, 2022 This paper offers a comprehensive analysis model for android malware. The model presents the essential factors affecting the analysis results of android malware that are vision-based. Current android malware analysis and solutions might consider one or some of these factors while building their malware predictive systems.
Malware analysis approaches can be classified as static, dynamic, and hybrid [2]. Static analysis can detect malware using the APK files without executing the application. While dynamic analysis executes and monitors apps to detect mali-cious behaviour during run-time. Static analysis has been shown as scalable, has.
Dec 14, 2021 A Systematic Overview of Android Malware Detection. Li Meijin. , Fang Zhiyang. , Wang Junfeng. , Cheng Luyu. , Zeng Qi. , Yang Tao. , show all. Article: 2007327 | Received 05 Aug 2021, Accepted 08 Nov 2021, Published online: 14 Dec 2021. Cite this article. https://doi.org/10.1080/08839514.2021.2007327. In this article. Full Article. Figures & data.
Jan 22, 2022 The proposed strategy achieved the highest accuracy in the machine learning model by increasing cluster diversity. Wu et al. decomposed Android apps into manifest files, Dalvik code files, and basic library files to detect maliciousness and identify their families to analyze and classify Android malware applications efficiently. Therefore ...
A Review on Malware Analysis for IoT and Android System. Chandra Shekhar Yadav1 Sangeeta Gupta2. Received: 31 December 2021 / Accepted: 10 November 2022 / Published online: 21 December 2022 The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd 2022. Abstract.
Jan 30, 2024 Analyzing and Detecting Malware Using Machine Learning and Deep Learning. January 2024. DOI: 10.1007/978-3-031-48573-2_75. In book: Artificial Intelligence, Data Science and Applications (pp.518 ...
Nov 2, 2023 A comparative analysis of three prominent malware detection tools, Google Rapid Response (GRR), Wireshark, and VirusTotal, is also conducted, aiding in informed decision-making for enhancing ...
Jul 5, 2021 This paper provides a systematic review of ML-based Android malware detection techniques. It critically evaluates 106 carefully selected articles and highlights their strengths and weaknesses as well as potential improvements.
Request PDF | On Nov 1, 2023, Ndukwe-Oke Eke and others published Comparative Analysis of Fully Automated Testing Techniques for Android Applications | Find, read and cite all the research you ...
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