[{“box”:0,”content”:”n[if 992 equals=”Open Access”]n
n
Open Access
nn
n
n[/if 992]n[if 2704 equals=”Yes”]n
nThis is an unedited manuscript accepted for publication and provided as an Article in Press for early access at the author’s request. The article will undergo copyediting, typesetting, and galley proof review before final publication. Please be aware that errors may be identified during production that could affect the content. All legal disclaimers of the journal apply.n
n[/if 2704]n
n
n
nn
n
Harsh Kumar, Rashid Rafiq Shah, Zubair Fayaz,
n t
n
n[/foreach]
n
n[if 2099 not_equal=”Yes”]n
- [foreach 286] [if 1175 not_equal=””]n t
- Student, Assistant Professor, Assistant Professor, Department of Computer Science and Engineering, Akal University, Talwandi Sabo, Department of Computer Science and Engineering, Akal University, Talwandi Sabo, Department of Computer Science and Engineering, Akal University, Talwandi Sabo, Punjab, Punjab, Punjab, India, India, India
n[/if 1175][/foreach]
n[/if 2099][if 2099 equals=”Yes”][/if 2099]n
Abstract
n
n
nThe widespread growth of Android malware has become a significant mobile security threat during the past few years thus requiring the development of strong detection solutions. The primary tool applied in this research for Android malware detection consists of app permissions. The main indicator in the dataset for identifying malicious and benign applications functions through displaying application permission information. The evaluation of particular permission relationships with malware behavior leads to the development of a machine learning model which then classifies applications into safe and potentially dangerous categories. The method utilizes supervised learning methodologies to check permission pattern predictions which results in a portable and efficient detection solution. Experimental testing demonstrates that the detection model performs accurately for malware identification which can serve as a foundation to improve Android systems based on permission security.nn
n
Keywords: Android security, android malware detection, machine learning, deep learning, malware detection
n[if 424 equals=”Regular Issue”][This article belongs to Journal Of Network security ]
n
n
n
n
nHarsh Kumar, Rashid Rafiq Shah, Zubair Fayaz. [if 2584 equals=”][226 wpautop=0 striphtml=1][else]Hybrid DL-ML Approach for Android Malware Detection[/if 2584]. Journal Of Network security. 17/09/2025; 13(03):18-25.
n
nHarsh Kumar, Rashid Rafiq Shah, Zubair Fayaz. [if 2584 equals=”][226 striphtml=1][else]Hybrid DL-ML Approach for Android Malware Detection[/if 2584]. Journal Of Network security. 17/09/2025; 13(03):18-25. Available from: https://journals.stmjournals.com/jons/article=17/09/2025/view=0
nn
n
n[if 992 not_equal=”Open Access”]n
n
n[/if 992]n
nn
n
n
n
References n
n[if 1104 equals=””]n
- Bulut I, Yavuz AG. Mobile malware detection using deep neural network. In 2017 IEEE 25th Signal Processing and Communications Applications Conference (SIU). 2017 May 15; 1–4.
- Sahs J, Khan L. A machine learning approach to android malware detection. In 2012 IEEE European intelligence and security informatics conference. 2012 Aug 22; 141–147.
- Yerima SY, Sezer S, Muttik I. Android malware detection using parallel machine learning classifiers. In 2014 IEEE Eighth international conference on next generation mobile apps, services and technologies. 2014 Sep 10; 37–42.
- Peiravian N, Zhu X. Machine learning for android malware detection using permission and api calls. In 2013 IEEE 25th international conference on tools with artificial intelligence. 2013 Nov 4; 300–305.
- Rathore H, Sahay SK, Rajvanshi R, Sewak M. Identification of significant permissions for efficient android malware detection. In International conference on broadband communications, networks and systems. Cham: Springer International Publishing; 2020 Dec 11; 33–52.
- Zhao L, Li D, Zheng G, Shi W. Deep neural network based on android mobile malware detection system using opcode sequences. In 2018 IEEE 18th International Conference on Communication Technology (ICCT). 2018 Oct 8; 1141–1147.
- Zhang T, Di S, Wang X, Zhu D, Ren X. 1-Hour Ahead Wind Power Prediction Based on Multi-model Fusion Strategy. In Annual Conference of China Electrotechnical Society. Singapore: Springer Nature Singapore; 2024 Sep 20; 263–274.
- Khalifa MA, Elsayed A, Hussien A, Hussainy AS. Android malware detection and prevention based on deep learning and tweets analysis. In 2024 IEEE 6th International Conference on Computing and Informatics (ICCI). 2024 Mar 6; 153–157.
- Kumar H, Chopan AA. Hybrid ML-DL Approach for Android Malware Detection. Research Square. 2024 Nov. PREPRINT (Version 1) available at [https://doi.org/10.21203/rs.3.rs-5358924/ v1]
- Xiong S, Zhang H. A multi-model fusion strategy for android malware detection based on machine learning algorithms. Journal of Computer Science Research. 2024 Jun; 6(2): 1–11.
nn[/if 1104][if 1104 not_equal=””]n
- [foreach 1102]n t
- [if 1106 equals=””], [/if 1106][if 1106 not_equal=””],[/if 1106]
n[/foreach]
n[/if 1104]
n
nn[if 1114 equals=”Yes”]n
n[/if 1114]
n
n
n
| Volume | 13 | |
| [if 424 equals=”Regular Issue”]Issue[/if 424][if 424 equals=”Special Issue”]Special Issue[/if 424] [if 424 equals=”Conference”][/if 424] | 03 | |
| Received | 28/04/2025 | |
| Accepted | 06/08/2025 | |
| Published | 17/09/2025 | |
| Retracted | ||
| Publication Time | 142 Days |
n
n
nn
n
Login
PlumX Metrics
n
n
n[if 1746 equals=”Retracted”]n
[/if 1746]nnn
nnn”}]