FORTIFYING ANDROID SECURITY: HYPERPARAMETER TUNED DEEP LEARNING APPROACH FOR ROBUST SOFTWARE VULNERABILITY DETECTION

Nada Alzaben, Abdullah M. Alashjaee, Mohammed Maray, Shoayee Dlaim Alotaibi, Abeer A. K. Alharbi and Ahmed Sayed
Additional contact information
Nada Alzaben: Department of Computer Sciences, College of Computer and Information Sciences Princess, Nourah bint Abdulrahman University (PNU), P. O. Box 84428, Riyadh 11671, Saudi Arabia
Abdullah M. Alashjaee: ��Department of Computer Sciences, Faculty of Computing and Information Technology Northern Border University, Rafha 91911, Saudi Arabia
Mohammed Maray: ��Department of Information Systems, College of Computer Science, King Khalid University Abha, Saudi Arabia
Shoayee Dlaim Alotaibi: �Department of Artificial Intelligence and Data Science, College of Computer Science and Engineering, University of Hail, Hail, Saudi Arabia
Abeer A. K. Alharbi: �Department of Information Systems, College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 11432, Saudi Arabia
Ahmed Sayed: ��Research Center, Future University in Egypt, New Cairo 11835, Egypt

FRACTALS (fractals), 2025, vol. 33, issue 02, 1-14

Abstract: Detecting software vulnerabilities is a vital component of cybersecurity, concentrating on identifying and remedying weaknesses or flaws in software that malicious actors could exploit. Improving Android security includes using robust software vulnerability detection processes to identify and mitigate possible threats. Leveraging advanced methods like dynamic and static analysis and machine learning (ML) approaches with fractals theories these models early scan Android apps for vulnerabilities. Effectual software vulnerability detection is critical to mitigate safety risks, security systems, and data from cyber-attacks. Android malware detection employing deep learning (DL) supports the control of neural networks (NNs) for identifying and mitigating malicious apps targeting the Android and Complex Systems platforms. DL approaches, namely recurrent neural networks (RNNs) and convolutional neural networks (CNNs) can be trained on massive datasets encompassing benign and malicious samples. This study develops a Hyperparameter Tuned Deep Learning Approach for Robust Software Vulnerability Detection (HPTDLA-RSVD) technique. The primary aim of the HPTDLA-RSVD technique is to ensure Android malware security using an optimal DL model. In the HPTDLA-RSVD technique, the min–max normalization method is applied to scale the input data into a uniform format. In addition, the HPTDLA-RSVD methodology employs ant lion fractal optimizer (ALO)-based feature selection (FS) named ALO-FS methodology for choosing better feature sets. Besides, the HPTDLA-RSVD technique uses a deep belief network (DBN) model for vulnerability detection and classification. Moreover, the slime mould algorithm (SMA) has been executed to boost the hyperparameter tuning process of the DBN approach. The experimental value of the HPTDLA-RSVD approach can be examined by deploying a benchmark database. The simulation outcomes implied that the HPTDLA-RSVD approach performs better than existing approaches with respect to distinct measures.

Keywords: Software Vulnerability; Cybersecurity; Deep Learning; Ant Lion Fractal Optimizer; Hyperparameter Tuning (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.worldscientific.com/doi/abs/10.1142/S0218348X25400432
Access to full text is restricted to subscribers

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:wsi:fracta:v:33:y:2025:i:02:n:s0218348x25400432

Ordering information: This journal article can be ordered from

DOI: 10.1142/S0218348X25400432

Access Statistics for this article

FRACTALS (fractals) is currently edited by Tara Taylor

More articles in FRACTALS (fractals) from World Scientific Publishing Co. Pte. Ltd.
Bibliographic data for series maintained by Tai Tone Lim ().