Enhancing Android Security Through Artificial Intelligence: A Hyperparameter-Tuned Deep Learning Approach for Robust Software Vulnerability Detection

Abstract: Detecting software vulnerabilities is essential for cybersecurity, particularly in Android systems, which are widely used and vulnerable due to their open-source nature. Conventional signature-based malware detection methods are inadequate against sophisticated and evolving threats. This paper introduces a Hyperparameter-Tuned Deep Learning Approach for Robust Software Vulnerability Detection (HPTDLA-RSVD) aimed at enhancing Android security through an optimized deep learning model. The HPTDLA-RSVD methodology encompasses min-max data normalization, feature selection using the Ant Lion Optimizer (ALO), classification via a Deep Belief Network (DBN), and hyperparameter optimization with the Slime Mould Algorithm (SMA). Experimental evaluations on a benchmark dataset reveal that HPTDLA-RSVD surpasses existing techniques across multiple performance metrics, confirming its efficacy in identifying and mitigating software vulnerabilities on Android platforms.

Keywords: Artificial Intelligence, Software Vulnerability, Cybersecurity, Deep Learning, Ant Lion Optimizer, Hyperparameter Tuning

DOI: 10.54941/ahfe1005919

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