Optimizing Software Vulnerability Detection with MDSADNet: A Multi-Scale Convolutional Approach Enhanced by Mantis-Inspired Optimization
Year: 2024
Type: article
Abstract: The persistent emergence of software vulnerabilities necessitates the development of effective detection methodologies.Machine learning (ML) and deep learning (DL) offer promising avenues for automating feature extraction; however, their efficacy in vulnerability detection remains insufficiently explored.This study introduces the Multi-Deep Software Automation Detection Network (MDSADNet) to enhance binary and multi-class software classification.Unlike traditional one-dimensional Convolutional Neural Networks (CNNs), MDSADNet employs a novel two-dimensional multi-scale convolutional process to capture both intra-data and inter-data n-gram features.Experimental evaluations conducted on binary and multi-class datasets demonstrate MDSADNet's superior performance in software automation classification.Furthermore, the Mantis Search Algorithm (MSA), inspired by the foraging and mating behaviors of mantises, was incorporated to optimize MDSADNet's hyperparameters.This optimization process was structured into three distinct stages: sexual cannibalism, prey pursuit, and prey assault.The model's validation involved performance metrics such as F1-score, recall, accuracy, and precision.Comparative analyses with state-of-the-art DL and ML models highlight MDSADNet's enhanced classification capabilities.The results indicate that MDSADNet significantly outperforms existing models, achieving higher accuracy and robustness in detecting software vulnerabilities.
DOI: 10.56578/ida030204
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4401034140