Optimizing Software Vulnerability Detection with MDSADNet: A Multi-Scale Convolutional Approach Enhanced by Mantis-Inspired Optimization
Journal Title: Information Dynamics and Applications - Year 2024, Vol 3, Issue 2
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 -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.
Authors and Affiliations
Srinivasa Rao Vemula, Maruthi Vemula, Ramesh Vatambeti
Keywords
Related Articles
- EP ID EP744290
- DOI https://doi.org/10.56578/ida030204
- Views 66
- Downloads 1
Integration of Ontology Transformation into Hidden Markov Model
The goal of this study is to suggest a method for turning an ontology into a hidden Markov model (HMM). Ontology properties (relationships between classes) and ontology classes are taken as HMM symbols and states, respec...
An Enhanced Convolutional Neural Network for Accurate Classification of Grape Leaf Diseases
Grape leaf diseases can significantly reduce grape yield and quality, making accurate and efficient identification of these diseases crucial for improving grape production. This study proposes a novel classification meth...
Enhancing Healthcare Data Security in IoT Environments Using Blockchain and DCGRU with Twofish Encryption
In the rapidly evolving landscape of digital healthcare, the integration of cloud computing, Internet of Things (IoT), and advanced computational methodologies such as machine learning and artificial intelligence (AI) ha...
Leveraging Artificial Intelligence for Blackhole Attack Detection in MANETs: A Comparative Study
Blackhole attacks represent a significant threat to the security of communication networks, particularly in emerging network architectures such as Mobile Ad Hoc Networks (MANETs). These attacks, characterized by their ab...
ECO-LEACH: A Blockchain-Based Distributed Routing Protocol for Energy-Efficient Wireless Sensor Networks
This paper proposes a novel architecture based on blockchain technology to enhance the dependability and safety of wireless sensor networks (WSN) by authenticating WSN nodes. In a WSN, sensor nodes collect and transmit d...