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<p>In this paper, we develop a deep learning model that aims at the identification of the vulnerabilities in an interactive software. The study identifies the classes of vulnerabilities and then makes the deep learning model to get trained with such classes. Upon training, it is effectively tested on a new set of data. The experiments are conducted in terms of real-interactive software, where the codes are generated automatically for the testing purpose. The study is further validated in terms of accuracy and error in finding the efficacy on large, labelled classes. The study is comparing the proposed ResNet-50 + DT framework with the advanced architectures including Bi-LSTM with attention, Temporal Convolutional Networks (TCN), and Transformer-based models in allowing a performance assessment.</p>

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Effective Vulnerability Detection in Interactive Software via Deep Learning Optimization

  • 期刊论文

  • 发表日期：

    2026年5月20日

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  • Jasem Alostad

摘要

In this paper, we develop a deep learning model that aims at the identification of the vulnerabilities in an interactive software. The study identifies the classes of vulnerabilities and then makes the deep learning model to get trained with such classes. Upon training, it is effectively tested on a new set of data. The experiments are conducted in terms of real-interactive software, where the codes are generated automatically for the testing purpose. The study is further validated in terms of accuracy and error in finding the efficacy on large, labelled classes. The study is comparing the proposed ResNet-50 + DT framework with the advanced architectures including Bi-LSTM with attention, Temporal Convolutional Networks (TCN), and Transformer-based models in allowing a performance assessment.