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International Journal of Advances in Applied Computational Intelligence
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Title

Deep Learning Defenders: Harnessing Convolutional Networks for Malware Detection

  Ahmed Abdelmonem 1 * ,   Shimaa S. Mohamed 2

1  Faculty of Computers and Informatics, Zagazig University, Zagazig 44519, Sharqiyah, Egypt
    (aabdelmounem@zu.edu.eg)

2  Faculty of Computers and Informatics, Zagazig University, Zagazig 44519, Sharqiyah, Egypt
    (shimaa_said@zu.edu.eg)

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Doi   :   https://doi.org/10.54216/IJAACI.010203

Received: January 15, 2022 Accepted: May 23, 2022

Abstract :

Malware attacks continue to pose a significant threat to computer systems and networks worldwide. Traditional signature-based malware detection methods have proven to be insufficient in detecting the increasing number of sophisticated malware variants. This has led to the exploration of new approaches, including machine learning-based techniques. In this paper, we propose a novel approach to malware detection using residually connect convolutional networks. We demonstrate the effectiveness of our approach by training CNN on a large dataset of malware samples and benign files and evaluating its performance on a separate test set. Extensive experiments on a public dataset of malware images demonstrated that our model could achieve high accuracy in detecting both known and unknown malware samples. The findings suggest that our residual convolution has great potential for improving malware detection and enhancing the security of computer systems and networks.

Keywords :

Computational Intelligence; Deep Learning; Convolutional Neural Networks; Malware Detection;  Machine Learning.

References :

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Cite this Article as :
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MLA Ahmed Abdelmonem, Shimaa S. Mohamed. "Deep Learning Defenders: Harnessing Convolutional Networks for Malware Detection." International Journal of Advances in Applied Computational Intelligence, Vol. 1, No. 2, 2022 ,PP. 46-55 (Doi   :  https://doi.org/10.54216/IJAACI.010203)
APA Ahmed Abdelmonem, Shimaa S. Mohamed. (2022). Deep Learning Defenders: Harnessing Convolutional Networks for Malware Detection. Journal of International Journal of Advances in Applied Computational Intelligence, 1 ( 2 ), 46-55 (Doi   :  https://doi.org/10.54216/IJAACI.010203)
Chicago Ahmed Abdelmonem, Shimaa S. Mohamed. "Deep Learning Defenders: Harnessing Convolutional Networks for Malware Detection." Journal of International Journal of Advances in Applied Computational Intelligence, 1 no. 2 (2022): 46-55 (Doi   :  https://doi.org/10.54216/IJAACI.010203)
Harvard Ahmed Abdelmonem, Shimaa S. Mohamed. (2022). Deep Learning Defenders: Harnessing Convolutional Networks for Malware Detection. Journal of International Journal of Advances in Applied Computational Intelligence, 1 ( 2 ), 46-55 (Doi   :  https://doi.org/10.54216/IJAACI.010203)
Vancouver Ahmed Abdelmonem, Shimaa S. Mohamed. Deep Learning Defenders: Harnessing Convolutional Networks for Malware Detection. Journal of International Journal of Advances in Applied Computational Intelligence, (2022); 1 ( 2 ): 46-55 (Doi   :  https://doi.org/10.54216/IJAACI.010203)
IEEE Ahmed Abdelmonem, Shimaa S. Mohamed, Deep Learning Defenders: Harnessing Convolutional Networks for Malware Detection, Journal of International Journal of Advances in Applied Computational Intelligence, Vol. 1 , No. 2 , (2022) : 46-55 (Doi   :  https://doi.org/10.54216/IJAACI.010203)
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