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Artificial intelligence-based antivirus in order to detect malware preventively

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Abstract

The proposed paper investigates commercial antiviruses. About 17% of the antiviruses did not recognize the existence of the malicious samples analyzed. In order to overcome the limitations of commercial antiviruses, this project creates an antivirus able to identify the modus operandi of a malware application before it is even executed by the user. In the proposed methodology, the features extracted from the executables are the input attributes of artificial neural networks. The classification of neural networks aims to group executables of 32-bit architectures into two classes: benign and malware. In total, 6272 executables are used in order to validate the proposed methodology. The proposed antivirus achieves an average performance of 98.32% in the distinction between benign and malware executables, accompanied by an average response time of only 0.07 s. Our antivirus is statistically superior and more effective when compared to the best state-of-the-art antivirus. The limitations of commercial antiviruses can be catering for artificial intelligence techniques based on machine learning. Instead of empirical and heuristic models, the proposed work identifies, in a statistical way, behaviors previously classified as suspects in real time.

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Notes

  1. Windows PE format. Available in https://docs.microsoft.com/en-us/windows/desktop/debug/pe-format. Accessed on June 2019.

  2. TheZoo: 32-bit architectures malwares executable databases. Available in: https://github.com/ytisf/theZoo. Accessed on November 2018.

  3. Sourceforge: Benign Application Repository. Available in: https://sourceforge.net/. Accessed on November 2018.

  4. Github: Benign Application Repository. Available in: https://github.com/. Accessed on November 2018.

  5. Sysinternals: Benign Application Repository. Available in: https://live.sysinternals.com/. Accessed on November 2018.

  6. PE (Portable Executable) file explorer. Available in: http://www.heaventools.com/. Accessed on February 2019.

  7. IDA: multiprocessor disassembler. Available in: https://www.hex-rays.com/products/ida/. Accessed on February 2019.

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Authors and Affiliations

  1. Electronics and Systems Department, Federal University of Pernambuco, Arquitetura Avenue No Number, Block A, 4th Andar, Cidade Universitária, Recife, Brazil

    Sidney M. L. de Lima

  2. Tempest Security Intelligence, Alfândega Street n.35, Bairro Do Recife, Recife, Brazil

    Heverton K. de L. Silva & João H. da S. Luz

  3. São Miguel Faculty, Conde da Boa Vista Avenue n. 1410, Boa Vista, Recife, Brazil

    Hercília J. do N. Lima, Samuel L. de P. Silva, Anna B. A. de Andrade & Alisson M. da Silva

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  1. Sidney M. L. de Lima
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  3. João H. da S. Luz
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  4. Hercília J. do N. Lima
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Correspondence to Sidney M. L. de Lima.

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de Lima, S.M.L., Silva, H.K.d.L., Luz, J.H.d.S. et al. Artificial intelligence-based antivirus in order to detect malware preventively. Prog Artif Intell 10, 1–22 (2021). https://doi.org/10.1007/s13748-020-00220-4

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