International Journal of Advances in Applied Computational Intelligence

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https://doi.org/10.54216/IJAACI

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Volume 4 , Issue 2 , PP: 26-32, 2023 | Cite this article as | XML | Html | PDF | Full Length Article

Enhancing Malware Detection in Cybersecurity through Optimized Machine Learning Technique

Ahmed Aziz 1 * , Sanjar Mirzaliev 2 , Yuldashev Maqsudjon 3

  • 1 Tashkent State Universtiy of Economics, Tashkent, Uzbekistan - (a.mohamed@tsue.uz)
  • 2 Tashkent State Universtiy of Economics, Tashkent, Uzbekistan - (sanjar2611@gmail.com)
  • 3 Tashkent State Universtiy of Economics, Tashkent, Uzbekistan - (maqsudjon.yuldashev@tsue.uz)
  • Doi: https://doi.org/10.54216/IJAACI.040203

    Received: April 17, 2023 Revised: June 12, 2023 Accepted: August 16, 2023
    Abstract

    This research is about the increasing cybersecurity challenges posed by modern malware threats and argues for an improved approach through optimized machine learning algorithms. We apply a Tree-structured Parzen Estimator (TPE) for hyperparameter tuning, focusing on the optimization of tree-based models such as Random Forest and Gradient Boosting. Our methodology includes careful correlation analysis, variable distribution examination, and feature importance assessment to make our models more robust and transparent. We present comprehensive visualizations that demonstrate the results of our optimized approach, which show improved accuracy, precision, and recall in malware detection. Our findings highlight the significance of feature engineering and model tuning, revealing subtle patterns indicative of malicious behavior. The findings indicate that our model provides a method that not only improves detection capabilities but also emphasizes the need for continuous improvement and innovation in addressing the ever-changing nature of malware threats.

    Keywords :

    Cybersecurity , Malware , Machine Learning , Security Threats , Data Analysis , Feature Engineering , Predictive Modeling , Cyber Threat Intelligence , Pattern Recognition.

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    Cite This Article As :
    Aziz, Ahmed. , Mirzaliev, Sanjar. , Maqsudjon, Yuldashev. Enhancing Malware Detection in Cybersecurity through Optimized Machine Learning Technique. International Journal of Advances in Applied Computational Intelligence, vol. , no. , 2023, pp. 26-32. DOI: https://doi.org/10.54216/IJAACI.040203
    Aziz, A. Mirzaliev, S. Maqsudjon, Y. (2023). Enhancing Malware Detection in Cybersecurity through Optimized Machine Learning Technique. International Journal of Advances in Applied Computational Intelligence, (), 26-32. DOI: https://doi.org/10.54216/IJAACI.040203
    Aziz, Ahmed. Mirzaliev, Sanjar. Maqsudjon, Yuldashev. Enhancing Malware Detection in Cybersecurity through Optimized Machine Learning Technique. International Journal of Advances in Applied Computational Intelligence , no. (2023): 26-32. DOI: https://doi.org/10.54216/IJAACI.040203
    Aziz, A. , Mirzaliev, S. , Maqsudjon, Y. (2023) . Enhancing Malware Detection in Cybersecurity through Optimized Machine Learning Technique. International Journal of Advances in Applied Computational Intelligence , () , 26-32 . DOI: https://doi.org/10.54216/IJAACI.040203
    Aziz A. , Mirzaliev S. , Maqsudjon Y. [2023]. Enhancing Malware Detection in Cybersecurity through Optimized Machine Learning Technique. International Journal of Advances in Applied Computational Intelligence. (): 26-32. DOI: https://doi.org/10.54216/IJAACI.040203
    Aziz, A. Mirzaliev, S. Maqsudjon, Y. "Enhancing Malware Detection in Cybersecurity through Optimized Machine Learning Technique," International Journal of Advances in Applied Computational Intelligence, vol. , no. , pp. 26-32, 2023. DOI: https://doi.org/10.54216/IJAACI.040203