Скачать или смотреть Enhancing Malware Detection Capabilities Using Deep Learning with Advanced Hyperparameter Tuning

Authors: Walid EL MOUHTADI, Yassine MALEH, Soufyane Mounir (IJEECS ID 38599)

As the threat landscape evolves with the emergence of sophisticated malware and Advanced Persistent Threats (APTs), there is an increasing need for an effective and efficient solution for detection. Traditional methods, such as signature-based and heuristic analysis, have limitations in keeping pace with the rapidly changing nature of malicious activities. On the other hand, while machine learning presents a promising approach, it often falls short due to the manual extraction and selection of features, resulting in time-consuming and error-prone processes. This research paper introduces a novel solution for malware detection, leveraging the power of deep learning to automatically treat machine learning weaknesses that are manual feature selection and extraction. The proposed approach customizes the ANN, CNN, and RNN hyperparameters to enhance detection capabilities. By tailoring these deep learning algorithms, the research aims to address the shortcomings of traditional methods and machine learning approaches. The solution presented in this research offers an effective means of malware detection and provides a structured methodology for customizing detection mechanisms. A comparative study highlights the advantages of the customized deep learning approach over traditional methods that rely on default algorithms. The results of this study demonstrate the superior performance, accuracy, and adaptability of the proposed solution in the face of evolving cyber threats. This research contributes to the ongoing efforts to develop robust and adaptive solutions in the ever-evolving cybersecurity landscape.

Indonesian Journal of Electrical Engineering and Computer Science
https://ijeecs.iaescore.com

Supported by Master Program of Electrical and Computer Engineering, Universitas Ahmad Dahlan, https://mee.uad.ac.id #yogyakarta
Admission: https://mee.uad.ac.id/pendaftaran/

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