<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>Malware remains a big threat to cyber security, calling for machine learning-based malware detection. Malware variations exhibit common behavioral patterns indicative of their source and intended use to enhance the existing framework’s usefulness. Here we present a novel model, i.e., African Vulture Optimization-based Decision Tree (AVO-DT) to increase the overall optimization.</p>
<p>The datasets from Android apps and malware software train the AVO-DT model. After training, the datasets are pre-processed by removing training errors. The DT algorithm is used by the developed AVO model to carry out the detection procedure and predict malware activity. To detect malware activities and improve accuracy, such an AVO-DT model technique employs both static and dynamic methodologies. The other measurements on Android applications might be either malicious or benign. Here we also developed malware prevention and detection systems to address ambiguous search spaces in multidimensionality difficulties and resolve optimization challenges.</p>
</abstract>

Malware remains a big threat to cyber security, calling for machine learning-based malware detection. Malware variations exhibit common behavioral patterns indicative of their source and intended use to enhance the existing framework’s usefulness. Here we present a novel model, i.e., African Vulture Optimization-based Decision Tree (AVO-DT) to increase the overall optimization.
The datasets from Android apps and malware software train the AVO-DT model. After training, the datasets are pre-processed by removing training errors. The DT algorithm is used by the developed AVO model to carry out the detection procedure and predict malware activity. To detect malware activities and improve accuracy, such an AVO-DT model technique employs both static and dynamic methodologies. The other measurements on Android applications might be either malicious or benign. Here we also developed malware prevention and detection systems to address ambiguous search spaces in multidimensionality difficulties and resolve optimization challenges.

African Vulture Optimization-Based Decision Tree (AVO-DT): An Innovative Method for Malware Identification and Evaluation through the Application of Meta-Heuristic Optimization Algorithm

Cybernetics and Information Technologies

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Malware remains a big threat to cyber security, calling for machine learning-based malware detection. Malware variations exhibit common behavioral patterns...

African Vulture Optimization-Based Decision Tree (AVO-DT): An Innovative Method for Malware Identification and Evaluation through the Application of Meta-Heuristic Optimization Algorithm

Online veröffentlicht: 27. Juni 2024

Seitenbereich: 142 - 155

Eingereicht: 12. Jan. 2024

Akzeptiert: 31. Mai 2024

DOI: https://doi.org/10.2478/cait-2024-0020

Schlüsselwörter
Android, Malware detection, Malicious software, Android malware detection, Machine learning, Classification

© 2024 Praveen Kumar Kaithal et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

African Vulture Optimization-Based Decision Tree (AVO-DT): An Innovative Method for Malware Identification and Evaluation through the Application of Meta-Heuristic Optimization Algorithm

Online veröffentlicht: 27. Juni 2024

Seitenbereich: 142 - 155

Eingereicht: 12. Jan. 2024

Akzeptiert: 31. Mai 2024

DOI: https://doi.org/10.2478/cait-2024-0020

SchlüsselwörterAndroid, Malware detection, Malicious software, Android malware detection, Machine learning, Classification

© 2024 Praveen Kumar Kaithal et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Schlüsselwörter
Android, Malware detection, Malicious software, Android malware detection, Machine learning, Classification