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@article{174223,
        author = {Mr. K. Sudhakar and P. Pallavi and P.Sanjay Kumar and P. Harshitha Syamala and M. Siva Manikanta},
        title = {Android Malware Detection Using Optimal Ensemble Learning Approach For Cyber Security},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {10},
        pages = {4685-4692},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=174223},
        abstract = {Malware attacks have increased due to the growing use of Android devices, endangering user data and system integrity. Identification of malware using machine learning can analyse large datasets and identify patterns that point to malicious behaviour, it has become increasingly popular. To increase detection accuracy and reduce false positives, this study suggests an ideal ensemble learning strategy that makes use of the advantages of several classifiers. The model uses methods for feature extraction, selection, and classification that are tailored for the detection of Android malware.
The effectiveness of the suggested system in recognising different malware types is demonstrated by experimental results, guaranteeing strong cyber security.},
        keywords = {Android Malware Detection, Cyber Security, Optimal Ensemble Learning, Machine Learning .},
        month = {April},
        }