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@article{164483,
        author = {Yeturu Sai Praneeth Reddy and Gurrala Shashi Kumar and Mukesh Gilda  and Penmatsa Shivaram Sandeep Varma},
        title = {Android Malware Detection Using Genetic Algorithm },
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {10},
        number = {12},
        pages = {1018-1023},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=164483},
        abstract = {This study presents an innovative approach for enhancing Android malware detection through a Genetic Algorithm (GA)-based optimized feature selection coupled with machine learning techniques. Leveraging the evolutionary principles of GA, the proposed method effectively identifies a subset of features from a large pool, maximizing the discriminative power while minimizing computational complexity. By integrating this feature selection mechanism with machine learning classifiers, the system achieves superior performance in distinguishing between, benign and malicious Android applications. Through extensive experimentation and evaluation using real-world datasets, the effectiveness of the proposed framework is demonstrated, showcasing significant improvements in detection accuracy, scalability, and efficiency compared to traditional methods. This research contributes to the advancement of Android security, offering a robust and adaptable solution for combating evolving malware threats in mobile ecosystems.},
        keywords = {Genetic Algorithm, Machine Learning, Android Malware, Feature Selection Mechanism, Accuracy.},
        month = {},
        }