• Mohamed Zehruddin Badusha.M.K
• Saravanan.M
• Vignesh.S
• Rajesh.R
• Mrs.S.Saraswathi

Android has been the most popular operating system over the past ten years. Despite this quickly rising. Android is a target for malware distribution due to its prevalence. Applications from unofficial marketplaces can be installed on Android devices. The ability to install malicious apps and interact with Android devices is made possible by this reality. Both static analysis and dynamic analysishave so far been used in the development of malware analysis and detection tools. However, the performance of current research is still lacking in terms of correctly and efficiently detecting malware. It frequently makes use of numerous resources from mobile devices with limited resources to identify malware accurately. Therefore, by creating and testing an accurate and efficient machine learning and deep learning model for this issue, this study suggests a solution. The dataset of malware genomes was utilized.Test results show that hybrid analysis increases by 5% of malicious software detections.

prevalence, malicious, malware gnomes, datasets, hybrid analysis.

"Hybrid analysis of malware detection in android", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.10, Issue 7, page no.a382-a386, July-2023, Available :http://www.jetir.org/papers/JETIR2307044.pdf

"Hybrid analysis of malware detection in android", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.10, Issue 7, page no. ppa382-a386, July-2023, Available at : http://www.jetir.org/papers/JETIR2307044.pdf