Novel nature-inspired optimization approach-based svm for identifying the android malicious data

Malicious malware targeting Android systems has alarmingly increased due to the quick spread of Android devices. For these devices to be secure and to protect the private data of users, Android virus detection is essential. The selection of features, model performance, and efficiency are issues with existing Android malware detection techniques. To overcome these drawbacks, we suggest a unique method for identifying malicious Android data that combines Tree Seed Optimization with Support Vector Machines (TSO-SVM).TSO is a nature-inspired optimization technique that looks for the best feature subsets by simulating the tree's seed dispersal process. The efficiency and effectiveness of SVM-based classification are increased by our method's use of TSO to choose the most instructive features from the Android malware dataset. To normalize the features of the Android application dataset before training, we use a data-cleaning method known as Z-Score normalization. Our Android malware detection solution uses Independent Component Analysis (ICA) as a feature reduction method. Our test results show how well the TSO-SVM technique works at detecting Malicious Android data. In terms of accuracy, precision, recall, and F1-Score for malicious detection, the suggested model achieves 97.12%, 96.35%, 97.88%, and 96.84%, respectively. The proposed technique successfully solves the problem of suboptimal classification accuracy in the presence of dynamic and changing malware threats. The results of this work highlight the potential of TSO techniques for enhancing the security of Android-based devices and present a promising direction for further investigation in the area of mobile security.