Published Paper Details:

The identification of mobile malware has emerged as a critical concern due to the widespread usage of mobile devices in our daily lives and the storing of significant personal and financial data on these devices. It has become more and more important to protect sensitive information on smartphones and tablets from unwanted assaults. The identification of mobile malware has emerged as a critical concern due to the widespread usage of mobile devices in our daily lives and the storing of significant personal and financial data on these devices. It has become more and more important to protect sensitive information on smartphones and tablets from unwanted assaults. Since machine learning enables us to automatically identify patterns and features in the data that may be suggestive of mobile malware, it can be a strong tool for malware detection on mobile devices. Following are some steps: Data gathering: In order to train and test the machine learning models, a significant dataset of mobile apps-- both malicious and benign--must be gathered first. Model choice: Mobile malware can be detected using a variety of machine learning algorithms, including decision trees, random forests, and support vector machines. model education The labelled dataset, in which each app is classified as either benign or malicious, is used to train the machine learning model. To reduce prediction errors, the model's parameters must be improved iteratively during the training phase. The model gains the ability to generate precise predictions by modifying its internal parameters after being exposed to a sizable dataset of input-output pairings. The training algorithm's optimisation goal is to reduce the discrepancy between the model's predictions and the desired results. The model continuously increases its capacity to make precise predictions and generalise to unobserved data through numerous iterations and modifications. Metrics including precision, accuracy, F1 score, and recall are frequently used to judge a model's efficacy. exemplary deployment The model is prepared for use in real-world scenarios for mobile virus detection following the completion of the training and evaluation phases. It's important to note that creating a machine learning strategy that effectively detects mobile malware can be difficult because malware authors are continuously coming up with innovative and complex ways to avoid detection.