Android Malware Detection: Necessity, Applications and Future Direction

The increasing adoption of Android devices has made them a primary target for malware attacks, creating critical challenges in ensuring user security and privacy. This review paper examines state-of-the-art techniques in Android malware detection, focusing on the application of Machine Learning (ML) and Deep Learning (DL) methods. Traditional signature-based approaches, while effective against known threats, fail to address the rapidly evolving nature of malware, highlighting the need for adaptive and scalable solutions. Through a detailed literature review, this paper analyzes static, dynamic, and hybrid detection methodologies, emphasizing the strengths and limitations of each. Special attention is given to feature engineering techniques and their role in optimizing model accuracy and computational efficiency. The paper also identifies key research gaps, such as the need for lightweight detection models suitable for resource-constrained devices and the lack of standardized benchmarks for evaluating detection systems. By synthesizing existing approaches, this review aims to provide insights into current challenges and opportunities in Android malware detection, paving the way for more robust and effective solutions in the future.