Improving mobile security: A study on android malware detection using LOF

Abstract

The ubiquity of smartphones in our daily lives has made them attractive targets for malicious actors seeking to compromise user data and device functionality. Android malware detection has become imperative to protect user privacy and device integrity. This paper presents a focused study on leveraging the Local Outlier Factor (LOF) method for Android malware detection using the DREBIN dataset. Our research addresses the need for accurate and efficient Android malware detection. We explore the LOF method, an anomaly-based detection technique, to assess its effectiveness in distinguishing malicious applications from benign ones within the Android ecosystem. Rigorous experiments using the extensive DREBIN dataset reveal LOF's superiority in accuracy, precision, recall, and False Positive Rate (FPR). We introduce additional metrics like Area Under the Curve (AUC), Matthews Correlation Coefficient (MCC), and True Negative Rate (TNR) to comprehensively evaluate LOF. Our findings highlight LOF's ability to balance false positives and false negatives, making it an ideal choice for Android malware detection. We emphasize the importance of representative datasets, such as DREBIN, for validation. In conclusion, this research positions LOF as a reliable tool for Android malware detection, offering robust protection against emerging threats. As mobile technology evolves, our study encourages further exploration of advanced techniques and real-world deployment scenarios.