Analysis of Bayesian classification-based approaches for Android malware detection

摘要: Mobile malware has been growing in scale and complexity spurred by the unabated uptake of smartphones worldwide. Android is fast becoming most popular mobile platform resulting sharp increase targeting platform. Additionally, evolving rapidly to evade detection traditional signature-based scanning. Despite current measures place, timely discovery new still a critical issue. This calls for novel approaches mitigate threat zero-day malware. Hence, authors develop analyse proactive machine-learning based on Bayesian classification aimed at uncovering unknown via static analysis. The study, which large sample set majority existing families, demonstrates capabilities with high accuracy. Empirical results comparative analysis are presented offering useful insight towards development effective static-analytic classification-based solutions detecting