Conventional ad blocking and tracking prevention tools often fall short in addressing web content manipulation. Machine learning approaches have been proposed to enhance detection accuracy, yet aspects of practical deployment have frequently been overlooked. This paper introduces AdFlush, a novel machine learning model for real-world browsers. To develop AdFlush, we evaluated the effectiveness of 883 features, ultimately selecting 27 key features for optimal performance. We tested AdFlush on a dataset of 10,000 real-world websites, achieving an F1 score of 0.98, thereby outperforming AdGraph (F1 score: 0.93), WebGraph (F1 score: 0.90), and WTAgraph (F1 score: 0.84). Additionally, AdFlush significantly reduces computational overhead, requiring 56% less CPU and 80% less memory than AdGraph. We also assessed AdFlush's robustness against adversarial manipulations, demonstrating superior resilience with F1 scores ranging from 0.89 to 0.98, surpassing the performance of AdGraph and WebGraph, which recorded F1 scores between 0.81 and 0.87. A six-month longitudinal study confirmed that AdFlush maintains a high F1 score above 0.97 without the need for retraining, underscoring its effectiveness.

Decompilation is frequently used to analyze binary programs. In Android, however, decompilers all perform differently with varying apps due to their own characteristics. Obviously, there is no universal solution in all conditions. Based on this observation, we present a practical Android app decompilation system (called Kerberoid) that automatically stitches the results from multiple decompilers together to maximize the coverage and the accuracy of decompiled codes. We evaluate the performance of Kerberoid with 151 Android apps in which their corresponding source codes are publicly available. Kerberoid fully recovered all functions for 17% of the apps tested and gained a similarity score over 50% for 40% of the apps tested, increased by 7% and 9%, respectively, compared with the best existing decompiler.