D. Du

TL;DR: Wang et al. as discussed by the authors proposed a reliable encrypted traffic classification framework by only using the flow header called Only Header, which avoids privacy risks and achieves lightweight storage, and they introduced a twice segmentation mechanism to dilute the interference traffic and increase the weight of effective traffic.

TL;DR: This study proposes a comprehensive framework for insider threat detection, integrating statistical and sequential analysis using a neural network-based approach, CATE, to effectively learn and model user behavior, improving detection accuracy and robustness.

TL;DR: This paper proposes a gradient boosting decision tree based method to train and predict the cost of forwarding contract by following the sequence of machine learning steps which consist of data analysis, feature engineering and model construction.

TL;DR: This paper presents a fast gradient boosting based approach to this problem consisting of data analysis, feature engineering, and model construction, and achieves an AUC score of 0.8008 and takes 156 seconds for 10-fold cross-validation, demonstrating its effectiveness and efficiency.