Kun Qiu

Kun Qiu, Ying Wang, Baoqian Li et al.

Traffic classification, a technique for assigning network flows to predefined categories, has been widely deployed in enterprise and carrier networks. With the massive adoption of mobile devices, encryption is increasingly used in mobile applications to address privacy concerns. Consequently, traditional methods such as Deep Packet Inspection (DPI) fail to distinguish encrypted traffic. To tackle this challenge, Artificial Intelligence (AI), in particular Machine Learning (ML), has emerged as a promising solution for encrypted traffic classification. A crucial prerequisite for any ML-based approach is traffic data cleaning, which removes flows that are not useful for training (e.g., irrelevant protocols, background activity, control-plane messages, and long-lived sessions). Existing cleaning solutions depend on manual inspection of every captured packet, making the process both costly and time-consuming. In this poster, we present an unsupervised framework that automatically cleans encrypted mobile traffic. Evaluation on real-world datasets shows that our framework incurs only a 2%~2.5% reduction in classification accuracy compared with manual cleaning. These results demonstrate that our method offers an efficient and effective preprocessing step for ML-based encrypted traffic classification.

Tsong Yueh Chen, Pak-Lok Poon, Kun Qiu et al.

Training multiple-layered deep neural networks (DNNs) is difficult. The standard practice of using a large number of samples for training often does not improve the performance of a DNN to a satisfactory level. Thus, a systematic training approach is needed. To address this need, we introduce an innovative approach of using metamorphic relations (MRs) as "knowledge carriers" to train DNNs. Based on the concept of metamorphic testing and MRs (which play the role of a test oracle in software testing), we make use of the notion of metamorphic group of inputs as concrete instances of MRs (which are abstractions of knowledge) to train a DNN in a systematic and effective manner. To verify the viability of our training approach, we have conducted a preliminary experiment to compare the performance of two DNNs: one trained with MRs and the other trained without MRs. We found that the DNN trained with MRs has delivered a better performance, thereby confirming that our approach of using MRs as knowledge carriers to train DNNs is promising. More work and studies, however, are needed to solidify and leverage this approach to generate widespread impact on effective DNN training.