The proliferation of mobile devices over recent years has led to a dramatic increase in mobile traffic. Demand for enabling accurate mobile app identification is coming as it is an essential step to improve a multitude of network services: accounting, security monitoring, traffic forecasting, and quality-of-service. However, traditional traffic classification techniques do not work well for mobile traffic. Besides, multiple machine learning solutions developed in this field are severely restricted by their handcrafted features as well as unreliable datasets. In this paper, we propose a framework for real network traffic collection and labeling in a scalable way. A dedicated Android traffic capture tool is developed to build datasets with perfect ground truth. Using our established dataset, we make an empirical exploration on deep learning methods for the task of mobile app identification, which can automate the feature engineering process in an end-to-end fashion. We introduce three of the most representative deep learning models and design and evaluate our dedicated classifiers, namely, a SDAE, a 1D CNN, and a bidirectional LSTM network, respectively. In comparison with two other baseline solutions, our CNN and RNN models with raw traffic inputs are capable of achieving state-of-the-art results regardless of TLS encryption. Specifically, the 1D CNN classifier obtains the best performance with an accuracy of 91.8% and macroaverage F-measure of 90.1%. To further understand the trained model, sample-specific interpretations are performed, showing how it can automatically learn important and advanced features from the uppermost bytes of an app’s raw flows.

With Internet access shifting from desktop-driven to mobile-driven, application-level mobile traffic identification has become a research hotspot. Although considerable progress has been made in this research field, two obstacles are hindering its further development. Firstly, there is a lack of sharable labeled mobile traffic datasets. Although it is easy to capture mobile traffic, labeling traffic at the application level is non-trivial. Besides, researchers usually hold a conservative attitude toward publishing their datasets for privacy concerns. Secondly, most of the datasets used by existing studies are inadequate to evaluate the proposed methods, since they usually have the problems of inaccurate labels, small scale and simple collection configurations. To tackle these two obstacles, a mobile traffic collection is carried out in this paper. The collected traffic has the advantages of large-scale data size, accurate application-level labels and diverse collection configurations. Then, the collected traffic is anonymized carefully to make it public. Several mobile traffic identification methods are compared based on our anonymized dataset, which proves the applicability of our dataset.

Regular expression matching engine is a crucial infrastructure which is widely used in network security systems, like IDS. We propose Gregex, a Graphics Processing Unit (GPU) based regular expression matching engine for deep packet inspection (DPI). Gregex leverages the computational power and high memory bandwidth of GPUs by storing data in proper GPU memory space and executing massive GPU thread concurrently to process lots of packets in parallel. Three optimization techniques, ATP, CAB, and CAT are proposed to significantly improve the performance of Gregex. On a GTX260 GPU, Gregex achieves a regular matching throughput of 126.8 Gbps, which is a speedup of 210× over traditional CPU-based implementation and a speedup of 7.9× over the state-of-the-art GPU based regular expression engine.

Packet classification plays a crucial role in various network functions such as access control and routing. In recent years, the rapid development of SDN and NFV poses new challenges for packet classification to support fast rule-set updates as introducing strong dynamics for the structure of networks. To this end, this paper proposes a novel scheme, TupleTree, to perform high-speed packet classification while providing fast rule-set update ability. TupleTree is a hybrid scheme combining decision tree and tuple space. In TupleTree, it organizes rules in a decision tree-like structure, but distributes rules in each node into child nodes through hashing rather than cutting or splitting. With the decision tree structure, for each classification, one leaf node containing a few rules can be rapidly indexed. Hence, a high classification performance can be achieved. Meanwhile, with hashing instead of cutting or splitting, it is easy to support fast rule-set updates due to having avoided the rule replication problem. Compared to state-of-the-art schemes that support fast rule-set updates, experimental results show that our proposed scheme achieves a classification performance improvement of 85% to 237% while retaining close update performance for large rule-sets.