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.

Due to the proliferation of mobile applications, mobile traffic identification plays a crucial role in understanding the network traffic. However, the pervasive unconcerned apps and the emerging apps pose great challenges to the mobile traffic identification method based on supervised machine learning, since such method merely identifies and discriminates several apps of interest. In this paper we propose a three-layer classifier using machine learning to identify mobile traffic in open-world settings. The proposed method has the capability of identifying traffic generated by unconcerned apps and zero-day apps; thus it can be applied in the real world. A self-collected dataset that contains 160 apps is used to validate the proposed method. The experimental results show that our classifier achieves over 98% precision and produces a much smaller number of false positives than that of the state of the art.

The popularity of mobile locate-enabled devices and Location Based Service (LBS) generates massive spatio-temporal data every day. Due to the close relationship between behavior patterns and movement trajectory, trajectory data mining has been applied in numerous fields to find the behavior pattern. Among them, discovering traveling companions is one of the most fundamental techniques in these areas. This paper proposes a flexible framework named GroupSeeker for discovering traveling companions in vast real-world trajectory data. In the real-world data resource, it is significant to avoid the companion candidate omitting problem happening in the time-snapshot-slicing-based method. These methods do not work well with the sparse real-world data, which is caused by the equipment sampling failure or manual intervention. In this paper, a 5-stage framework including Data Preprocessing, Spatio-temporal Clustering, Candidate Voting, Pseudo-companion Filtering, and Group Merging is proposed to discover traveling companions. The framework even works well when there is a long time span during several days. The experiments result on two real-world data sources which offer massive amount of data subsets with different scale and different sampling frequencies show the effective and robustness of this framework. Besides, the proposed framework has a higher-efficiency performing when discovering satisfying companions over a long-term period.