Software switches are being deployed in SDN to enable a wide spectrum of non-traditional applications. The popular Open vSwitch uses a variant of Tuple Space Search (TSS) for packet classifications. Although it has good performance on rule updates, it is less efficient than decision trees on lookups. In this paper, we propose a two-stage framework consisting of heterogeneous algorithms to adaptively exploit different characteristics of the rule sets at different scales. In the first stage, partial decision trees are constructed from several rule subsets grouped with respect to their small fields. This grouping eliminates rule replications at large scales, thereby enabling very efficient precuttings. The second stage handles packet classification at small scales for non-leaf terminal nodes, where rule replications within each subspace may lead to inefficient cuttings. A salient fact is that small space means long address prefixes or less nesting levels of ranges, both indicating a very limited tuple space. To exploit this favorable property, we employ a TSS-based algorithm for these subsets following tree constructions. Experimental results show that our work has comparable update performance to TSS in Open vSwitch, while achieving almost an order-of-magnitude improvement on classification performance over TSS.

In this letter we propose Meta-key, a data-sharing mechanism that enables users share their encrypted data under a blockchain-based decentralized storage architecture. All the data-encryption keys are encrypted by the owner’s public key and put onto the blockchain for safe and secure storage and easy key-management. Encrypted data are stored in dedicated storage nodes and proxy re-encryption mechanism is used to ensure secure data-sharing in the untrusted environment. Security analysis of our model shows that the proxy re-encryption adopted in our system is naturally free from collusion-attack due to the specific architecture of Meta-key.

For machine-learning-based network traffic classification, we usually need large number of correctly labeled samples (ground truth) for model-training to get high accuracy. However in practical environments obtaining ground truth may be time-consuming and needs massive manual work, so achieving higher accuracy at low labeling rate is still a tricky problem. In this paper we propose a novel Graph Convolutional Network (GCN) based network traffic classification method named GCN-TC. We combine the traffic trace graph with statistical features in GCN model-training, so as to take advantage of both of them to achieve higher classification accuracy with very few labeled data. Experiment results show that the proposed model achieves the best performance with 13% higher in Fl-score and 7.7% higher in accuracy than the best method of the rest.