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Information-Centric Networking (ICN) is proposed to address the inefficiency of content delivery of IP networks from the perspective of architecture. In contrast, Content Delivery Network (CDN) is an overlay solution in current IP networks. We believe that even though ICN is fully deployed, there is still a role for CDNs to play in ICN networks. Since ISPs in ICN will replicate and forward contents...
The recent years have seen extensive work on statistics-based network traffic classification using machine learning (ML) techniques. In the particular scenario of learning from unlabeled traffic data, some classic unsupervised clustering algorithms (e.g. K-Means and EM) have been applied but the reported results are unsatisfactory in terms of low accuracy. This paper presents a novel approach for...
Most network applications nowadays are related to the distribution of contents from a source to a number of users. Several developments such as CDN and P2P networking have been used to provide access to contents at the network edges. However, they have no ability to leverage caching at the network level and still rely on a host-based communication model. Recently, Content Centric Networking (CCN)...
In this article, an NAT-compatibility testbed is designed to automatically conduct the repeated experiments, collect the test results, and verify the direct connection rate (DCR) of any NAT traversal programs. Achieving a high DCR is important because using relays may unnecessarily increase the bandwidth cost, processing load of the relay servers, and the end-to-end packet delay. The NAT-compatibility...
Research, Development and Evaluation Commission once hold an Internet election of “Top Ten Complaints”, the result showed that the overabundance of phone and Internet fraud was included. This result represents that the phone and Internet fraud trouble people a lot. Thus, this paper will first discuss the analysis of the crime mode of phone and Internet fraud and the collect the security threats of...
A number of recent works have proposed using data mining and machine learning techniques to classify traffic flows based on statistical flow characteristics. Most of these classifiers work offline, since full-flow statistics are not available until a flow is finished. Therefore, it is usually too late to take actions for online deployment. In this paper, we propose a simple and effective technique...
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