Yadong Zhou

Bio: Yadong Zhou is an academic researcher from Xi'an Jiaotong University. The author has contributed to research in topics: Computer science & Virtual currency. The author has an hindex of 12, co-authored 53 publications receiving 365 citations. Previous affiliations of Yadong Zhou include Beijing University of Posts and Telecommunications.

Exploiting the Vulnerability of Flow Table Overflow in Software-Defined Network: Attack Model, Evaluation, and Defense

Abstract: As the most competitive solution for next-generation network, SDN and its dominant implementation OpenFlow are attracting more and more interests. But besides convenience and flexibility, SDN/OpenFlow also introduces new kinds of limitations and security issues. Of these limitations, the most obvious and maybe the most neglected one is the flow table capacity of SDN/OpenFlow switches. In this paper, we proposed a novel inference attack targeting at SDN/OpenFlow network, which is motivated by the limited flow table capacities of SDN/OpenFlow switches and the following measurable network performance decrease resulting from frequent interactions between data and control plane when the flow table is full. To the best of our knowledge, this is the first proposed inference attack model of this kind for SDN/OpenFlow. We implemented an inference attack framework according to our model and examined its efficiency and accuracy. The evaluation results demonstrate that our framework can infer the network parameters (flow table capacity and usage) with an accuracy of 80% or higher. We also proposed two possible defense strategies for the discovered vulnerability, including routing aggregation algorithm and multilevel flow table architecture. These findings give us a deeper understanding of SDN/OpenFlow limitations and serve as guidelines to future improvements of SDN/OpenFlow.

Empirical analysis of the evolution of follower network: A case study on Douban

Abstract: Follower networks such as Twitter and Digg are becoming popular form of social information networks. This paper seeks to gain insights into how they evolve and the relationship between their structure and their ability to spread information. By studying the Douban follower network, which is a popular online social network in China, we provide some evidences showing its suitability for information spreading. For example, it exhibits an unbalanced bow-tie structure with a large out-component, which indicates that the majority of users can spread information widely; the effective diameter of the strongly connected component is shrinking as the user base grows, which facilitates spreading; and the transitivity property shows that people in a follower network tend to shorten the path of information flow, i.e., it takes fewer hops to spread information. Also, we observe the following users' behaviors, a user's following activity decays exponentially during her lifetime and the following behaviors differ according to the age of the account. These findings provide a deep understanding on the evolution of follower networks, and can provide guidelines on how to build an efficient information diffusion system.

Early detection method for emerging topics based on dynamic bayesian networks in micro-blogging networks

Abstract: We propose a new method for early detection of emerging topics in micro-blogging.We find two characteristics of emerging topic which influence topic diffusion.We build a new DBN-based model to represent the temporal evolution of keyword.Performance of our method leads one to two hours earlier than others. Micro-blogging networks have become the most influential online social networks in recent years, more and more people are used to obtain and diffuse information in them. Detecting topics from a great number of tweets in micro-blogging is important for information propagation and business marketing, especially detecting emerging topics in the early period could strongly support these real-time intelligent systems, such as real-time recommendation, ad-targeting, marketing strategy. However, most of previous researches are useful to detect emerging topic on a large scale, but they are not so effective for the early detection due to less informative properties in a relatively small size. To solve this problem, we propose a new early detection method for emerging topics based on Dynamic Bayesian Networks in micro-blogging networks. We first analyze the topic diffusion process and find two main characteristics of emerging topic which are attractiveness and key-node. Then based on this finding, we select features from the topology properties of topic diffusion, and build a DBN-based model by the conditional dependencies between features to identify the emerging keywords. An emerging keyword not only occurs in a given time period with frequency properties, but also diffuses with specific topology properties. Finally, we cluster the emerging keywords into emerging topics by the co-occurrence relations between keywords. Based on the real data of Sina micro-blogging, the experimental results demonstrate that our method is effective and capable of detecting the emerging topics one to two hours earlier than the other methods.

Random graphs

Abstract: We will review some of the major results in random graphs and some of the more challenging open problems. We will cover algorithmic and structural questions. We will touch on newer models, including those related to the WWW.

An Introduction to Functional Grammar的“集大成”地位

Abstract: An Introduction to Functional Grammar(《功能语法导论》,简称《导论》)是Halliday的代表作。胡壮麟教授称此书为集中体现Halliday语言学思想的"集大成者"。这一点反映在三个方面:一是他在伦敦学派的基础上发展来的、关于系统和功能语言观的大量前期论述(包括来自学派内部诸多追随者的研究),二是

Duplication Models for Biological Networks

Abstract: Are biological networks different from other large complex networks? Both large biological and non-biological networks exhibit power-law graphs (number of nodes with degree k, N(k) ~ k-b) yet the exponents, b, fall into different ranges. This may be because duplication of the information in the genome is a dominant evolutionary force in shaping biological networks (like gene regulatory networks and protein-protein interaction networks), and is fundamentally different from the mechanisms thought to dominate the growth of most non-biological networks (such as the internet [1-4]). The preferential choice models non-biological networks like web graphs can only produce power-law graphs with exponents greater than 2 [1-4,8]. We use combinatorial probabilistic methods to examine the evolution of graphs by duplication processes and derive exact analytical relationships between the exponent of the power law and the parameters of the model. Both full duplication of nodes (with all their connections) as well as partial duplication (with only some connections) are analyzed. We demonstrate that partial duplication can produce power-law graphs with exponents less than 2, consistent with current data on biological networks. The power-law exponent for large graphs depends only on the growth process, not on the starting graph.

Early semantic Networks: Preferential Attachment or Preferential Acquisition?

Abstract: Analyses of adult semantic networks suggest a learning mechanism involving preferential attachment: A word is more likely to enter the lexicon the more connected the known words to which it is related. We introduce and test two alternative growth principles: preferential acquisition—words enter the lexicon not because they are related to well-connected words, but because they connect well to other words in the learning environment—and the lure of the associates—new words are favored in proportion to their connections with known words. We tested these alternative principles using longitudinal analyses of developing networks of 130 nouns children learn prior to the age of 30 months. We tested both networks with links between words represented by features and networks with links represented by associations. The feature networks did not predict age of acquisition using any growth model. The associative networks grew by preferential acquisition, with the best model incorporating word frequency, number of phonological neighbors, and connectedness of the new word to words in the learning environment, as operationalized by connectedness to words typically acquired by the age of 30 months.