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Browsing by Author "Ruan, Yefeng"
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Item Receipt-Freeness and Coercion Resistance in Remote E-Voting Systems(Inderscience, 2017) Ruan, Yefeng; Zou, Xukai; Computer and Information Science, School of ScienceAbstract: Remote electronic voting (E-voting) is a more convenient and efficient methodology when compared with traditional voting systems. It allows voters to vote for candidates remotely, however, remote E-voting systems have not yet been widely deployed in practical elections due to several potential security issues, such as vote-privacy, robustness and verifiability. Attackers' targets can be either voting machines or voters. In this paper, we mainly focus on three important security properties related to voters: receipt-freeness, vote-selling resistance, and voter-coercion resistance. In such scenarios, voters are willing or forced to cooperate with attackers. We provide a survey of existing remote E-voting systems, to see whether or not they are able to satisfy these three properties to avoid corresponding attacks. Furthermore, we identify and summarise what mechanisms they use in order to satisfy these three security properties.Item Survey of Return-Oriented Programming Defense Mechanisms(Wiley, 2016-07) Ruan, Yefeng; Kalyanasundaram, Sivapriya; Zou, Xukai; Department of Computer & Information Science, School of ScienceA prominent software security violation-buffer overflow attack has taken various forms and poses serious threats until today. One such vulnerability is return-oriented programming attack. An return-oriented programming attack circumvents the dynamic execution prevention, which is employed in modern operating systems to prevent execution of data segments, and attempts to execute unintended instructions by overwriting the stack exploiting the buffer overflow vulnerability. Numerous defense mechanisms have been proposed in the past few years to mitigate/prevent the attack – compile time methods that add checking logic to the program code before compilation, dynamic methods that monitor the control-flow integrity during execution and randomization methods that aim at randomizing instruction locations. This paper discusses (i) these different static, dynamic, and randomization techniques proposed recently and (ii) compares the techniques based on their effectiveness and performances.Item Using Twitter trust network for stock market analysis(Elsevier, 2018-04) Ruan, Yefeng; Durresi, Arjan; Alfantoukh, Lina; Computer and Information Science, School of ScienceOnline social networks are now attracting a lot of attention not only from their users but also from researchers in various fields. Many researchers believe that the public mood or sentiment expressed in social media is related to financial markets. We propose to use trust among users as a filtering and amplifying mechanism for the social media to increase its correlation with financial data in the stock market. Therefore, we used the real stock market data as ground truth for our trust management system. We collected stock-related data (tweets) from Twitter, which is a very popular Micro-blogging forum, to see the correlation between the Twitter sentiment valence and abnormal stock returns for eight firms in the S&P 500. We developed a trust management framework to build a user-to-user trust network for Twitter users. Compared with existing works, in addition to analyzing and accumulating tweets’ sentiment, we take into account the source of tweets – their authors. Authors are differentiated by their power or reputation in the whole community, where power is determined by the user-to-user trust network. To validate our trust management system, we did the Pearson correlation test for an eight months period (the trading days from 01/01/2015 through 08/31/2015). Compared with treating all the authors equally important, or weighting them by their number of followers, our trust network based reputation mechanism can amplify the correlation between a specific firm’s Twitter sentiment valence and the firm’s stock abnormal returns. To further consider the possible auto-correlation property of abnormal stock returns, we constructed a linear regression model, which includes historical stock abnormal returns, to test the relation between the Twitter sentiment valence and abnormal stock returns. Again, our results showed that by using our trust network power based method to weight tweets, Twitter sentiment valence reflect abnormal stock returns better than treating all the authors equally important or weighting them by their number of followers.