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Search results for: Qingbo Wu

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class="col-md-9 mx-auto"> <form method="get" action="https://publications.waset.org/search"> <div id="custom-search-input"> <div class="input-group"> <i class="fas fa-search"></i> <input type="text" class="search-query" name="q" placeholder="Author, Title, Abstract, Keywords" value="Qingbo Wu"> <input type="submit" class="btn_search" value="Search"> </div> </div> </form> </div> </div> <div class="row mt-3"> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Commenced</strong> in January 2007</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Frequency:</strong> Monthly</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Edition:</strong> International</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Paper Count:</strong> 2</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: Qingbo Wu</h1> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">2</span> FCNN-MR: A Parallel Instance Selection Method Based on Fast Condensed Nearest Neighbor Rule</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Lu%20Si">Lu Si</a>, <a href="https://publications.waset.org/search?q=Jie%20Yu"> Jie Yu</a>, <a href="https://publications.waset.org/search?q=Shasha%20Li"> Shasha Li</a>, <a href="https://publications.waset.org/search?q=Jun%20Ma"> Jun Ma</a>, <a href="https://publications.waset.org/search?q=Lei%20Luo"> Lei Luo</a>, <a href="https://publications.waset.org/search?q=Qingbo%20Wu"> Qingbo Wu</a>, <a href="https://publications.waset.org/search?q=Yongqi%20Ma"> Yongqi Ma</a>, <a href="https://publications.waset.org/search?q=Zhengji%20Liu"> Zhengji Liu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Instance selection (IS) technique is used to reduce the data size to improve the performance of data mining methods. Recently, to process very large data set, several proposed methods divide the training set into some disjoint subsets and apply IS algorithms independently to each subset. In this paper, we analyze the limitation of these methods and give our viewpoint about how to divide and conquer in IS procedure. Then, based on fast condensed nearest neighbor (FCNN) rule, we propose a large data sets instance selection method with MapReduce framework. Besides ensuring the prediction accuracy and reduction rate, it has two desirable properties: First, it reduces the work load in the aggregation node; Second and most important, it produces the same result with the sequential version, which other parallel methods cannot achieve. We evaluate the performance of FCNN-MR on one small data set and two large data sets. The experimental results show that it is effective and practical. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Instance%20selection" title="Instance selection">Instance selection</a>, <a href="https://publications.waset.org/search?q=data%20reduction" title=" data reduction"> data reduction</a>, <a href="https://publications.waset.org/search?q=MapReduce" title=" MapReduce"> MapReduce</a>, <a href="https://publications.waset.org/search?q=kNN." title=" kNN."> kNN.</a> </p> <a href="https://publications.waset.org/10007534/fcnn-mr-a-parallel-instance-selection-method-based-on-fast-condensed-nearest-neighbor-rule" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/10007534/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/10007534/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/10007534/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/10007534/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/10007534/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/10007534/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/10007534/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/10007534/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/10007534/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/10007534/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/10007534.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">1017</span> </span> </div> </div> <div class="card publication-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">1</span> An Improved Method on Static Binary Analysis to Enhance the Context-Sensitive CFI</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Qintao%20Shen">Qintao Shen</a>, <a href="https://publications.waset.org/search?q=Lei%20Luo"> Lei Luo</a>, <a href="https://publications.waset.org/search?q=Jun%20Ma"> Jun Ma</a>, <a href="https://publications.waset.org/search?q=Jie%20Yu"> Jie Yu</a>, <a href="https://publications.waset.org/search?q=Qingbo%20Wu"> Qingbo Wu</a>, <a href="https://publications.waset.org/search?q=Yongqi%20Ma"> Yongqi Ma</a>, <a href="https://publications.waset.org/search?q=Zhengji%20Liu"> Zhengji Liu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Control Flow Integrity (CFI) is one of the most promising technique to defend Code-Reuse Attacks (CRAs). Traditional CFI Systems and recent Context-Sensitive CFI use coarse control flow graphs (CFGs) to analyze whether the control flow hijack occurs, left vast space for attackers at indirect call-sites. Coarse CFGs make it difficult to decide which target to execute at indirect control-flow transfers, and weaken the existing CFI systems actually. It is an unsolved problem to extract CFGs precisely and perfectly from binaries now. In this paper, we present an algorithm to get a more precise CFG from binaries. Parameters are analyzed at indirect call-sites and functions firstly. By comparing counts of parameters prepared before call-sites and consumed by functions, targets of indirect calls are reduced. Then the control flow would be more constrained at indirect call-sites in runtime. Combined with CCFI, we implement our policy. Experimental results on some popular programs show that our approach is efficient. Further analysis show that it can mitigate COOP and other advanced attacks. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Contex-sensitive" title="Contex-sensitive">Contex-sensitive</a>, <a href="https://publications.waset.org/search?q=CFI" title=" CFI"> CFI</a>, <a href="https://publications.waset.org/search?q=binary%20analysis" title=" binary analysis"> binary analysis</a>, <a href="https://publications.waset.org/search?q=code%20reuse%0D%0Aattack." title=" code reuse attack."> code reuse attack.</a> </p> <a href="https://publications.waset.org/10007533/an-improved-method-on-static-binary-analysis-to-enhance-the-context-sensitive-cfi" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/10007533/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/10007533/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a 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