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Browsing by Author "Tian, Ke"

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    CS5604 Fall 2016 Solr Team Project Report
    Li, Liuqing; Pillai, Anusha; Wang, Ye; Tian, Ke (Virginia Tech, 2016-12-07)
    This submission describes the work the SOLR team completed in Fall 2016. It includes the final report and presentation, as well as key relevant materials (indexing scripts & Java code). Based on the work in Spring 2016, the SOLR team improved the general search infrastructure supporting the IDEAL and GETAR projects, both funded by NSF. The main responsibility was to configure the Basic Indexing and Incremental Indexing (Near Real Time, NRT Indexing) for tweets and web page collections in DLRL's Hadoop Cluster. The goal of Basic Indexing was to index the big collection that contains more than 1.2 billion tweets. The idea of NRT Indexing was to monitor real-time changes in HBase and update the Solr results as appropriate. The main motivation behind the Custom Ranking was to design and implement a new scoring function to re-rank the retrieved results in Solr. Based on the text similarity, a basic document recommender was also created to retrieve the similar documents related to a specific one. Finally, new well written manuals could be easier for users and developers to read and get familiar with Solr and its relevant tools. Throughout the semester we closely collaborated with the Collection Management Tweets (CMT), Collection Management Webpages (CMW), Classification (CLA), Clustering and Topic Analysis (CTA), and Front-End (FE) teams in getting requirements, input data, and suggestions for data visualization.
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    Learning-based Cyber Security Analysis and Binary Customization for Security
    Tian, Ke (Virginia Tech, 2018-09-13)
    This thesis presents machine-learning based malware detection and post-detection rewriting techniques for mobile and web security problems. In mobile malware detection, we focus on detecting repackaged mobile malware. We design and demonstrate an Android repackaged malware detection technique based on code heterogeneity analysis. In post-detection rewriting, we aim at enhancing app security with bytecode rewriting. We describe how flow- and sink-based risk prioritization improves the rewriting scalability. We build an interface prototype with natural language processing, in order to customize apps according to natural language inputs. In web malware detection for Iframe injection, we present a tag-level detection system that aims to detect the injection of malicious Iframes for both online and offline cases. Our system detects malicious iframe by combining selective multi-execution and machine learning algorithms. We design multiple contextual features, considering Iframe style, destination and context properties.