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Clickstream ppt copy

  1. 1. JSPM’s RAJARSHI SHAHU COLLEGE OF ENGG. PUNE-33 DEPARTMENT OF COMPUTER ENGG. BE Computer Engineering Preliminary Project Presentation 2013-14 1
  2. 2. Identifying Fraudulent Activities Over Online Application Through Clickstream Analysis Group No.: 08 Exam Seat No. Name of Student B80374244 Nikita Hiremath B80374265 Surbhi Sonkhaskar B80374270 Shital More Guided by:-Ms. V.M.Barkade 2
  3. 3. Introduction • Internet has been integrated in day to day activities of human beings. • Frauds followed with the advent of e-commerce. • It is necessary for all the online applications involving monetary transactions to ensure the safety of money being invested by people. • Click stream analysis is one such technique which helps in detecting frauds by analyzing the user behavior. 3
  4. 4. Problem Statement To Develop a Business Solution for Identifying the Fraudulent Activities on Online Application through Click Stream Analysis using Hadoop. 4
  5. 5. Scope 1. 2. 3. 4. 5. Detecting only order frauds. Limited to only detecting the fraudulent user. No recovery measure Analysis will be done only for a particular session of an user There is no restriction over number of clicks or for time stamp. 5
  6. 6. Literature Survey • Paper 1: Wichian Premchaiswadi;Walisa Romsaiyud,” Extracting WebLog of Siam University for Learning User Behavior on MapReduce”,Siam University,Thailand.(ICIAS2012) • Paper 2: Narayanan Sadgopan;Jie Li,”Characterizing Typical and Atypical User Sessions in Clickstreams”Yahoo!.WWW2008 • Paper 3: Bimal Viswanath;Ansley Post;Krishna P.Gummadi;Alan Mislove,”An Analysis of Social Network-based Sybil Defenses”,MPI-SWS and North Eastern University .SIGCOMM’10. 6
  7. 7. Literature Survey Paper 1 Abstract: MapReduce is a framework that allows developers to write applications that rapidly process and analyze large volumes of data in a massively parallel scale.Moreover, a clickstream is a record of a user's activity on the Internet. Using a clickstream analysis we can collect, analyze, and report aggregate data about which pages visitors visit in what order – and which are the result of the succession of mouse clicks each visitor makes. Clickstream analysis can reveal usage patterns leading to a heightened understanding of users’ behavior. In this paper, we introduced a novel and efficient web log mining model for web users clustering. In general, our model consists of three main steps; 1) Computing the similarity measure of any path in a web page, 2) Defining the k-mean clustering for group customerID 3) Generating the report based on the Hadoop MapReduce Framework. Consequently.Our experiments were run on real world data derived from weblogs of SiamUniversity at Bangkok, Thailand (www.siam.edu). 7
  8. 8. In this paper they have proposed: The paper has suggested how two algorithms: Calculate the similarity of the graph and Fuzzy K-mean clustering can be used to analyze the user behavior using click stream. These algorithm use graphs and data set as input respectively. From this paper we have referred: An already existing systems’ study that has used Clickstream analysis for studying the behavior of the user over an educational website. 8
  9. 9. Paper 2 Abstract: Millions of users retrieve information from the Internet using search engines. Mining these user sessions can provide valuable information about the quality of user experience and the perceived quality of search results. Often search engines rely on accurate estimates of Click Through Rate (CTR) to evaluate the quality of user experience. The vast heterogeneity in the user population and presence of automated software programs (bots) can result in high variance in the estimates of CTR. To improve the estimation accuracy of user experience metrics like CTR, we argue that it is important to identify typical and atypical user sessions in clickstreams. Our approach to identify these sessions is based on detecting outliers using Mahalanobis distance in the user session space. Our user session model incorporates several key clickstream characteristics including a novel conformance score obtained by Markov Chain analysis. Editorial results show that our approach of identifying typical and atypical sessions has a precision of about 89%. Filtering out these atypical sessions reduces the uncertainty (95% confidence interval) of the mean CTR by about 40%. These results demonstrate that our approach of identifying typical and atypical user sessions is extremely valuable for cleaning “noisy" user session data for increased accuracy in evaluating user experience. 9
  10. 10. In this paper they have proposed: Use of Markov Chain analysis to improve the detection of typical and atypical user sessions. Also they have used Click Through Rate(CTR) to evaluate the quality of users. From this paper we have referred: From this paper we referred to various techniques for analyzing typical and atypical users depending on the clicks made by the user. It has suggested few models like Click-based model, Time-based model and Hybrid model, using which the sessions can be divide and analyzed. The concept of Click Through Rate is referred from this paper. 10
  11. 11. Paper 3 Abstract: Recently, there has been much excitement in the research community over using social networks to mitigate multiple identity, or Sybil, attacks. A number of schemes have been proposed, but they differ greatly in the algorithms they use and in the networks upon which they are evaluated. As a result, the research community lacks a clear understanding of how these schemes compare against each other, how well they would work on real-world social networks with different structural properties, or whether there exist other (potentially better) ways of Sybil defense. In this paper, we show that, despite their considerable differences, existing Sybil defense schemes work by detecting local communities (i.e., clusters of nodes more tightly knit than the rest of the graph) around a trusted node. Our finding has important implications for both existing and future designs of Sybil defense schemes. First, we show that there is an opportunity to leverage the substantial amount of prior work on general community detection algorithms in order to defend against Sybils. Second, our analysis reveals the fundamental limits of current social network-based Sybil defenses: We demonstrate that networks with well-defined community structure are inherently more vulnerable to Sybil attacks, and that, in such networks, Sybils can carefully target their links in order make their attacks more effective. 11
  12. 12. In this paper they have proposed: An analysis of Sybil attacks on social networking sites has been given. They have given how even a well structured site can be targeted for such attacks. From this paper we referred: In this paper we got more information about Sybil Attacks over online social network. We got an understanding that Sybil attacks over an online shopping website cannot completely block the site. But partial Sybil attack can be done through order frauds. 12
  13. 13. Requirement Analysis Software Requirement • • • • Hardware Requirements Shell Script Apache Hadoop 0.20.x Pig Script 0.9.1 Ubuntu 12.04 • Processor :Intel Pentium IV 2.1 GHz or above • Clock speed:500 MHz • RAM:128MB • HD:20 GB or higher 13
  14. 14. Proposed System Data gathering Extraction of weblogs Storing and structuring data Pattern matching and map reduce algorithm Data analysis HDFS Data visualization 14
  15. 15. SYSTEM DIAGRAMS • • • • • • • • • • • Class diagram State Transition Diagram System Architecture Diagram Use case diagram Activity Diagram Object Diagram Sequence Diagram Collaboration Diagram State chart Diagram Component Diagram Deployment Diagram 15
  16. 16. WORKING OF THE SYSTEM USER 1 USER 2 USER 3 Extracting Weblogs FLUME AGENT 16
  17. 17. FLUME AGENT PROVIDING WEBLOGS SERVER STORING AND STRUCTURING DATA HDFS 17
  18. 18. HDFS DATA NODES PROVIDING THE MATCHED VALUES PATTERN MATCHING ALGORITHM 18
  19. 19. DATA ANALYSIS HDFS PROVIDING PROCESSED DATA DATA VISUALIZATION USING DATA ANALYTICS TOOLS SERVER 6 4 Se 2 0 rie s 1 10 5 0 19
  20. 20. Algorithms The various pattern matching algorithm that can be applied are:1. 2. 3. 4. Brute force algorithm Boyer Moore algorithm Not so naïve algorithm Knuth-Morris-Pratt algorithm Out of all the above listed algorithms, we are going to use the KnuthMorris-Pratt algorithm since it is most efficient algorithm for matching short as well as long patterns. 20
  21. 21. MATHEMATICAL MODEL Bernoulli’s Distribution: This distribution best describes all situations where a "trial" is made resulting in either "success" or "failure," such as when tossing a coin, or when modeling the success or failure of a surgical procedure. The Bernoulli distribution is defined as: f(x) = px (1-p)1-x, for x = 0, where, p is the probability that a particular event (e.g.,success) will occur Arithmetic Mean: The arithmetic mean of a set of data is found by taking the sum of the data, and then dividing the sum by the total number of values in the set. A mean is commonly referred to as an average. n/sum(n) where n is total number of elements Arithmetic Mode: Mode is a most frequently occurring value in frequency distribution. 21
  22. 22. Arithmetic Median: Median is the “middle number” value in number. Variance: The variance (σ2), is defined as the sum of the squared distances of each term in the distribution from the mean (μ), divided by the number of terms in the distribution (N). 22
  23. 23. Future Scope • This system can be implemented for any online commercial application. • Currently only detection of fraudulent users is being done, the system can be expanded to undertake the necessary authentication steps. 23
  24. 24. References 1. SADAGOPAN, N., AND LI, J. Characterizing typical and atypical user sessions in clickstreams. In Proc. of WWW(2008). 2. You are How You Click: Clickstream Analysis for Sybil Detection Gang Wang, Tristan Konolige, Christo Wilson, Xiao Wang, Haitao Zheng and Ben Y. Zhao. 3. Wichian Premchaiswadi, Walisa Romsaiyud Extracting WebLog of Siam University for Learning User Behavior on MapReduce. 4. YU, H., KAMINSKY, M., GIBBONS, P. B., AND FLAXMAN,A. Sybilguard: defending against sybil attacks via social networks. In Proc. of SIGCOMM (2006). 5. DOUCEUR, J. R. The Sybil attack. In Proc. of IPTPS(2002). 24
  25. 25. THANK YOU! 25

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