California State University – Sacramento
Human or bot.
Degree: MS, Computer Science, 2017, California State University – Sacramento
Online auctions have become an increasingly important aspect of e-commerce. The
major problem with online bidding/auctioning is that there is no way of identifying if
the user is a human or a bot. Human bidders are becoming increasingly frustrated with
their inability to win auctions against their software-controlled counterparts. Bidding
robots such as ???Ez Sniper??? and ???Auction Sniper??? are pieces of software that are
configured by users to follow any number of auctions on different auction sites
simultaneously, bidding in place of the user according to predefined settings and
preferences. Humans are not able to attend to and monitor auctions with the same
capacity as a running program, which can make complex bidding decisions in splitsecond
time and can follow an auction with nonstop, undivided attention. As a result,
usage from the site's core customer base is plummeting. To rebuild customer happiness,
the auction service providers need to eliminate computer generated bidding from their
This project aims to address this problem by providing a solution to identify bids that
are placed by bidding bots. The identification is achieved by using machine learning techniques, such as Random Forest and Decision Tree, and uses several key features
extracted from online bidding data, such as time, country, IP address, etc.
The application provides three major functionalities as follows, CreateTrainingData,
CreateModel, and Identify. In the absence of existing training data, CreateTrainingData
creates training data with the help of the built-in model. The created data is to be
verified. Once done so it would be used in the CreateModel function. CreateModel
function creates a model based on the existing/created training data and its extracted
features. The created model would be used for the identification process. The Identify
function identifies the user/set of users whom uses bidding bots based on the model
The application also provides some security protection by preventing SQL injection
attacks. The 1st order and 2nd order SQL injection attacks are detected and prevented
from execution. The application can receive input values and SQL statement as JSON
value. The input values are first checked for special character if found, then the
received SQL statement is split on input values and checked for SQL injection
Advisors/Committee Members: Cheng, Yuan.
Subjects/Keywords: Apache Spark; Machine learning; Bidding robots
to Zotero / EndNote / Reference
APA (6th Edition):
Srinivasan, S. (2017). Human or bot. (Masters Thesis). California State University – Sacramento. Retrieved from http://hdl.handle.net/10211.3/198808
Chicago Manual of Style (16th Edition):
Srinivasan, Srivats. “Human or bot.” 2017. Masters Thesis, California State University – Sacramento. Accessed November 13, 2019.
MLA Handbook (7th Edition):
Srinivasan, Srivats. “Human or bot.” 2017. Web. 13 Nov 2019.
Srinivasan S. Human or bot. [Internet] [Masters thesis]. California State University – Sacramento; 2017. [cited 2019 Nov 13].
Available from: http://hdl.handle.net/10211.3/198808.
Council of Science Editors:
Srinivasan S. Human or bot. [Masters Thesis]. California State University – Sacramento; 2017. Available from: http://hdl.handle.net/10211.3/198808