VTU FINAL YEAR PROJECT REPORT Front pages

VISVESVARAYA TECHNOLOGICAL UNIVERSITY
BELAGAVI-590018
A Project Report On
“COLLECTIVE HYPING DETECTION SYSTEM TO
IDENTIFY ONLINE SPAM ACTIVITIES USING AI”
Submitted in the partial fulfilment of the requirements for the award of the Degree of
Bachelor of Engineering in Computer Science and Engineering
Submitted by
Enter name here (enter usn here)
Enter usn here (enter usn here)
Enter name here (enter usn here)
Under the support and guidance of
Ms. Florance G.
Assistant Professor,
Department of CSE
Department of Computer Science and Engineering
The Oxford College of Engineering
Hosur Road, Bommanahalli, Bangalore-560068
(Approved by AICTE, New Delhi, Accredited by NBA, NAAC, New Delhi & Affiliated to VTU, Belagavi)
2017-2018

DEPARTMENT OF COMPUTER SCIENCE AND ENGINEERING
THE OXFORD COLLEGE OF ENGINEERING
CERTIFICATE
Certified that the project entitled “COLLECTIVE HYPING DETECTION SYSTEM
TO IDENTIFY ONLINE SPAM ACTIVITIES USING AI” carried out by ENTER
NAME HERE (10X14CS004), ENTER USN HERE (enter usn here), ENTER U
HERE WADHWA (enter usn here), KARAN AGARWAL (1OX14CS037) bonafide
student of The Oxford College of Engineering, Bangalore in partial fulfilment for the
award of the Degree of Bachelor of Engineering in Computer Science and
Engineering of Visvesvaraya Technological University, Belagavi during the year
2017-2018. The project report has been approved as it satisfies the academic requirements
in respect of project work prescribed for the said degree.
Ms. Florance G. Dr. R.J. Anandhi Dr R.V Praveena Gowda
Project Guide Vice Principal & H.O.D. Principal
External Viva
Name of Examiner Signature with date
1. 1.
2. 2.

Department Vision
To establish the department as a renowned center of excellence in the area of
scientific education, research with industrial guidance, and exploration of the
latest advances in the rapidly changing field of computer science.
Department Mission
To produce technocrats with creative technical knowledge and intellectual
skills to sustain and excel in highly demanding world with confidence.

DECLARATION
We, the students of Eighth semester B.E, at the Department of Computer
Science and Engineering, The Oxford College of Engineering, Bangalore
declare that the Project entitled “COLLECTIVE HYPING DETECTION
SYSTEM TO IDENTIFY ONLINE SPAM ACTIVITIES USING AI” has been
presented by us and submitted in partial fulfilment of course requirements
for the award of degree in Bachelor of Engineering in Computer Science and
Engineering discipline of Visvesvaraya Technological University, Belagavi
during the academic year 2017-2018.
Enter name here
Enter usn here
Enter u here Wadhwa
Karan Agarwal
Place: Bangalore
Date:

ACKNOWLEDGEMENT
The satisfaction and euphoria that accompany the successful completion of any
task would be incomplete without the mention of the people who made it possible, whose
constant guidance and encouragement crowned our efforts with success.
I have great pleasure in expressing my gratitude to my respected founder chairman
late Shri S. Narasa Raju and to the respected chairman Shri S.N.V.L Narasimha Raju
for having provided me with great infrastructure and well-furnished labs.
I take this opportunity to express my profound gratitude to our respected Principal
Dr. R V Praveena Gowda for his constant support and encouragement.
I am grateful to the Vice Principal and Head of the Department Dr. R. J.
Anandhi, Department of CSE, for her unfailing encouragement and suggestion given to
me in the course of my Seminar work.
Guidance and deadlines play a very important role in successful completion of the
seminar report on time. I also convey my gratitude to Ms. Florance G., Assistant
Professor, Department of CSE, for having constantly monitored the development of the
project report and setting up precise deadlines.
Finally, a note of thanks to the Department of Computer Science Engineering,
both teaching and non-teaching staff for their co-operation extended to me.
i

ABSTRACT
Many approaches have been developed to detect online spam. Fangtao Li and colleagues
initially analysed several attributes related to spam behaviour, such as content, sentiment,
product, and metadata features, and exploited a two-view supervised method to identify
spam reviews. Geli Fei and colleagues proposed a model to detect spammed products or
product groups by comparing the differences in rating behaviours between suspicious and
normal users. All these models rely on content features that can be easily found by
inserting special characters, but other features, such as temporal and network information,
have been employed as well. Qian Xu and colleagues collected large-scale real-world
datasets from telecommunication service providers and combined temporal and user
network information to classify spammers using Short Message Service (SMS). Sihong
Xie and colleagues proposed a model that uses only temporal features, with no semantic
or rating behaviour analysis, to detect abnormal bursts as the number of reviews
increases. Finally, Tyler Moore and colleagues studied the problem of temporal
correlations between spam and phishing websites. Intuitively, these works can also be
used to uncover sophisticated spam strategies.

ii
Table of Contents
Acknowledgement i
Abstract ii
Table of Contents iii
List of Figures v
Chapter Page no
1 Preamble 1
1.1 Definition 1
1.2 Objectives 2
1.3 Existing System 3
1.4Proposed System 3
2 Literature Survey 4
3 System Design 7
3.1 Design Consideration 7
3.2 System Architecture 7
3.3 Use Cases 9
3.4 Data Flow 10
4 System Requirement Specifications 13
4.1 Hardware Requirements 14
4.2 Software Requirements 14
5 Implementation 24
5.1 Selection of Programming Language 24
5.2 Selection of Platform 25

5.3 Functional Description of Modules 26
iii
6 System Testing 27
7 Conclusion 30
References 31
Appendix A : Snapshots ofthe Project 29
Appendix B : Exhibition and Conference Details 30

iv
List of Figures
Figure Page No.
Fig 3.1 System Architecture 8
Fig 3.2 Use Case Diagram 9
Fig 3.3 Data Flow Diagram 11
Fig 3.4 Activity Diagram 12
Fig 4.1 JSP Model 15
Fig 4.2 Life of JSP File 16
Fig 4.3 Net Beans Snapshot 20
Fig 5.1
Fig 5.2
Fig A 1
Fig A 2
Fig A3
Fig A 4
Fig A 5
Features of Java
Java Interpreter Architecture
Registration Page
Login Page
User Home
IP Blocking
Blocked User Message
22
23
29
30
31
32
33

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