ON THE EFFECTIVENESS OF SHILLING ATTACK OR PROFILE INJECTION

ON THE EFFECTIVENESS OF SHILLING ATTACK OR
PROFILE INJECTION ATTACKS AGAINST RECOMMENDER
SYSTEMS
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Table of Contents
Chapter 1: Introduction....................................................................................................................5
1.1 Introduction............................................................................................................................5
1.2 Background of the study........................................................................................................5
1.3 Research aim and objectives..................................................................................................6
1.4 Significance of research.........................................................................................................6
1.5 Limitation of research............................................................................................................7
Chapter 2: Literature Review...........................................................................................................9
2.1 Concept of shilling attack......................................................................................................9
2.2 Identification of different types of attacks...........................................................................10
2.3 Impact of shilling attack on online e-commerce site...........................................................10
2.4 Prediction shift and rating variances of shilling attack........................................................11
2.5 Credibility of group users by rating prediction model.........................................................12
2.6 Locate suspicious attack segments by rating time series.....................................................12
2.7 Detecting profile injection attack.........................................................................................14
Chapter 3: Methodology................................................................................................................15
3.1 Data collection................................................................................................................15
3.2 SVM classifier.................................................................................................................15
3.3 Decision tree...................................................................................................................17
3.4 Method of Fog war..........................................................................................................20
3.5 Research onion................................................................................................................21
3.6 Research philosophy.......................................................................................................22
3.6.1 Justification...................................................................................................................22
3.7 Research Approach.........................................................................................................22
3.7.1 Justification...................................................................................................................23
3.8 Research Design..............................................................................................................23
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3.9 Data collection methods..................................................................................................24
3.10 Population and sampling.................................................................................................24
3.11 Ethical consideration.......................................................................................................25
3.12 Limitation........................................................................................................................25
Chapter 4: Data analysis................................................................................................................26
4.1 Introduction..........................................................................................................................26
4.2 SVM Classifier.....................................................................................................................26
4.3 Building a BDT (Binary Decision Tree) with intra-cluster correlation attribute.................27
4.4 Traversing BDT for detecting shilling profiles....................................................................29
Chapter 5. Implementation............................................................................................................32
5.1 Detection of shilling attack with coding..............................................................................32
5.2 Impact of shilling attack on recommended system with data..............................................32
5.3 Identification of most dangerous attack on recommended system......................................34
5.4 Overview of the proposed Algorithm..................................................................................36
5.5 Implementation of developed coding with the help of respective models...........................37
5.6 Comparison existing approach with the proposed model....................................................40
5.7 E-commerce website creation..............................................................................................41
5.8 Demo for downloading user data from e-commerce website..............................................47
5.9 Analysis of the result or outcome........................................................................................61
5.10 Identification of method or techniques to prevent recommender system from shilling
attacks.........................................................................................................................................74
5.11 Benefits of global communication.....................................................................................75
Chapter 6: Conclusion...................................................................................................................76
6.1 Conclusion...........................................................................................................................76
6.2 Outcome of the experiment..................................................................................................77
6.3 Future scope.........................................................................................................................77
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to synchronize the entire data base (Alostad, 2019). One of the major benefit of this research is
reduction in shilling attacks that a improve customer experience by protecting users identity. It
enables e-commerce sites and online marketing platform to strengthen recommender system by
using shilling detection algorithm that allows users in gaining trust model. The research allows e-
commerce sites to develop user’s friendly algorithm to assure that the needs and demands of
consumers can be met effectively. Therefore, a proper research needs to perform related to
recommender system wisely that facilitates in assessing interest and preference of consumers
positively.
1.5 Limitation of research
There are several limitation of research that reflect on feasibility and reliability of developed
recommended system. The major limitation of this research is constraint budget that create a
range of problems in assessing advance technology in developing shilling detection algorithm. It
resists researcher in implementing new and advance techniques in improving user’s service
experience to make sure a significant change can be deployed. In addition, constraint budget has
also created adverse impact on research by set the boundary of research. The limited research
reduces the development of effective and user’s metrics algorithm to prevent potential shilling
attacks against recommended system. On the other hand, limited resources also create an adverse
impact on research by changing user’s service model and algorithm. A sustainable resource is
required to deploy for research so that each task or activity can be performed in a systematic way
by triggering each deliverable positively. Similarly, the requirement of a firm machine language
is required to implement or use so that user metrics algorithm can be change that help in
reducing unauthorized identity positively. The limited resources and time reduces the reliability
of research by resisting researcher in utilizing appropriate tools and techniques for successful
accomplishment of task.
1.6 Scope of research
The research sheds light on potential issues and challenges in reducing shilling attacks against
recommender system to make sure an effective coding can be developed. The internet revolution
increases the market of online marketing and e-commerce platform that demand for effective
system and user’s algorithm. This user algorithm helps in providing right product at right time
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based on consumers’ preference and choices to assure that maximum benefit can be gathered
effectively. This research enables several e-commerce sites in assessing the needs and demand of
consumers that eventually strengthen the relationship. The major advantage of this research is
improvement of customer experience by giving right product at right time by protecting user
personal information. A constructive approach has been applied to understand different variables
of recommender system to make sure shilling attacks can be minimized as far as possible. A
detection method with respect to group users that help in reducing shilling attack against
recommender system through changing user’s metrics algorithm. A range of models and
algorithm methods have been applied that help in developing an effective algorithm that
strengthen the recommender system effectively. There are ranges of methods and model have
been using into the recommender system that help in giving right product at r right services to
assure that maximum benefit can be gathered positively (Patentimages.storage.googleapis.com,
2019). A suspect time series has been implemented into users algorithm that help in prioritizing
the tasks and attack profile so that potential attacks can be reduced as far as possible. Shilling
detection method is considered as one of the feasible ways to synchronize the entire data base.
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