An Investigation into Shilling Attacks on Recommender Systems: A Study

This project delves into the critical issue of shilling attacks, also known as profile injection attacks, targeting recommender systems within e-commerce and online marketing platforms. The research investigates the effectiveness of these attacks and explores various detection and mitigation strategies. The project employs several methodologies, including the use of SVM classifiers, decision trees, and the Naive Bayes algorithm to analyze and identify malicious profiles. The study further includes coding implementation to simulate and test the impact of shilling attacks, as well as the development of an e-commerce website demo to illustrate the practical implications. Data analysis, including the building of a Binary Decision Tree (BDT) with intra-cluster correlation attributes, is performed to identify suspicious attack segments. The project also considers ethical implications, limitations, and the benefits of global communication in addressing these attacks. The ultimate goal is to provide insights and recommendations for enhancing the security and reliability of recommender systems, protecting both businesses and consumers from manipulation.

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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6.4 Recommendation.................................................................................................................78
6.4.1 Recommendation 1: Implementation of customer feedback.........................................78
6.4.2 Recommendation 2: Encourage open communication with consumers........................79
Reference list.................................................................................................................................80
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Chapter 1: Introduction
1.1 Introduction
Recommender system is referred to sub- part of filtering system that enables consumers in
getting right product at reasonable price. It has been using on different online marketing and e-
commerce platform that improves the services and customer experience gradually. The purpose
of this research is to examine different effectiveness of profile injection attack and shilling attack
with the help of different models and algorithm to reduce the rate of this attacks at significant
level. Online selling platform and digital marketing are contributing a large market all across the
world that enables consumers to purchase desired products at affordable rate (Alonso et al.,
2019). Recommender system is beneficial for the consumers who compare products’
specification and prices to get right products. The rise of online marketing allows hackers or
attacker to influence the decision of consumers in purchasing relevant products through profile
injection attacks. The research sheds light on different models and algorithm to reduce the
shilling attacks as far as possible.
1.2 Background of the study
The rise of digital marketing and online selling websites demand for an effective recommender
system that allow consumers in getting right products and services within given time period.
Amazon and Netflix are two examples who are using recommender system to improve their
services by helping consumers in getting right products. A collaborative filtering algorithm has
been used to improve the reliability and feasibility of recommender system that eventually
upsurge the profit and sales performance gradually. The implication of recommender system is
successful within e-commerce sites that enhance the competitors gradually. On the another hand,
shilling attacks have been evident in last few years in which an unauthorized or fake users have
been insert to users item matrices. This approach benefits other competitors in gaining
competitive advantage by predicting rating that help in changing needs and demand of
consumers. The world population is about 7.6 billion in which 4.1 billion users are internet users
that demand for an effective algorithm. This research scrutinize different shilling attacks
strategies and shilling attacks detection schemes so that potential attacks can be minimized as far
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as possible. There are ranges of independent qualitative factors that are using in recommender
system that help in detecting shilling attacks. The metrics standard deviation in user’s rating,
Number of Prediction-Differences (NPD), degree of similarity with top neighbors and degree of
agreement with other users. This research emphasis on different models and algorithm to
detected shilling attacks that help in improving recommender system.
1.3 Research aim and objectives
The aim of this research is to examine effectiveness of profile injection attacks or shilling attacks
with the help of different models and user metrics algorithm.
 To understand the concept of shilling attacks against recommender system
 To use Naïve Bayes algorithm to understand the pattern
 To develop the code by using python language so that user metrics can be changed
1.4 Significance of research
The research emphasis on broad perspective of shilling attacks against recommended system so
that sales and financial performance can be improved by improving sales performance gradually.
This research supports many online platform and e-commerce sites to provide right product at
reasonable price that facilitates in gaining competitive advantage vigilantly. In the UK, two
majorly attacks have been evident against recommender system that includes push attacks and
nuke attacks in which users faces difficulties in purchasing desired products. The developed
coding and implementation module help in amending significant impact on recommender system
that eventually improved customer services. The effectiveness of recommender system has been
improved through implementing detection technology vigilantly. A detection method with
respect to group users that help in reducing shilling attack against recommender system through
changing user’s metrics algorithm. A rating prediction method has been used in order to trigger
shilling attack on the basis of credibility evaluation method by resisting unauthorized access. The
wide aspects of recommender system have been critically examined by making unfair
competition that result in loss of genuine users. 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
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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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Chapter 2: Literature Review
2.1 Concept of shilling attack
E-commerce has entered into every one daily basis life and important a part daily routine. People
now a day completely slave of e- commerce .people buy and sale all kind of things for example
food, clothes, transport, movie, job, apartment chat on ,etc. In short they can’t imagine without
internet or addicted. its impact positive and negative both way here we are going to explain how
some retailers are track our record and search and accordingly they recommend their choice of
things and willing to make us feel how this product is important. Normal users are bound to buy
those useless product just because they are seen them top fake rank. Users are mainly trapped in
this case those who highly rated .overload of information is the main cause of its bad conditions,
online retailers try to generate their product top rank to attract theirs buyers. Some are push
behind this strategy only one economic motive according to survey sixty four percent users are
attract only to see items rating and judge them by comments. The profile injection is known us
shilling attack. Push attack and nuke attack are two type attacks (Bilge, Ozdemir and Polat,
2014). In push attack specified item and injecting biased ratings on the other hand nuke attack
influenced by just opposite way of push attack. Shilling attack causes damage on a lot in user’s
recommender operating system.
Most of the buyers are register their account their on social media like Facebook, Instagram,
LinkedIn or they are using direct their users id, which make easier way to know about customer
to their choice preference. The retailer make few fake profile and give some different types of
product which they wants to sale to their buyers comment a positive review and give five star.
Most of the time buyers have only one faithful choice to choose only that product that is highly
rated and given below a positive review. Shilling attacker traps them via their other social media
information. It is very hard to differentiate which one is real or fake comment. In shilling attack
profile perfectly designed like no one guess it is completely fraud or real. Push attack mainly
used for to for high market demand to increase his productivity. On other side nuke attack are
using for decreasing the demand of competitors. There are so many attack we just here discussed
few of them segment attack is kind of where they target only group of audience like if he like
comedy movie than they provide them to comedy and recommend his group. The attacker
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bandwagon attack is the phenomenon where the trend or fashion gone viral which already
adopted by other. in this phenomenon more people comes to believe that a particular fashion is
more trendy .average attack are those where when they judge by maximum and minimum value
in between and that much crowd seen as much seen bandwagon attack.so here we discussed all
of the aspects of retailer recommendation and their attack. how they change and to get their
buyer given data to track and follow and make them fool for their own profit making and take
advantage of their customers casual and unawareness behavior which make them in trouble
(Dhawan, 2016).
2.2 Identification of different types of attacks
we are reach in that age where technology are advanced and work very smooth and fast .only one
click we get all history past present information about any things. At past we are not that much
advanced normally people depend on other people to know the product value quality demand is
good or bad .now a day we get recommender system where we know historical behavior .The
most popular approaches are content based and collaborating filtering .In content based approach
requires we get good amount features example it can be movie review actor ,director content
article etc. on the other hand on the other hand collaborating filtering its only require users
historical preference .it generating a recommendation it determine both users similarities and
dissimilarities .the future announcement or prediction are only judge items scores and similarities
.we will only describe it only two categories
1.exlicit rating-its rate only given by their buyer to particular product on sliding scale like four
star for jungle book .this called direct review (Dwivedi, 2018) .The users express their feeling
how he feel.
2-implicit rating-here implicates rating users indirectly express like or dislike for an item for
example whether or not listen to music track, click. Here we are going to show you a closer look
at collaborating filtrating.
2.3 Impact of shilling attack on online e-commerce site
E-commerce is spread huge area beyond our imagination. People cannot imagine how other
cleverly people steal their data and information through our own small careless activities make
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for us a big trapping net .Online world also have dark side area where we unknowingly
participate without our knowledge. We share our knowledge ideas. We share our thought email
id, bank detail; credit card detail, friends and family, contact number our daily basis life style
mention over there, we lives in imaginary world. What we thought today, what we feel
yesterday, what is our desire destine etc. We never know their all things are share by some
hidden person and with the help of all these data they are going to be making profit and balance
are gone through our pocket .They controlled our mind as they wanted to do, how many time we
visit sites, clicking by mistake ad sense, how much we spend time to watch our likable things
and willing to buy .some shilling attacker secretly watch our each and every activities and
present and past future style choice and preferences. After all these circumstances they
recommended us according to our choice taste, and preference wise to buy product .Below the
comments of given items they given fake reviews and likes to attract viewers and try to
convinced and control our mind and prepared us that these are items are that which the buyer
waiting for long time to buy its part of our life mean to say necessities .If we skip or ignore they
recommend us again and again different types of as per our requirement (FAKE USER
PROFILE DETECTION ON ONLINE SOCIAL NETWORKING., 2017).They refer as to where
we visit more frequently soon as soon buyers getting addicted to this site ,for hope may they get
more offer more discount . Shilling attacker increase their own choice item and decreased value
of competitor’s product, at the starting point buyers see only rupees two fifty item to pay but
after clicking order placed they got notification hundred rupees extra for shipping charge include.
These are their strategy to trap customers.
2.4 Prediction shift and rating variances of shilling attack
This predicts is based on fluctuation of rating scores how it look before and after the
implementation during attack. In few experiment they show how it inject how it attack an
shilling type of attack size attacker fake way to change .the stalker stalking users data and as per
your choice they rate the desirable item given five rate and present to make users feel comfort
zone. Rate variance is very common in this shilling attack. E-commerce deep impact on users
and non-users here using word non users for those who is not aware or rarely using sites or less
active and feel fear to visit sites .They users are already trap to waste there with blindly non users
are those who entered into their just a faith on users words .rating variances are fluctuate time to
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time if retailers wants to make down competitors they increase the rating of their items wise
versa (Fire et al., 2014).
2.5 Credibility of group users by rating prediction model
The main aim of our research is to web content evaluation which is focus on people. The shilling
attacker hit each interval. Credibility scores q1 for each users it is based on users attack items are
score on average score of profile. In this section depends on rating, it is proposed to calculate
completely credibility of users previous data and information. They actual work on assumption
basis which show difference between forged rating and rating of genuine profile. In comparison
genuine profile and attack profile should be higher than in range. Whenever rating variance
increase automatically accuracy of prediction decreases. Therefore, measurement descriptor is
much more confidence to recommender system for injecting. They actual work one users on data
base which help to them more predictable for users and users group of people of circle. It defines
as the worst standard, the value as much as higher, some forged highest probability rather than
normal profile. On the other hand the small value minimum chance or we can say genuine
profiles set to be a small value. They set range in between zero point eight (0.8 to 1.2) to one
point two in hybrid according to recommendation systems.
At the previous discussion we notice that the attacker generated average score of rated items
which given by their users. We clearly notice that they recommend only highly rated variance are
highlighting from shilling attacker. If it seen something abnormally rating variance occurs in
profile for some particular time of period, which makes them way to prediction plan and inject
users to trap them in there term and condition without users knowledge .When the users are
rating any particular item which shown his internet on it or preference s how much they like or
dislike that particular item. The users are self-informer, if profile is continue given by without
any disturbance so that profile is genuine profile category. (Gurajala et al., 2016) Conversely, if a
profile is forged it’s say that the users profile is already on an attack profile; normally the rating
is given in between one to five star. So, the attacker easily identifies a user is rated or unrated
items. Randomly filler items are pickup, but unrated items are already present there.
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