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Shilling Attack
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Anomaly Detection Against Shilling Attacks

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Description

Anomaly Detection Against Shilling Attacks In E-Com Site

Various types of web applications have gained both higher customer satisfaction and morebenefits since being successfully armed with personalized recommendation. However, the increasingly rampant shilling attackers apply biased rating profiles to systems to manipulate item recommendations, which not just lower the recommending precision and user satisfaction but also damage the trustworthiness of intermediated transaction platforms and participants.

Many studies have offered methods against shilling attacks, especially user profile based-detection. However, this detection suffers from the extraction of the universal feature of attackers, which directly results in poor performance when facing the improved shilling attack types. This paper presents a novel dynamic time interval segmentation technique based item anomaly detection approach to address these problems. In particular, this study is inspired by the common attack features from the standpoint of the item profile, and can detect attacks regardless of the specific attack types.

The proposed segmentation technique could confirm the size of the time interval dynamically to group as many consecutive attack ratings together as possible. In addition, apart from effectiveness metrics, little attention has been paid to the robustness of detection methods, which includes measuring both the accuracy and the stability of results. Hence, we introduced stability metric as a complement for estimating the robustness. Thorough experiments on the Movie Lens dataset illustrate the performance of the proposed approach, and justify the value of the proposed approach for online applications.Anomaly Detection Against Shilling Attacks In E-Com Site

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Anomaly Detection Against Shilling Attacks

1 file(s) 99.08 KB
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Software Requirements: –
Front End: HTML5, CSS3, Bootstrap
Back End: PHP, MYSQL
Control End: Angular Java Script

Android Tools:
IDE: Android Studio
Android Emulator
XAMPP 7.4 – 64 bit

PHP Tools:
XAMPP 7.4 – 64 bit

Existing Definition

Many studies have offered methods against shilling attacks, especially user profile based-detection. However, this detection suffers from the extraction of the universal feature of attackers, which directly results in poor performance when facing the improved shilling attack types.

This paper presents a novel dynamic time interval segmentation technique based item anomaly detection approach to address these problems. Inparticular, this study is inspired by the common attack features from the standpoint of the item profile, and can detect attacks regardless of the specific attack types.

Proposed Solution:

The proposedsegmentation technique could confirm the size of the time interval dynamically to group asmany consecutive attack ratings together as possible. In addition, apart from effectivenessmetrics, little attention has been paid to the robustness of detection methods, whichincludes measuring both the accuracy and the stability of results. Hence, we introducedstability metric as a complement for estimating the robustness. Thorough experimentson the MovieLens dataset illustrate the performance of the proposed approach, and justifythe value of the proposed approach for online applications.

 System Modules:

ADMIN

  • Login
  • Verify Attacks
  • Delete Attacks

USER

  • Register
  • Login
  • Product List
  • Product description
  • Ratting
  • comment

MODULES:

ADMIN:

  • Login:

Admin can login this system after they can view home page.

  • Verify Attacks:

Admin enters this system and view verifies the attacks details.

  • Delete Attacks:

Admin can only provide approval to publishing the user research document.

USER:

  • Register:

User enters this system and register with own details.

  • Login:

User can login this system after they can view home page.

  • Product list:

User can login this system after they can view the product list.

  • Product Description:

User can login this system after they can view product description.

  • Ratings:

User can login this system after they can view product description after that they gives for ratings.

  • Comment:

User can login this system after they can view product description after that they gives for comment also.

FREE PPT
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PPT Detection Against Shilling Attacks

1 file(s) 233.55 KB
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FAQS

1. When will I get the project source code after payment?

Our Tech support team will contact you immediately.

a) Instant download link available from our website  (or)

b) You will receive the project source code within an hour by mail or whatsapp

2. How you will deliver the project?

We will connect your system through (Remote Support) Splashtop and Anydesk software and configure the source code.

3. Will your provide revision / modification?

 Yes, We will provide 1 revision / modification as per acceptable time.

4. Will I get a refund?

Yes, for following two reason

a) If we failed to deliver the Deliverables  within 3 days (Or)

b) If complete software deployment is failed in Customer Laptop, i.e configuration of source code.

Customer can request for refund contact@codeshopy.com

5. How do I run the project on my PC?

You don’t need to worry about it. Our Tech support team will do the project installation and configuration.

6. Will I get a Project Explanation and the Project Demo?

Yes, through Google Meet our developer will explain live demo with recording session.

7. What should I do if there are any issues after the project delivery?

You can contact our support team if there are any issues. Tech Support: +91 9629754500

8. How can I make the Payment?

Payment Options: •Debit Card •Credit Card •Net Banking •Google Pay •UPI Payment •Wallet •Direct Bank Transfer •Pay pal

9. How to buy the project?

You can buy from our official website codeshoppy.com or direct bank transfer. Through any online payment method.

10. How Can I do modification in source code ?

Yes, Using change management tutorial you can do the changes in backup copies.

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