Hazard Identification and Detection using Machine Learning Approach

Project Code :TCMAPY201

Objective

In this proposed model, we design a new classification system to analyze and detect the malicious web pages using machine learning classifiers such as, random forest, support vector machine. Naive Bayes, logistic regression and Some special URL (Uniform Resource Locator) based on extricated features the classifiers are trained to predict the malicious web pages.

Abstract

With the rapid development of the web, more and more services like internet banking, e-commerce, social networking, shopping, making a bill payment, e-learning usage of internet surfing has become increased. This becomes become the source of attacks for the intruder and the websites are put at hazard. However, the existing approaches are not adequate to protect the surfers which require an expeditious and precise model that can be able to distinguish between the begnign or malicious webpages. In this research article, we design a new classification system to analyze and detect the malicious web pages using machine learning classifiers such as, random forest, support vector machine. naïve Bayes, logistic regression and Some special URL (Uniform Resource Locator) based on extricated features the classifiers are trained to predict the malicious web pages. The experimental results have shown that the performance of the random forest classifier achieves better accuracy of 95% in comparison to other machine learning classifiers

Keywords: Malicious Web Page, Machine Learning, Detection, URL, Malicious Websites, Random Forest, Ensemble, Naïve Bayes.

NOTE: Without the concern of our team, please don't submit to the college. This Abstract varies based on student requirements.

Block Diagram

Specifications

SOFTWARE SPECIFICATIONS:

  • Technology: Machine Learning, Application.
  • Libraries: Pandas, Numpy, Sklearn.
  • Version: Python 3.6+
  • Server-side scripts: HTML, CSS, JS
  • Frame works: Flask
  • IDE: Pycharm

HARDWARE SPECIFICATIONS:

  • RAM: 8GB, 64-bit os.
  • Processor: I3/Intel processor
  • Hard Disk Capacity: 128 GB +

Learning Outcomes

  • Scope of Real Time Application Scenarios.
  • Objective of the project.
  • How Internet Works.
  • What is a search engine and how browser can work.
  • What type of technology versions are used.
  • Use of HTML , and CSS on UI Designs.
  • Data Parsing Front-End to Back-End.
  • Working Procedure.
  • Introduction to basic technologies used for.
  • How project works.
  • Input and Output modules.
  • Frame work use.
  • Datasets properties.
  • Machine learning algorithms.
  • Data preprocessing techniques.
  • Graphs for drawing based on highest accuracy model.
  • Implementing Naive Bayes.
  • What is ensemble technique.
  • How bagging works.
  • Importance of Random Forest.
  • Project Development Skills:
    • Problem analyzing skills.
    • Problem solving skills.
    • Creativity and imaginary skills.
    • Programming skills.
    • Deployment.
    • Testing skills.
    • Debugging skills.
    • Project presentation skills.
    • Thesis writing skills.

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