Intrusion Detection of Imbalanced Network Traffic Based on ML and DL

Project Code :TCMAPY436

Objective

The main objective of this project is to use machine learning techniques for detecting intrusions in any network system.

Abstract

We develop a Network Intrusion Detection System (NIDS), which uses a suite of machine learning techniques to automatically detect attacks against computer networks and systems. It uses an approach to develop efficient NIDS by using the principal component analysis (PCA) along with different classification algorithms like Support Vector Machines, Random Forest and XgBoost. The purpose of an intrusion detection system is to detect attacks. However, it is equally important to detect attacks at an early stage in order to minimize their impact.


Keywords: Supervised Learning, Anomaly Detection, Intrusion Detection, Principal Component Analysis, Ensemble methods, Bagging and Boosting.

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

Block Diagram

Specifications

  • HARDWARE SPECIFICATIONS:

    • Processor: I3/Intel
    • Processor RAM: 4GB (min)
    • Hard Disk: 128 GB
    • Key Board: Standard Windows Keyboard
    • Mouse: Two or Three Button Mouse
    • Monitor: Any

    SOFTWARE SPECIFICATIONS:

    • Operating System: Windows 7+
    • Server-side Script: Python 3.6+
    • IDE: PyCharm
    • Libraries Used: Pandas, Numpy, Matplotlib, SciKit-Learn.
    • Server Side Scripts: HTMl,CSS,JS
    • Frame works:Flask.

Learning Outcomes

  • About Python.
  • About PyCharm.
  • About Pandas.
  • About Numpy.
  • About Machine Learning.
  • About Artificial Intelligent.
  • About how to use the libraries.
  • 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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