Detection of Distributed Denial of Service Attacks in SDN using Machine learning techniques

Project Code :TCMAPY476

Objective

The main objective of this project is to create an Effective Detection system for stress detection among individuals and taking necessary precautions to prevent the users from committing suicide.

Abstract

Software-defined network (SDN) is a network architecture that used to build, design the hardware components virtually. We can dynamically change the settings of network connections. In the traditional network, it's not possible to change dynamically, because it's a fixed connection. SDN is a good approach but still is vulnerable to DDoS attacks. The DDoS attack is menacing to the internet. To prevent the DDoS attack, the machine learning algorithm can be used. The DDoS attack is the multiple collaborated systems that are used to target the particular server at the same time. In SDN control layer is in the center that link with the application and infrastructure layer, where the devices in the infrastructure layer controlled by the software. In this paper, we propose a machine learning technique namely Decision Tree to detect malicious traffic. Our test outcome shows that the Decision Tree detects whether the attack is safe or not.

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 IDE
  • Libraries Used: Pandas, Numpy, Sklearn

Learning Outcomes

  • Scope of Real Time Application Scenarios
  • What type of technology versions are used
  • Working Procedure
  • Introduction to basic technologies used for
  • How project works.
  • Input and Output modules
  • Frame work use
  • Datasets properties
  • Deep learning algorithms.
  • Data pre-processing techniques
  • What is model
  • 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.

Demo Video

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