Optimized machine learning model for detecting DDoS attacks

An Optimized Intrusion Detection Model for Wireless Sensor Networks


  •   The objective is to apply machine learning methods for addressing intrusion detection challenges in traditional networks.
  •   The goal is To produce an intrusion detection system that utilizes a machine learning model specifically designed for clustered wireless sensor network (WSN) environments.
  •   The proposed model is designed to have a high detection rate, low false positive rate, and low energy consumption.
  •   The results of the experiment demonstrate that the performance is promising and the learning speed is very fast.

  • Description

The development of low-cost, low-power, and multi-functional sensor nodes has been made possible by advances in digital electronics, wireless communications, and electro-mechanical systems technology. This has led to the advancement of sensor networks which exploit the sensing, data processing, and communication features of these nodes. As a result, society and the global economy have been revolutionized. Wireless sensor network (WSN) nodes have limited energy, and as a result, the accuracy of intrusion detection systems in WSN is weak.