Hybrid Intrusion Detection

EasyChair Preprint no. 12844

Abstract

With the increasing population of Industry industrial big data (IBD) has become a hotly discussed topic in digital and intelligent industry  fields. The security problem existing in the signal processing on large scale of data stream is still a challenge issue in industrial internet of things, especially when dealing with the high-dimensional DDoS attack detection for intelligent industrial application. DDoS attcak detection has been widely used to ensure network security, but classical detection means are usually signature-based or explicit-behavior-based and fail to detect unknown attacks intelligently, which are hard to satisfy the requirements of SD-IoT Networks.
In this process we propose a machine learning algorithm and to detect the DDoS attack from network. Firstly, we need apply the UNSW NB15 as input. Then find the target variable and split the data into training set and testing set Then it will applied into classifcication method. In this method the machine learning algorithm like Extra Tree classifier and Random Forest is applied to detect the DDoS attack. Finally predict the type of DoS attack and find the result based on accuracy, precision, recall, and f1-measure.

Keyphrases: Discovery of hidden performance opportunities, Finding of Vulnerabilities, Intrusion Detection System, Prevention of security breaches and threats

@Booklet{EasyChair:12844,
  author = {Athava Bhanu Naga Pavan and Kalyan Reddy Atmakur and Gangireddy Nagarjunareddy and Muppuri Lakshmi Narayana and Chitturi Rakesh and Muskhan Kumari},
  title = {Hybrid Intrusion Detection},
  howpublished = {EasyChair Preprint no. 12844},

  year = {EasyChair, 2024}}