Intrusion Detection System Using Hybrid Model

EasyChair Preprint no. 13245

Abstract

Technology and network industries have advanced quickly in recent decades.The expansion of piracy and the compromise of many existing systems has made it essential to develop information security solutions that can identify new threats. In order to address these issues, this work proposes a system called IDS-AI that is based on artificial intelligence techniques. It is capable of detecting recent and dispersed invasions. We employed an auto-encoder for features reduction and two models to assess CNN and SVM in order to evaluate our strategy. The experimental analysis of the dataset demonstrates the suggested model's capability to produce reliable results. On the UNSW-NB15 dataset, our model actually achieves 99.58% and 99.66% accuracy for SVM and CNN, respectively.

Keyphrases: CNN, Intrusion Detection System, SVM, UNSW-NB15

@Booklet{EasyChair:13245,
  author = {Imen Chebbi and Ahlem Ben Younes and Leila Ben Ayed},
  title = {Intrusion Detection System Using Hybrid Model},
  howpublished = {EasyChair Preprint no. 13245},

  year = {EasyChair, 2024}}