GIDS: GAN based Intrusion Detection System for In-Vehicle Network

By Eunbi Seo, Hyun Min Song, Huy Kang Kim. Conference on Privacy, Security and Trust, 2018

435 citations recorded by the source corpus.

A Controller Area Network (CAN) bus in the vehicles is an efficient standard bus enabling communication between all Electronic Control Units (ECU). However, CAN bus is not enough to protect itself because of lack of security features. To detect suspicious network connections effectively, the intrusion detection system (IDS) is strongly required. Unlike the traditional IDS for Internet, there are small number of known attack signatures for vehicle networks. Also, IDS for vehicle requires high accuracy because any false-positive error can seriously affect the safety of the driver. To solve this problem, we propose a novel IDS model for in-vehicle networks, GIDS (GAN based Intrusion Detection System) using deep-learning model, Generative Adversarial Nets. GIDS can learn to detect unknown attacks using only normal data. As experiment result, GIDS shows high detection accuracy for four unknown attacks.

Subfields: Artificial Intelligence, Computer Networks and Communications, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/21b7dea0b9751c6bc7db2086109b6b1a4f9e60b5

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

  1. Abdallah Moubayed
  2. Li Yang
  3. Abdallah Shami

In Computer Networks and Communications

  1. Li Yang
  2. Abdallah Shami
  3. Abdallah Moubayed

In Electrical and Electronic Engineering

  1. Li Yang
  2. Abdallah Shami
  3. Abdallah Moubayed

Citations in the corpus