Demystifying In-Vehicle Intrusion Detection Systems: A Survey of Surveys and a Meta-Taxonomy

Abstract

Breaches in the cyberspace due to cyber-physical attacks can harm the physical space, and any type of vehicle is an alluring target for wrongdoers for an assortment of reasons. Especially, as the automobiles are becoming increasingly interconnected within the Cooperative Intelligent Transport System (C-ITS) realm and their level of automation elevates, the risk for cyberattacks augments along with the attack surface, thus inexorably rendering the risk of complacency and inaction sizable. Next to other defensive measures, intrusion detection systems (IDS) already comprise an inextricable component of modern automobiles in charge of detecting intrusions in the system while in operation. This work concentrates on in-vehicle IDS with the goal to deliver a fourfold comprehensive survey of surveys on this topic. First, we collect and analyze all existing in-vehicle IDS classifications and fuse them into a simpler, overarching one that can be used as a base for classifying any work in this area. Second, we gather and elaborate on the so-far available datasets which can be possibly used to train and evaluate an in-vehicle IDS. Third, we survey non-commercial simulators which may be utilized for creating a dataset or evaluating an IDS. The last contribution pertains to a thorough exposition of the future trends and challenges in this area. To our knowledge, this work provides the first wholemeal survey on in-vehicle IDS, and it is therefore anticipated to serve as a groundwork and point of reference for multiple stakeholders at varying levels.

Special Vehicle Like Ambulance Recognition and Security System Using Mobility Accredit System

“…Using graph-based machine learning and lightweight cryptography, this study seeks to present a method for overcoming authentication and security challenges in ITS. In order supply authentication and security to the smart vehicle in ITS, derived system makes use of the ideas of identity-based authentication method and graph-based machine learning [18]. Karopoulos et al [18] work focuses on in-vehicle IDS.…”

“…In order supply authentication and security to the smart vehicle in ITS, derived system makes use of the ideas of identity-based authentication method and graph-based machine learning [18]. Karopoulos et al [18] work focuses on in-vehicle IDS. In order to classify any work in this field, authors first compiled and assessed every existing in-vehicle IDS classification, then combined them into a single, more general category.…”

Automotive Cybersecurity Application Based on CARDIAN

“…As explained in the previous section, the main attack identification techniques can be divided into signature-based and anomaly-based. Signature-based techniques are ineffective against unknown attacks; therefore, we proceed to describe different approaches for anomaly detection [13][14][15].…”

AI-Based Intrusion Detection Systems for In-Vehicle Networks: A Survey

“…Furthermore, modern vehicles embody software that exceeds 100 million lines of code, and it is expected to grow beyond 300 million lines of code in the near future [24]. Software on modern automobiles Evaluation Results [179] 2016 2 X X [101] 2016 5 X X X [104] 2011-2017 13 X X X [135] 2015-2017 9 X X [175] 2012-2018 9 X X X [2] 2015-2018 23 X X X [37] 2016-2018 7 X X [160] 2016-2018 12 X X X [7] 2014-2020 14 X X X [74] 2020-2022 33 X This survey 2016-2022 102 X X X X X IDS approaches were discussed and compared in the work of Aliwa et al [7]. Similar to Young et al [179], the authors classified in-vehicle IDSs into signature based and anomaly based.…”

“…However, most of these works belong to 2018 or earlier (12 out of 14), and the latest works were not included. Karopoulos et al [74] provided a unified taxonomy for IVN IDS. They identified 33 ML-based IDSs designed for IVNs.…”