Entropy-based anomaly detection for in-vehicle networks

By Michael Müter, Naim Asaj. 2011 IEEE Intelligent Vehicles Symposium (IV), 2011

359 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Electrical and Electronic Engineering, Signal Processing

View Paper: https://www.semanticscholar.org/paper/7e0e23218cfad13c0efb6a506bbc44a6ddbace3a

How researchers see this paper

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

In Computer Networks and Communications

1. Hyunsung Lee
2. Kang G. Shin
3. Nathalie Japkowicz
4. Wenchao Xu
5. Huy Kang Kim
6. Kyong-Tak Cho
7. S. Jeong
8. Adrian Taylor

In Electrical and Electronic Engineering

1. Huy Kang Kim
2. S. Jeong
3. Hyunsung Lee
4. Hyun Min Song
5. Kang G. Shin
6. Nathalie Japkowicz
7. Wenchao Xu
8. Kyong-Tak Cho

In Signal Processing

1. Hyunsung Lee
2. Kang G. Shin
3. Nathalie Japkowicz
4. Huy Kang Kim
5. Kyong-Tak Cho
6. S. Jeong
7. Adrian Taylor
8. Sylvain P. Leblanc

Citations in the corpus

• GIDS: GAN based Intrusion Detection System for In-Vehicle Network (2018, neutral): “proposed an anomaly detection based entropy [8].”
• A Survey of Intrusion Detection for In-Vehicle Networks (2020, background): “first introduced the concept of entropy-based attack detection for in-vehicle networks, which is efcient for detecting DoS attacks; however, a small number of malicious messages injected by malicious adversaries are difficult to recognize [61].”
• Evolutionary V2X Technologies Toward the Internet of Vehicles: Challenges and Opportunities (2020, background): “For example, CAN messages can be utilized for in-vehicle network intrusion detection in previous studies [56]–[58].”
• Fingerprinting Electronic Control Units for Vehicle Intrusion Detection (2016, methodology): “Our experiments on a CAN bus prototype and on real vehicles have shown CIDS to be able to detect a wide range of in-vehicle network attacks.”
• OTIDS: A Novel Intrusion Detection System for In-vehicle Network by Using Remote Frame (2017, methodology): “Thus, state-of-the-art IDS mainly used frequency or sequence analysis of overall messages [19], [20].”
• Anomaly Detection in Automobile Control Network Data with Long Short-Term Memory Networks (2016, methodology): “Converting CAN data into a format suitable for distance-based anomaly detection methods would require the development of compression algorithms analogous to SAX [8].”
• Intrusion detection system based on the analysis of time intervals of CAN messages for in-vehicle network (2016, methodology): “Muter and Asaj proposed an entropy-based anomaly detection method [19].”