Intrusion Detection for in-Vehicle Communication Networks: An Unsupervised Kohonen SOM Approach

Vita Santa Barletta, Danilo Caivano, Antonella Nannavecchia and Michele Scalera
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Vita Santa Barletta: Department of Informatics, University of Bari, Via E. Orabona 4–Bari, 70125 Bari, Italy
Danilo Caivano: Department of Informatics, University of Bari, Via E. Orabona 4–Bari, 70125 Bari, Italy
Antonella Nannavecchia: Department of Economics and Management, University LUM Jean Monnet, SS 100 km 18–Casamassima (BA), 70010 Bari, Italy
Michele Scalera: Department of Informatics, University of Bari, Via E. Orabona 4–Bari, 70125 Bari, Italy

Future Internet, 2020, vol. 12, issue 7, 1-24

Abstract: The diffusion of embedded and portable communication devices on modern vehicles entails new security risks since in-vehicle communication protocols are still insecure and vulnerable to attacks. Increasing interest is being given to the implementation of automotive cybersecurity systems. In this work we propose an efficient and high-performing intrusion detection system based on an unsupervised Kohonen Self-Organizing Map (SOM) network, to identify attack messages sent on a Controller Area Network (CAN) bus. The SOM network found a wide range of applications in intrusion detection because of its features of high detection rate, short training time, and high versatility. We propose to extend the SOM network to intrusion detection on in-vehicle CAN buses. Many hybrid approaches were proposed to combine the SOM network with other clustering methods, such as the k-means algorithm, in order to improve the accuracy of the model. We introduced a novel distance-based procedure to integrate the SOM network with the K-means algorithm and compared it with the traditional procedure. The models were tested on a car hacking dataset concerning traffic data messages sent on a CAN bus, characterized by a large volume of traffic with a low number of features and highly imbalanced data distribution. The experimentation showed that the proposed method greatly improved detection accuracy over the traditional approach.

Keywords: intrusion detection systems; unsupervised learning; self-organizing maps; CAN bus; Kohonen SOM network; cyber–physical systems; security; vehicle safety; cyber-attacks (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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