Proposal of HILS-Based In-Vehicle Network Security Verification Environment

2018 ◽  
Author(s):  
Ryo Kurachi ◽  
Toshiyuki Fujikura
Keyword(s):  
Network Security ◽  
Vehicle Network ◽  
Security Verification

Long Short-Term Memory Neural Network-Based Attack Detection Model for In-Vehicle Network Security

2020 ◽  
Vol 4 (6) ◽  
pp. 1-4
Author(s):  
Zadid Khan ◽  
Mashrur Chowdhury ◽  
Mhafuzul Islam ◽  
Chin-Ya Huang ◽  
Mizanur Rahman
Keyword(s):  
Neural Network ◽  
Network Security ◽  
Short Term Memory ◽  
Attack Detection ◽  
Short Term ◽  
Term Memory ◽  
Detection Model ◽  
Long Short Term Memory ◽  
Vehicle Network

scholarly journals A new methodology for analyzing vehicle network topologies for critical hacking

2020 ◽  
Author(s):  
Maen Ghadi ◽  
Ádám Sali ◽  
Zsolt Szalay ◽  
Árpád Török
Keyword(s):  
Network Security ◽  
Statistical Techniques ◽  
New Approach ◽  
Large Sample ◽  
Passenger Safety ◽  
Network Topologies ◽  
Vehicle Networks ◽  
Positive Effect ◽  
Vehicle Network ◽  
New Framework

Abstract This study aims to provide a new approach for describing and measuring the vulnerability of in-vehicle networks regarding cyberattacks. Cyberattacks targeting in-vehicle networks can result in a reasonable threat considering passenger safety. Unlike previous literature, the methodology focuses on a comparatively large sample of vehicle networks (114 objects) by proposing a new framework of statistical techniques for measuring, classifying, and modelling in-vehicle networks concerning the changed vulnerability, instead of dealing with each vehicle network individually. To facilitate understanding of the vulnerability patterns of in-vehicle networks, the dataset has been evaluated through three analytic stages: vulnerability identification, classification, and modeling. The result has helped in ranking vehicles based on their network vulnerability level. The result of the modeling has shown that every additional remote endpoint installation causes a relevant weakening in security. Higher cost vehicles have also appeared to be more vulnerable to cyberattacks, while the increase in the number of segmented network domains has had a positive effect on network security.

scholarly journals Research on Vehicle Network Security Situation Prediction Based on Improved CLPSO-RBF

2021 ◽  
Vol 1757 (1) ◽  
pp. 012148
Author(s):  
Ruxiang Li ◽  
Fei Li ◽  
Chunwang Wu ◽  
Jiaqi Song
Keyword(s):  
Network Security ◽  
Vehicle Network

scholarly journals Chaotic and Random Neural Network Cipher for In-Vehicle Network Security

2021 ◽  
Author(s):  
Zhongda Liu ◽  
Takeshi Murakami ◽  
Satoshi Kawamura ◽  
Hitoaki Yoshida
Keyword(s):  
Neural Network ◽  
Network Security ◽  
Random Neural Network ◽  
Vehicle Network
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