3 research outputs found
C-ITS Environment Modeling and Attack Modeling
As technology advances, cities are evolving into smart cities, with the
ability to process large amounts of data and the increasing complexity and
diversification of various elements within urban areas. Among the core systems
of a smart city is the Cooperative-Intelligent Transport Systems (C-ITS). C-ITS
is a system where vehicles provide real-time information to drivers about
surrounding traffic conditions, sudden stops, falling objects, and other
accident risks through roadside base stations. It consists of road
infrastructure, C-ITS centers, and vehicle terminals. However, as smart cities
integrate many elements through networks and electronic control, they are
susceptible to cybersecurity issues. In the case of cybersecurity problems in
C-ITS, there is a significant risk of safety issues arising. This technical
document aims to model the C-ITS environment and the services it provides, with
the purpose of identifying the attack surface where security incidents could
occur in a smart city environment. Subsequently, based on the identified attack
surface, the document aims to construct attack scenarios and their respective
stages. The document provides a description of the concept of C-ITS, followed
by the description of the C-ITS environment model, service model, and attack
scenario model defined by us.Comment: in Korean Language, 14 Figures, 15 Page
AI-based Attack Graph Generation
With the advancement of IoT technology, many electronic devices are
interconnected through networks, communicating with each other and performing
specific roles. However, as numerous devices join networks, the threat of
cyberattacks also escalates. Preventing and detecting cyber threats are
crucial, and one method of preventing such threats involves using attack
graphs. Attack graphs are widely used to assess security threats within
networks. However, a drawback emerges as the network scales, as generating
attack graphs becomes time-consuming. To overcome this limitation, artificial
intelligence models can be employed. By utilizing AI models, attack graphs can
be created within a short period, approximating optimal outcomes. AI models
designed for attack graph generation consist of encoders and decoders, trained
using reinforcement learning algorithms. After training the AI models, we
confirmed the model's learning effectiveness by observing changes in loss and
reward values. Additionally, we compared attack graphs generated by the AI
model with those created through conventional methods.Comment: in Korean Language, 8 Figures, 14 Page
A Vehicle Crash Simulator Using Digital Twin Technology for Synthesizing Simulation and Graphical Models
Computer vehicle simulators are used to model real-world situations to overcome time and cost limitations. The vehicle simulators provide virtual scenarios for real-world driving. Although the existing simulators precisely observe movement on the basis of good-quality graphics, they focus on a few driving vehicles instead of accident simulation. In addition, it is difficult to represent vehicle collisions. We propose a vehicle crash simulator with simulation and animation components. The proposed simulator synthesizes and simulates models of vehicles and environments. The simulator animates corresponding to the simulation through the execution results. The simulation results validate that the proposed simulator provides collision and non-collision results according to the speed of two vehicles at an intersection