EVSOAR: Security Orchestration, Automation and Response via EV Charging Stations
- URL: http://arxiv.org/abs/2503.16984v1
- Date: Fri, 21 Mar 2025 09:48:29 GMT
- Title: EVSOAR: Security Orchestration, Automation and Response via EV Charging Stations
- Authors: Tadeu Freitas, Erick Silva, Rehana Yasmin, Ali Shoker, Manuel E. Correia, Rolando Martins, Paulo Esteves-Verissimo,
- Abstract summary: Vehicle cybersecurity has emerged as a critical concern, driven by the innovation in the automotive industry.<n>Current efforts to address these challenges are constrained by the limited computational resources of vehicles.
- Score: 0.0
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: Vehicle cybersecurity has emerged as a critical concern, driven by the innovation in the automotive industry, e.g., automomous, electric, or connnected vehicles. Current efforts to address these challenges are constrained by the limited computational resources of vehicles and the reliance on connected infrastructures. This motivated the foundation of Vehicle Security Operations Centers (VSOCs) that extend IT-based Security Operations Centers (SOCs) to cover the entire automotive ecosystem, both the in-vehicle and off-vehicle scopes. Security Orchestration, Automation, and Response (SOAR) tools are considered key for impelementing an effective cybersecurity solution. However, existing state-of-the-art solutions depend on infrastructure networks such as 4G, 5G, and WiFi, which often face scalability and congestion issues. To address these limitations, we propose a novel SOAR architecture EVSOAR that leverages the EV charging stations for connectivity and computing to enhance vehicle cybersecurity. Our EV-specific SOAR architecture enables real-time analysis and automated responses to cybersecurity threats closer to the EV, reducing the cellular latency, bandwidth, and interference limitations. Our experimental results demonstrate a significant improvement in latency, stability, and scalability through the infrastructure and the capacity to deploy computationally intensive applications, that are otherwise infeasible within the resource constraints of individual vehicles.
Related papers
- Leveraging Edge Intelligence and LLMs to Advance 6G-Enabled Internet of Automated Defense Vehicles [6.294884163829944]
This work presents opportunities and challenges with a vision of realizing the full potential of these technologies in critical defense applications.<n>The advent of 6G strengthens the Internet of Automated Defense Vehicles (IoADV) concept within the realm of Internet of Military Defense Things (IoMDT)
arXiv Detail & Related papers (2024-12-28T23:07:25Z) - RISC-V Needs Secure 'Wheels': the MCU Initiator-Side Perspective [2.7605172967806237]
The automotive industry is experiencing a massive paradigm shift.
Cars are becoming increasingly autonomous, connected, and computerized.
This development drives (cyber-security) requirements in cars, and has paved the way for the release of the new security certification standard ISO21434.
Ricardo-V has great potential to transform automotive computing systems, but we argue that current ISA/extensions are not ready yet.
arXiv Detail & Related papers (2024-10-13T13:38:57Z) - A Learning-based Incentive Mechanism for Mobile AIGC Service in Decentralized Internet of Vehicles [49.86094523878003]
We propose a decentralized incentive mechanism for mobile AIGC service allocation.
We employ multi-agent deep reinforcement learning to find the balance between the supply of AIGC services on RSUs and user demand for services within the IoV context.
arXiv Detail & Related papers (2024-03-29T12:46:07Z) - SISSA: Real-time Monitoring of Hardware Functional Safety and
Cybersecurity with In-vehicle SOME/IP Ethernet Traffic [49.549771439609046]
We propose SISSA, a SOME/IP communication traffic-based approach for modeling and analyzing in-vehicle functional safety and cyber security.
Specifically, SISSA models hardware failures with the Weibull distribution and addresses five potential attacks on SOME/IP communication.
Extensive experimental results show the effectiveness and efficiency of SISSA.
arXiv Detail & Related papers (2024-02-21T03:31:40Z) - Cyber-Twin: Digital Twin-boosted Autonomous Attack Detection for Vehicular Ad-Hoc Networks [8.07947129445779]
The rapid evolution of Vehicular Ad-hoc NETworks (VANETs) has ushered in a transformative era for intelligent transportation systems (ITS)
VANETs are increasingly susceptible to cyberattacks, such as jamming and distributed denial of service (DDoS) attacks.
Existing methods face difficulties in detecting dynamic attacks and integrating digital twin technology and artificial intelligence (AI) models to enhance VANET cybersecurity.
This study proposes a novel framework that combines digital twin technology with AI to enhance the security of RSUs in VANETs.
arXiv Detail & Related papers (2024-01-25T08:05:41Z) - DynamiQS: Quantum Secure Authentication for Dynamic Charging of Electric Vehicles [61.394095512765304]
Dynamic Wireless Power Transfer (DWPT) is a novel technology that allows charging an electric vehicle while driving.
Recent advancements in quantum computing jeopardize classical public key cryptography.
We propose DynamiQS, the first post-quantum secure authentication protocol for dynamic wireless charging.
arXiv Detail & Related papers (2023-12-20T09:40:45Z) - Autonomous Vehicles an overview on system, cyber security, risks,
issues, and a way forward [0.0]
This chapter explores the complex realm of autonomous cars, analyzing their fundamental components and operational characteristics.
The primary focus of this investigation lies in the realm of cybersecurity, specifically in the context of autonomous vehicles.
A comprehensive analysis will be conducted to explore various risk management solutions aimed at protecting these vehicles from potential threats.
arXiv Detail & Related papers (2023-09-25T15:19:09Z) - Reinforcement Learning based Cyberattack Model for Adaptive Traffic
Signal Controller in Connected Transportation Systems [61.39400591328625]
In a connected transportation system, adaptive traffic signal controllers (ATSC) utilize real-time vehicle trajectory data received from vehicles to regulate green time.
This wirelessly connected ATSC increases cyber-attack surfaces and increases their vulnerability to various cyber-attack modes.
One such mode is a'sybil' attack in which an attacker creates fake vehicles in the network.
An RL agent is trained to learn an optimal rate of sybil vehicle injection to create congestion for an approach(s)
arXiv Detail & Related papers (2022-10-31T20:12:17Z) - Learning energy-efficient driving behaviors by imitating experts [75.12960180185105]
This paper examines the role of imitation learning in bridging the gap between control strategies and realistic limitations in communication and sensing.
We show that imitation learning can succeed in deriving policies that, if adopted by 5% of vehicles, may boost the energy-efficiency of networks with varying traffic conditions by 15% using only local observations.
arXiv Detail & Related papers (2022-06-28T17:08:31Z) - Differentiable Control Barrier Functions for Vision-based End-to-End
Autonomous Driving [100.57791628642624]
We introduce a safety guaranteed learning framework for vision-based end-to-end autonomous driving.
We design a learning system equipped with differentiable control barrier functions (dCBFs) that is trained end-to-end by gradient descent.
arXiv Detail & Related papers (2022-03-04T16:14:33Z) - Smart and Secure CAV Networks Empowered by AI-Enabled Blockchain: Next
Frontier for Intelligent Safe-Driving Assessment [17.926728975133113]
Securing a safe-driving circumstance for connected and autonomous vehicles (CAVs) continues to be a widespread concern.
We propose a novel framework of algorithm-enabled intElligent Safe-driving assessmenT (BEST) to offer a smart and reliable approach.
arXiv Detail & Related papers (2021-04-09T19:08:34Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.