Research Domain
Our comprehensive approach to enhancing autonomous vehicle security through four key research components.
Project Overview
Our research project "Enhancing Autonomous Vehicle Security" addresses critical security challenges in the rapidly evolving field of autonomous vehicles. As vehicles become increasingly connected and autonomous, they face a growing array of security threats that could compromise safety, privacy, and functionality.
Through a comprehensive approach, we've identified four key areas that require innovative security solutions:
- Vehicle access and authentication systems
- Vehicle-to-vehicle and vehicle-to-infrastructure communications
- Hardware-level security mechanisms
- Location and navigation system security
Each research component addresses a specific vulnerability in autonomous vehicle systems, with the collective goal of creating a comprehensive security framework that can protect against both current and emerging threats.
Research Components
This component focuses on developing a smart key system using an Android application to replace traditional vehicle key fobs. The system enhances security through advanced encryption, multi-factor authentication, and role-based access control.
Key Features:
- Enhanced encryption for secure communication
- Multi-factor authentication (MFA)
- Role-based access control (RBAC)
- Offline unlocking mechanism
- Bluetooth Low Energy (BLE) for proximity detection
This component implements lightweight Elliptic Curve Cryptography (ECC) for secure Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) communications, protecting against network attacks such as black hole attacks.
Key Features:
- Lightweight ECC-based authentication
- Black-hole attack mitigation
- Trust-based security mechanism
- Performance evaluation metrics
- Scalable solution for V2V and V2I communications
This component utilizes Physical Unclonable Functions (PUFs) to create a robust challenge-response mechanism, enhancing authentication and guarding against side-channel attacks and cloning attempts.
Key Features:
- Challenge-response mechanism
- FPGA implementation
- Hardware-based security
- Protection against cloning attempts
- Resistance to side-channel attacks
This component develops a machine learning-based anomaly detection system to identify and counter GPS spoofing attacks, ensuring reliable navigation for autonomous vehicles.
Key Features:
- Machine learning anomaly detection
- Real-time GPS data analysis
- IoT-based rover implementation
- Mobile app integration for alerts
- Multi-sensor data fusion
Research Methodology
Our research follows a systematic approach that combines theoretical analysis, practical implementation, and rigorous testing:
- Literature Review: Comprehensive analysis of existing research and technologies in autonomous vehicle security.
- Problem Identification: Identifying specific security vulnerabilities and challenges in autonomous vehicle systems.
- Solution Design: Designing innovative security solutions for each identified problem area.
- Implementation: Developing prototypes and proof-of-concept implementations of the proposed solutions.
- Testing and Evaluation: Rigorous testing of the implemented solutions under various scenarios and conditions.
- Analysis and Refinement: Analyzing test results and refining the solutions based on performance metrics.
- Integration: Integrating the individual components into a comprehensive security framework.
Explore Our Research Components
Dive deeper into each of our research components to learn about the specific challenges, methodologies, and solutions.