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ACM Workshop on Wireless Security and Machine Learning (WiseML)

A Key Generation Scheme for IoV Communication Based on Neural Network Autoencoders

Liquan Chen, Han Wang, Tianyu Lu, Zeyu Xu

Published Jun 1, 2023• 2 citations
Publisher

Abstract

In recent years, the Internet of Vehicles (IoV) has become more and more widely used. Due to the high dynamic and point-to-point characteristics of IoV communication, IoV needs a secure and effective key generation mechanism. Physical layer key generation has become a promising technology, known for its lightweight and information theory security. IoV communication is usually realized using Wi-Fi, ZigBee, LoRa and other technologies. Based on ESP32 device, this paper explores the use of WiFi communication in the vehicle-to-everything (V2X) scenario of IoV. Focusing on the V2X scenario, we conduct channel modeling based on line of sight (LoS) and multipath fading and present Secure-Vehicle-Key, which is an environment-adaptive key generation scheme using neural network autoencoders. This scheme can realize the dynamic balance of reliability and confidentiality to meet the requirements of different vehicle network situations. Compared with the reconciliation scheme implemented by Slepian-Wolf low-density parity check (LDPC) codes, the method in this paper reduces the bit disagreement rate (BDR) of key generation by 30%-40% and passes the NIST randomness test with excellent results.

DOI
10.1145/3586209.3591397

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In recent years, the Internet of Vehicles (IoV) has become more and more widely used. Due to the high dynamic and point-to-point characteristics of IoV communication, IoV needs a secure and effective key generation mechanism. Physical layer key generation has become a promising technology, known for its lightweight and information theory security. IoV communication is usually realized using Wi-Fi, ZigBee, LoRa and other technologies. Based on ESP32 device, this paper explores the use of WiFi communication in the vehicle-to-everything (V2X) scenario of IoV. Focusing on the V2X scenario, we conduct channel modeling based on line of sight (LoS) and multipath fading and present Secure-Vehicle-Key, which is an environment-adaptive key generation scheme using neural network autoencoders. This scheme can realize the dynamic balance of reliability and confidentiality to meet the requirements of different vehicle network situations. Compared with the reconciliation scheme implemented by Slepian-Wolf low-density parity check (LDPC) codes, the method in this paper reduces the bit disagreement rate (BDR) of key generation by 30%-40% and passes the NIST randomness test with excellent results.
Recent years have witnessed the remarkable growth of the Internet of Vehicles (IoV). Due to the high dynamics and ad-hoc nature of IoV communication, the lack of effective secret key establishment in IoV remains a security bottleneck. Physical layer key generation has emerged as a promising technology to establish a pair of cryptographic keys in a lightweight and information-theoretic secure way. However, prior works mainly focus on legacy communication technologies such as Wi-Fi, ZigBee, and 5G which can only achieve short range IoV communications. The emergence of Long-range (LoRa) communication technology that features long-range, low power, and extremely low data rate, brings new challenges for key generation in long range IoV scenarios. In this paper, we present Vehicle-Key, which is a secret key generation system to secure LoRa-enabled IoV communications. In Vehicle-Key, we design a novel deep learning model that can achieve channel prediction and quantization simultaneously. Additionally, we propose an autoencoder-based reconciliation method that improves the key agreement rate significantly. Extensive real-world experiments show that Vehicle-Key improves the key agreement rate by 15.10%–49.81% and key generation rate by 9–14× compared with the state-of-the-art. Security analysis demonstrates that Vehicle-Key is secure against several common attacks. Moreover, we implement Vehicle-Key on a Raspberry Pi and show that it can be executed in 3.4 ms.
The Internet of Vehicles (IoV) technology has recently emerged as a popular area of research, and vehicle-to-cloud communication forms the basis of the IoV. This process involves uploading a large amount of information related to the vehicle and its location. The security of vehicle-to-cloud communication thus has a significant impact on the safety of the IoV. This paper proposes a vehicle-to-cloud communication system based on lightweight authentication and extended Quantum Key Distribution. The system supports the wireless distribution of session keys between vehicles and the Quantum Cloud Server (QCS) by using a WAN (Wide Area Network), and supports the wired transmission of quantum keys between the QCS and the Telematics Service Provider (TSP) through a LAN (Local Area Network) by using the BB84 protocol. The system can implement encrypted communication based on quantum physical security in a hybrid network. Moreover, we propose a lightweight identity authentication scheme. An analysis of its performance shows that the proposed system requires a small amount of computation and incurs a low communication overhead. This scheme is applicable to most Internet devices with low computing and communication capacity, and can potentially be used in the IoV framework. We conducted simulations of the proposed system and experiments to test its performance. The maximum average delay in communication in experiments was 40.681 ms and the maximum rate of packet loss was 0.1222%. The proposed system can thus ensure safe and efficient IoV communication.
With the rise of connected vehicular networks, Vehicle-to-Vehicle (V2V) communication has become a fundamental enabler for intelligent transportation systems. However, the open nature of wireless channels introduces risks such as eavesdropping and key compromise. To address these issues, we propose a secure V2V communication scheme based on Physical Layer Key Generation (PLKG) using LoRa technology. The proposed system utilizes Received Signal Strength Indicator (RSSI) measurements between two moving vehicles as the entropy source. To improve the channel correlation, a Savitzky-Golay filter is applied as a preprocessing step. Adaptive multibit quantization is then used to convert the RSSI into binary sequences, followed by the Cascade algorithm for error reconciliation. To enhance randomness, privacy amplification is applied using universal hashing and SHA-1. Finally, the generated key is used for AES-128 encryption. The system is implemented on Raspberry Pi 4B using AcSiP EK-S76SXB and RAK2245 LoRa modules. Experimental validation was conducted in three movement scenarios firstly both vehicle move from opposite-direction at10 km/h\mathbf{1 0 ~ k m} / \mathbf{h}10 km/hfor300\mathbf{3 0 0}300meters, secondly both vehicle move at the same-direction at10 km/h\mathbf{1 0 ~ k m} / \mathbf{h}10 km/hfor800\mathbf{8 0 0}800meters, and lastly same as the second scenario but with different speed at20 km/h20 ~\text{km} / \mathrm{h}20 km/h. Results shown that the system achieves a reliable key generation rate (KGR) of up to 176075.73 bit/s while maintaining low computation time, demonstrating its feasibility for secure real-time V2V communication.
Devices in the Internet of Things (IoT) are usually limited in computing resources and energy capacity, which means that encryption schemes with higher complexity are not suitable for them to ensure secure communication. As a promising solution to this problem, physical layer key generation suggests that shared secret keys can be generated from noisy wireless channel measurements to enhance the security of wireless communications. In this article, we propose a key generation scheme with extremely low implementation complexity, which allows physical layer key generation to be implemented on IoT nodes. First, we preprocess the channel measurements with simple moving average filtering before quantization to improve channel reciprocity. Next, a bidirectional difference quantization scheme is proposed to realize reliable quantization of channel measurements, which is ingenious in that the quantization process does not depend on quantization thresholds, and thus, the mismatched key bits caused by measurements close to quantization thresholds can be effectively avoided. Then, we propose an improved Cascade protocol to achieve lightweight and efficient information reconciliation. The simulation results show that our scheme can well balance the reliability and efficiency of key generation, and has excellent performance in terms of implementation complexity and key randomness.
With the rapid development of intelligent connected vehicles, networked vehicles carry a large amount of sensitive data. Secure authentication and controlled access to data in the Internet of Vehicles (IoV) are essential to protect vehicular private data. We propose a quantum-key-based authentication and key agreement scheme for IoV, called QKBAKA. Its features are as follows: 1) Role-Based Data Access Control Strategy: Access to private data requires authorization and management by vehicle owners. 2) Vehicle privatization manages quantum session keys. The quantum random number generator (QRNG) deployed on the vehicle side generates quantum session keys and realizes secure agreement through multilevel quantum keys. The multilevel quantum key includes the quantum prefilled key, the quantum protection key and the quantum session key. 3) Secure and Efficient Authentication Scheme: QKBAKA uses quantum keys, symmetric encryption algorithms, and hash algorithms. It is relatively secure for quantum computing and has better performance. Security analysis shows that QKBAKA is secure under the real-or-random (ROR) model and meets the security requirements of IoV. Performance evaluation shows that, compared with the existing schemes, QKBAKA reduces computation and communication costs by 74.83%–98.59% and 28.7%–83.1%. Simulation and real vehicle tests show that QKBAKA has good communication stability and feasibility.
The vehicle to everything (V2X) serves as a crucial foundation for future intelligent transportation systems. Security concerns within the V2X have garnered significant attention and key generation from wireless channels have emerged as a promising technique. However, applying key generation to V2X is quite challenging because the fast moving vehicles result in very small coherence time and impact channel measurements correlation. This paper designed a practical V2X key generation by enhancing channel state information (CSI) reciprocity and carried out extensive experimental evaluation. In particular, the designed key generation consists of channel probing, CSI preprocessing, CSI compensation and key establishment. In the channel probing, we deliberately reduced the time delay between uplink and downlink transmissions, to allow almost simultaneous measurements. We then carefully designed CSI preprocessing to remove hardware carrier leakage and eliminate noise effects. Furthermore, we devised CSI compensation by using interpolation or deep learning prediction to further improve the reciprocity. Finally, key establishment converted the measured CSI into binary sequences and reconcile on a common key via low-density parity-check (LDPC) code. We adopted universal software radio peripheral (USRP) X310 platforms for channel measurements and implemented the above algorithms. We carried out extensive experiments in real-road environments with various vehicle speeds. These carefully designed algorithms enabled our system working robustly even in high mobility scenarios, e.g., 40 km/h. Experimental results demonstrated common and random key can be generated with a key block error rate (BER) less than 0.1.