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1. REPLACE: Real-time Security Assurance in Vehicular Platoons Against V2V Attacks

   Boddupalli, S ; Hegde, A ; Ray, S. ( January 2021 , 24th IEEE International Conference on Intelligent Transportation (ITSC 2021))

   null (Ed.)

   Connected Autonomous Vehicular (CAV) platoon refers to a group of vehicles that coordinate their movements and operate as a single unit. The vehicle at the head acts as the leader of the platoon and determines the course of the vehicles following it. The follower vehicles utilize Vehicle-to-Vehicle (V2V) communication and automated driving support systems to automatically maintain a small fixed distance between each other. Reliance on V2V communication exposes platoons to several possible malicious attacks which can compromise the safety, stability, and efficiency of the vehicles. We present a novel distributed resiliency architecture, RePLACe for CAV platoon vehicles to defend against adversaries corrupting V2V communication reporting preceding vehicle position. RePLACe is unique in that it can provide real-time defense against a spectrum of communication attacks. RePLACe provides systematic augmentation of a platoon controller architecture with real-time detection and mitigation functionality using machine learning. Unlike computationally intensive cryptographic solutions RePLACe accounts for the limited computation capabilities provided by automotive platforms as well as the real-time requirements of the application. Furthermore, unlike control-theoretic approaches, the same framework works against the broad spectrum of attacks. We also develop a systematic approach for evaluation of resiliency of CAV applications against V2V attacks. We perform extensive experimental evaluation to demonstrate the efficacy of RePLACe. 

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2. Convoy: Physical Context Verification for Vehicle Platoon Admission

   https://doi.org/10.1145/3032970.3032987  

   Han, Jun ; Harishankar, Madhumitha ; Wang, Xiao ; Chung, Albert Jin ; Tague, Patrick ( February 2017 , Proc. 18th Int'l Workshop on Mobile Computing Systems and Applications (HotMobile))

   Truck platooning is emerging as a promising solution with many economic incentives. However, securely admitting a new vehicle into a platoon is an extremely important yet difficult task. There is no adequate method today for verifying physical arrangements of vehicles within a platoon formation. Specifically, we address the problem of a platoon ghost attack wherein an attacker spoofs presence within a platoon to gain admission and subsequently execute malicious attacks. To address such concerns, we present Convoy, a novel autonomous platoon admission scheme which binds the vehicles' digital certificates to their physical context (i.e., locality). Convoy exploits the findings that vehicles traveling together experience similar context to prove to each other over time that they are co-present. Specifically, they experience similar road (e.g., bumps and cracks) and traffic (e.g., acceleration and steering) conditions. Our approach is based on the ability for vehicles to capture this context, generate fingerprints to establish shared keys, and later bind these symmetric keys to their public keys. We design and implement the Convoy protocol and evaluate it with real-world driving data. Our implementation demonstrates that vehicles traveling in adjacent lanes can be sufficiently distinguished by their context and this can be utilized to thwart platoon ghost attacks and similar misbehavior 

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3. Vehicle-to-Vehicle Charging Coordination over Information Centric Networking

   https://doi.org/10.1109/LCN53696.2022.9843207  

   Thompson, Robert ; Ismail, Muhammad ; Shannigrahi, Susmit ( September 2022 , IEEE LCN)

   Cities around the world are increasingly promoting electric vehicles (EV) to reduce and ultimately eliminate greenhouse gas emissions. A huge number of EVs will put unprecedented stress on the power grid. To efficiently serve the increased charging load, these EVs need to be charged in a coordinated fashion. One promising coordination strategy is vehicle-to-vehicle (V2V) charging coordination, enabling EVs to sell their surplus energy in an ad-hoc, peer to peer manner. This paper introduces an Information Centric Networking (ICN)-based protocol to support ad-hoc V2V charging coordination (V2V-CC). Our evaluations demonstrate that V2V-CC can provide added flexibility, fault tolerance, and reduced communication latency than a conventional centralized cloud based approach. We show that V2V-CC can achieve a 93% reduction in protocol completion time compared to a conventional approach. We also show that V2V-CC also works well under extreme packet loss, making it ideal for V2V charging coordination. 

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4. On the Elliptic Curve Cryptography for Privacy-Aware Secure ACO-AODV Routing in Intent-Based Internet of Vehicles for Smart Cities

   https://doi.org/10.1109/TITS.2020.3008361  

   Safavat, Sunitha ; Rawat, Danda B. ( June 2020 , IEEE Transactions on Intelligent Transportation Systems)

   null (Ed.)

   Internet of Vehicles (IoV) in 5G is regarded as a backbone for intelligent transportation system in smart city, where vehicles are expected to communicate with drivers, with road-side wireless infrastructure, with other vehicles, with traffic signals and different city infrastructure using vehicle-to-vehicle (V2V) and/or vehicle-to-infrastructure (V2I) communications. In IoV, the network topology changes based on drivers' destination, intent or vehicles' movements and road structure on which the vehicles travel. In IoV, vehicles are assumed to be equipped with computing devices to process data, storage devices to store data and communication devices to communicate with other vehicles or with roadside infrastructure (RSI). It is vital to authenticate data in IoV to make sure that legitimate data is being propagated in IoV. Thus, security stands as a vital factor in IoV. The existing literature contains some limitations for robust security in IoV such as high delay introduced by security algorithms, security without privacy, unreliable security and reduced overall communication efficiency. To address these issues, this paper proposes the Elliptic Curve Cryptography (ECC) based Ant Colony Optimization Ad hoc On-demand Distance Vector (ACO-AODV) routing protocol which avoids suspicious vehicles during message dissemination in IoV. Specifically, our proposed protocol comprises three components: i) certificate authority (CA) which maps vehicle's publicly available info such as number plates with cryptographic keys using ECC; ii) malicious vehicle (MV) detection algorithm which works based on trust level calculated using status message interactions; and iii) secure optimal path selection in an adaptive manner based on the intent of communications using ACO-AODV that avoids malicious vehicles. Experimental results illustrate that the proposed approach provides better results than the existing approaches. 

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5. Multipoint channel charting-based radio resource management for V2V communications

   https://doi.org/10.1186/s13638-020-01723-3  

   Al-Tous, Hanan ; Ponnada, Tushara ; Studer, Christoph ; Tirkkonen, Olav ( December 2020 , EURASIP Journal on Wireless Communications and Networking)

   Abstract We consider a multipoint channel charting (MPCC) algorithm for radio resource management (RRM) in vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication systems. A massive MIMO (mMIMO) infrastructure network performs logical localization of vehicles to a MPCC, based on V2I communication signals. Combining logical distances given by channel charting with V2V measurements, the network trains a function to predict the quality of a direct V2V communication link from observed V2I communication signals. In MPCC, the network uses machine learning techniques to learn a logical radio map from V2I channel state information (CSI) samples transmitted from unknown locations. The network extracts CSI features, constructs a dissimilarity matrix between CSI samples, and performs dimensional reduction of the CSI feature space. Here, we use Laplacian Eigenmaps (LE) for dimensional reduction. The resulting MPCC is a two-dimensional map where the spatial distance between a pair of vehicles is closely approximated by the distance in the MPCC. In addition to V2I CSI, the network acquires V2V channel quality information for vehicles in the training set and develops a link quality predictor. MPCC provides a mapping for any vehicle location in the training set. To use MPCC for cognitive RRM of V2I and V2V communications, network management has to find logical MPCC locations for vehicles not in the training set, based on newly acquired V2I CSI measurements. For this, we develop an extension of LE-based MPCC to out-of-sample CSI samples. We evaluate the performance of link quality prediction for V2V communications in a mMIMO millimeter-wave scenario, in terms of the relative error of the predicted outage probability. 

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