# Evaluation of a Reputation Management Technique for Autonomous Vehicles
## 2 Background
### Attacks on Autonomous Vehicles  
Malicious actors may seek to interfere with the operations of a transportation system network through attacks, such as traffic sign modification, GPS spoofing, distributed denial of service attacks, and injecting false information. As a vehicular network grows in size, the interconnected nature of the network increases the number of attack surfaces for malicious vehicles to exploit and magnifies the impact of attacks [22]. There are three classification parameters to consider, according to [PERSON] et al., when determining the capabilities and intentions of an attacker vehicle: insider versus outsider, malicious versus rational, and active versus passive [23]. These are summarized in Table 1.  
The different types of attacks, which might be carried out by malicious actors, also bear consideration. Key attack types, which are relevant to autonomous vehicle systems, include:  
* Fabrication: A vehicle intentionally spreads falsified warnings, road traffic information, and certificates. This type of attack also occurs when the sender pretends that they are somebody else [24]. An instance of this is a sybil attack. In this type of attack, a vehicle is able to impersonate multiple vehicles to send false data [25]. A recipient vehicle trusts the data because it is being received from numerous sources, making it more likely to be trustworthy [26]. * Denial of Service (DoS): An attacker sends large amounts of irrelevant data to other vehicles to occupy communication channels and consume computational resources [27]. The end goal of a DoS attack is to substantially interfere with or crash the network, which would significantly hamper the capability of vehicles to make decisions and navigate through their surrounding environment [28]. * Impersonation: An attacker assumes the identity of a normal vehicle in the network to inject false information [29]. By gaining control of the identity of a vehicle in the network, the attacker is able to spread malicious information by leveraging the vehicle's pre-existing reputation. This also typically results in the non-malicious vehicle being removed from the network, further impairing system operations [30]. * Eavesdropping: The goal of this attack is to steal information on other vehicles, such as location, by listening to network communications [31]. Although this does not consume the computational and communication resources of a network, like a DoS attack, it is able to access private information. This gives the attacker a significant amount of information about the vehicles in the network, which can be leveraged by other attacks [32]. * Data modification: This attack is similar to eavesdropping. However, instead of passively listening to information, attackers may also modify the data before it is received or prevent the data from reaching the intended recipient altogether [33]. \begin{table}
\begin{tabular}{c c} \hline \hline
**Attack** & **Description** \\ \hline \multirow{3}{*}{Insider vs. Outsider} & An insider vehicle is one that is authenticated in the network with the possession of a certified public key. The outsider is viewed as an intruder on the vehicular network, which immediately hinders the attack capabilities of the vehicle. \\ \hline \multirow{3}{*}{Malicious vs. Rational} & The goal of a malicious actor is to interfere with the functionality of individual vehicles or the network as a whole, with no motivation for personal gain. A rational attack seeks gain, which makes their attacks and targets more predictable. \\ \hline \multirow{3}{*}{Active vs. Passive Active vs. Passive} & The key difference between an active and passive attacker is their ability to generate packets or signals. Active attackers are able to directly influence the network, while passive attackers can only eavesdrop. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Autonomous vehicle attacks [23]. * Wormhole: An attacker funnels captured packets from one area of the network to another.