In recent time, the standards for Vehicular Ad-hoc Networks (VANETs) and Intelligent Transportation Systems (ITSs) matured and scientific and industry interest is high especially as autonomous driving gets a lot of media attention. Autonomous driving and other assistance systems for cars make heavy use of VANETs to exchange information.They may provide more comfort, security and safety for drivers. However, it is of crucial importance for the user's trust in these assistance systems that they could not be influenced by malicious users. VANETs are likely attack vectors for such malicious users, hence application-specific security requirements must be considered during the design of applications using VANETs. In literature, many attacks on vehicular communication have been described but attacks on specific vehicular networking applications are often missing. This paper fills in this gap by describing standardized vehicular networking applications, defining and extending previous attacker models, and using the resulting new models to characterize the possible attackers interested in the specific vehicular networking application. The attacker models presented in this paper hopefully provide great benefit for the scientific community and industry as they allow to compare security evaluations of different works, characterize attackers, their intentions and help to plan application-specific security controls for vehicular networking applications.
A realistic attack scenario is the exploitation of low level software or hardware vulnerabilities in the network stacks of wireless transceivers. The existence and importance of these vulnerabilities has been discussed in various publications, @cite_18 @cite_1 @cite_11 @cite_15 . This scenario marks the lower bound of attack scenarios that are discussed in this paper. While still being relevant specifically to wireless communication, cellular or ad-hoc, it is also not specific to only one vehicular networking application and the root cause of vulnerable soft- and hardware proliferates through all the layers of current systems and is not specific to wireless communications. Therefore, this is not in focus for this publication. Instead, the main contribution is the combination and extension of previous attacker models by @cite_4 @cite_8 @cite_22 and the detailed description of realistic attacker models via the extended model. Most of the previous works @cite_12 @cite_17 @cite_7 @cite_21 @cite_16 , are missing realistic attacker models. Some like @cite_17 @cite_7 @cite_21 use categories of attacks, like impersonation, data tampering, sybil, or DOS attacks and describe each attacker based on its category. @cite_16 is really close to defining realistic attacker models by defining categories of attackers, like driver, road side or infrastructure.
@cite_18 @cite_4 @cite_22 @cite_7 @cite_8 @cite_21 @cite_1 @cite_17 @cite_15 @cite_16 @cite_12 @cite_11
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In wireless networking R&D we typically depend on experimentation to further evaluate a solution, as simulation is inherently a simplification of the real-world. However, experimentation is limited in aspects where simulation excels, such as repeatability and reproducibility. Real wireless experiments are hardly repeatable. Given the same input they can produce very different output results, since wireless communications are influenced by external random phenomena such as noise, interference, and multipath. Real experiments are also difficult to reproduce due to testbed operational constraints and availability. We have previously proposed the Trace-based Simulation (TS) approach, which uses the TraceBasedPropagationLossModel to successfully reproduce past experiments. Yet, in its current version, the TraceBasedPropagationLossModel only supports point-to-point scenarios. In this paper, we introduce a new version of the model that supports Multiple Access wireless scenarios. To validate the new version of the model, the network throughput was measured in a laboratory testbed. The experimental results were then compared to the network throughput achieved using the ns-3 trace-based simulation and a pure ns-3 simulation, confirming the TS approach is valid for multiple access scenarios too. This paper describes a new class of attacks specific to cognitive radio networks. Wireless devices that can learn from their environment can also be taught things by malicious elements of their environment. By putting artificial intelligence in charge of wireless network devices, we are allowing unanticipated, emergent behavior, fitting a perhaps distorted or manipulated level of optimality. The state space for a cognitive radio is made up of a variety of learned beliefs and current sensor inputs. By manipulating radio sensor inputs, an adversary can affect the beliefs of a radio, and consequently its behavior. In this paper we focus primarily on PHY-layer issues, describing several classes of attacks and giving specific examples for dynamic spectrum access and adaptive radio scenarios. These attacks demonstrate the capabilities of an attacker who can manipulate the spectral environment when a radio is learning. The most powerful of which is a self-propagating AI virus that could interactively teach radios to become malicious. We then describe some approaches for mitigating the effectiveness of these attacks by instilling some level of "common sense" into radio systems, and requiring learned beliefs to expire and be relearned. Lastly we provide a road-map for extending these ideas to higher layers in the network stack. Even well administered networks are vulnerable to attack. Recent work in network security has focused on the fact that combinations of exploits are the typical means by which an attacker breaks into a network. Researchers have proposed a variety of graph-based algorithms to generate attack trees (or graphs). Either structure represents all possible sequences of exploits, where any given exploit can take advantage of the penetration achieved by prior exploits in its chain, and the final exploit in the chain achieves the attacker's goal. The most recent approach in this line of work uses a modified version of the model checker NuSMV as a powerful inference engine for chaining together network exploits, compactly representing attack graphs, and identifying minimal sets of exploits. However, it is also well known that model checkers suffer from scalability problems, and there is good reason to doubt whether a model checker can handle directly a realistic set of exploits for even a modest-sized network. In this paper, we revisit the idea of attack graphs themselves, and argue that they represent more information explicitly than is necessary for the analyst. Instead, we propose a more compact and scalable representation. Although we show that it is possible to produce attack trees from our representation, we argue that more useful information can be produced, for larger networks, while bypassing the attack tree step. Our approach relies on an explicit assumption of monotonicity, which, in essence, states that the precondition of a given exploit is never invalidated by the successful application of another exploit. In other words, the attacker never needs to backtrack. The assumption reduces the complexity of the analysis problem from exponential to polynomial, thereby bringing even very large networks within reach of analysis Machine learning (ML) models, e.g., deep neural networks (DNNs), are vulnerable to adversarial examples: malicious inputs modified to yield erroneous model outputs, while appearing unmodified to human observers. Potential attacks include having malicious content like malware identified as legitimate or controlling vehicle behavior. Yet, all existing adversarial example attacks require knowledge of either the model internals or its training data. We introduce the first practical demonstration of an attacker controlling a remotely hosted DNN with no such knowledge. Indeed, the only capability of our black-box adversary is to observe labels given by the DNN to chosen inputs. Our attack strategy consists in training a local model to substitute for the target DNN, using inputs synthetically generated by an adversary and labeled by the target DNN. We use the local substitute to craft adversarial examples, and find that they are misclassified by the targeted DNN. To perform a real-world and properly-blinded evaluation, we attack a DNN hosted by MetaMind, an online deep learning API. We find that their DNN misclassifies 84.24 of the adversarial examples crafted with our substitute. We demonstrate the general applicability of our strategy to many ML techniques by conducting the same attack against models hosted by Amazon and Google, using logistic regression substitutes. They yield adversarial examples misclassified by Amazon and Google at rates of 96.19 and 88.94 . We also find that this black-box attack strategy is capable of evading defense strategies previously found to make adversarial example crafting harder.