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handle: 2117/78669 , 20.500.14243/307077
Connected vehicles promise to enable a wide range of new automotive services that will improve road safety, ease traffic management, and make the overall travel experience more enjoyable. However, they also open significant new surfaces for attacks on the electronics that control most of modern vehicle operations. In particular, the emergence of vehicle-to-vehicle (V2V) communication risks to lay fertile ground for self-propagating mobile malware that targets automobile environments. In this work, we perform a first study on the dynamics of vehicular malware epidemics in a large-scale road network, and unveil how a reasonably fast worm can easily infect thousands of vehicles in minutes. We determine how such dynamics are affected by a number of parameters, including the diffusion of the vulnerability, the penetration ratio and range of the V2V communication technology, or the worm self-propagation mechanism. We also propose a simple yet very effective numerical model of the worm spreading process, and prove it to be able to mimic the results of computationally expensive network simulations. Finally, we leverage the model to characterize the dangerousness of the geographical location where the worm is first injected, as well as for efficient containment of the epidemics through the cellular network.
Peer Reviewed
Malware (Computer software), Comunicacions mòbils, Seguretat informàtica, Àrees temàtiques de la UPC::Informàtica::Seguretat informàtica, Sistemes de, V2V communication, Comunicacions mòbils, Sistemes de, V2V, Mobile communication systems, Vehicular networks, Mobile malware
Malware (Computer software), Comunicacions mòbils, Seguretat informàtica, Àrees temàtiques de la UPC::Informàtica::Seguretat informàtica, Sistemes de, V2V communication, Comunicacions mòbils, Sistemes de, V2V, Mobile communication systems, Vehicular networks, Mobile malware
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 28 | |
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| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Top 10% | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Top 10% |
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| downloads | 248 |

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