Ad Hoc Vehicular Fog Enabling Cooperative Low-Latency Intrusion Detection

Internet of Vehicles and vehicular networks have been compelling targets for malicious security attacks where several intrusion detection solutions have been proposed for protecting them. Nonetheless, their main problem lies in their heavy computation, which makes them unsuitable for next-generation artificial intelligence-powered self-driving vehicles whose computational power needs to be primarily reserved for real-time driving decisions. To address this challenge, several approaches have been lately presented to take advantage of the cloud computing for offloading intrusion detection tasks to central cloud servers, thus reducing storage and processing costs on vehicles. However, centralized cloud computing entails high latency on intrusion detection related data transmission and plays against its adoption in delay-critical intelligent applications. In this context, this article proposes a vehicular-edge computing (VEC) fog-enabled scheme allowing offloading intrusion detection tasks to federated vehicle nodes located within nearby formed ad hoc vehicular fog to be cooperatively executed with minimal latency. The problem has been formulated as a multiobjective optimization model and solved using a genetic algorithm maximizing offloading survivability in the presence of high mobility and minimizing computation execution time and energy consumption. Experiments performed on resource-constrained devices within actual ad hoc fog environment illustrate that our solution significantly reduces the execution time of the detection process while maximizing the offloading survivability under different real-life scenarios.

Automated summary

The article discusses the challenges of intrusion detection in Internet of Vehicles and vehicular networks, and proposes a vehicular-edge computing (VEC) fog-enabled scheme to offload intrusion detection tasks with minimal latency. The scheme aims to maximize offloading survivability while minimizing computation execution time and energy consumption.