Detection of cyber attacks against vehicles is of growing interest. As vehicles typically afford limited processing resources, proposed solutions are rule-based or lightweight machine learning techniques. We argue that this limitation can be lifted with computational offloading commonly used for resource-constrained mobile devices. The increased processing resources available in this manner allow access to more advanced techniques. Using as case study a small four-wheel robotic land vehicle, we demonstrate the practicality and benefits of offloading the continuous task of intrusion detection that is based on deep learning. This approach achieves high accuracy much more consistently than with standard machine learning techniques and is not limited to a single type of attack or the in-vehicle CAN bus as previous work. As input, it uses data captured in realtime that relate to both cyber and physical processes, which it feeds as time series data to a neural network architecture. We use both a deep multilayer perceptron and a recurrent neural network architecture, with the latter benefitting from a long-short term memory hidden layer, which proves very useful for learning the temporal context of different attacks. We employ denial of service, command injection and malware as examples of cyber attacks that are meaningful for a robotic vehicle. The practicality of computation offloading depends on the resources afforded onboard and remotely, and the reliability of the communication means between them. Using detection latency as the criterion, we have developed a mathematical model to determine when computation offloading is beneficial given parameters related to the operation of the network and the processing demands of the deep learning model. The more reliable the network and the greater the processing demands, the greater the reduction in detection latency achieved through offloading.

Current and future multimedia networks require connections under specific quality of service (QoS) constraints which can no longer be provided by the best-effort Internet. Therefore, ‘smarter’ networks have been proposed in order to cover this need. The cognitive packet network (CPN) is a routing protocol that provides QoS-driven routing and performs self-improvement in a distributed manner, by learning from the experience of special packets, which gather on-line QoS measurements and discover new routes. The CPN was first introduced in 1999 and has been used in several applications since then. Here we provide a comprehensive survey of its variations, applications and experimental performance evaluations.

Smart networks have grown out of the need for stable, reliable, and predictable networks that will guarantee packet delivery under Quality of Service (QoS) constraints. In this article we present a measurement-based admission control algorithm that helps control traffic congestion and guarantee QoS throughout the lifetime of a connection. When a new user requests to enter the network, probe packets are sent from the source to the destination to estimate the impact that the new connection will have on the QoS of both the new and the existing users. The algorithm uses a novel algebra of QoS metrics, inspired by Warshall's algorithm, to look for a path with acceptable QoS values to accommodate the new flow. We describe the underlying mathematical principles and present experimental results obtained by evaluating the method in a large laboratory test-bed operating the Cognitive Packet Network (CPN) protocol.