In recent years the emergence of Spiking Neural Net- works (SNNs) has shown that these networks are a promis- ing alternative to traditional Artificial Neural Networks (ANNs) due to their low-power computing capabilities and noise robustness. Nevertheless, in recent approaches, they have either strayed away from spike time backpropaga- tion, used discrete time, single spike neurons, and/or limited themselves to shallow networks. These approaches limit the potential of SNNs by sacrificing significant aspects of what makes them special in order to have a functional network. It is for this reason that we believe that, the implemen- tation of residual connections, allowing a model that has multi-spiking neurons, precise time and spike time back- propagation is the path to allow these networks to truly shine. As it will solve one of this model’s most severe lim- itations which prevent it from being used to build deeper networks, the banishing of spikes at a deeper depth. Hence we aim to remedy this problem by feeding inputs from fur- ther up the network to revitalize the spike counts at deeper layers. In this paper, we explore the implementation of residual connections in precise time multi-spiking neural networks as a way of solving the disappearing spikes problem that inhibits spike time backpropagation. We explore two al- ternatives in the implementation of the residual connection and we analyze how they affect the accuracy of the network as the depth increases in both Multi-Layer Perceptrons and Convolutional Neural Networks. We also developed an ar- chitecture to allow for swapping of the fuse function in or- der to take full advantage of the flexibility that precise time provides us. Results show that the implemented residual connections allow for deeper training, which has the poten- tial to aid in the network’s performance, although in some cases some hurdles remain.

Software-Defined Networks (SDNs) are a promising new network design paradigm that allows for better control of the network. But as with any new software implementation, there are new security concerns that arise. In the past there have been various papers covering specific side-channel attacks on SDNs; most of them consist of either using time delays in operations to create a covert communication channel between two compromised hosts or using proving packets and their response times to determine the flow rules and configuration of the network.
This paper intents to investigate the impact of different types of side-channel attacks in SDN scenarios. Provide a survey on the state-of-the-art solutions that are proposed to address the side-channel attacks in SDN. Particularly, identifying different ways through which an adversary can launch side-channel attacks, and the different entities and network metrics that are impacted by a specific side-channel attack. Next, identify and survey the solutions available in the state-of-the-art that tackle side-channel attacks. Finally, propose new possible improved solutions to the issue of side-channel attacks in SDNs and future research on the field.
We conclude that current side-channel attacks target the separation control and data planes at the core of the SDN paradigm, by exploiting the response delay created by a centralized logic system in the controller. This as seen can be exploited in two main ways related to the two main attack categories mentioned in this paper: teleportation attacks and recognisance attacks.