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Abstract-In this article, we design and evaluate the proposed Geographic-based Spray-and-Relay (GSaR) routing scheme in Delay/Disruption Tolerant Networks (DTNs). To the best of our knowledge, GSaR is the first spray based geographic routing scheme using the historical geographic information for making routing decision. Here, the term spray means only a limited number of message copies are allowed for replication in the network.By estimating a movement range of destination via the historical geographic information, GSaR expedites message being sprayed towards this range, meanwhile prevents that away from and postpones that out of this range. As such, the combination of them intends to fast and efficiently spray the limited number of message copies towards this range, and effectively spray them within range, in order to reduce the delivery delay and increase the delivery ratio. Furthermore, GSaR exploits Delegation Forwarding (DF) to enhance the reliability of routing decision and handle the local maximum problem, considered as the challenges for applying geographic routing scheme in sparse networks. We evaluate GSaR under three city scenarios abstracted from real world, with other routing schemes for comparison. Results show that GSaR is reliable for delivering messages before expiration deadline and efficient for achieving low routing overhead ratio. Further observation indicates that GSaR is also efficient in terms of a low and fair energy consumption over the nodes in the network.

Abstract-

Deep neural networks have been used for traffic classifications and promising results have been obtained. However, most of the previous work confined to one specific task of the classification, where restricts the classifier potential performance and application areas. The traffic flow can be labeled from a different perspective which might help to improve the accuracy of classifier by exploring more meaningful latent features. In addition, deep neural network (DNN)-based model is hard to adapt the changes in new classification demand, because of training such a new model costing not only many computing resources but also lots of labeled data. For this purpose, we proposed a multi-output DNN model simultaneously learning multi-task traffic classifications. In this model, the common knowledge of traffic is exploited by the synergy among the tasks and improves the performance of each task separately. Also, it is showed that this structure shares the potential of meeting new demands in the future and meanwhile being able to achieve the classification with advanced speed and fair accuracy. One-shot learning, which refers to the learning process with scarce data, is also explored and our approach shows notable performance. INDEX TERMS Traffic classification, multi-task model, transfer learning, one-shot learning. I. INTRODUCTION

Few-shot learning (FSL) is a core topic in the domain of machine learning (ML), in which the focus is on the use of small datasets to train the model. In recent years, there have been many important data-driven ML applications for intrusion detection. Despite these great achievements, however, gathering a large amount of reliable data remains expensive and time-consuming, or even impossible. In this regard, FSL has been shown to have advantages in terms of processing small, abnormal data samples in the huge application space of intrusion detection. FSL can improve ML for scarce data at three levels: the data, the model, and the algorithm levels. Previous knowledge plays an important role in all three approaches. Many promising methods such as data enrichment, the graph neural network model, and multitask learning have also been developed. In this paper, we present a comprehensive review of the latest research progress in the area of FSL. We first introduce the theoretical background to ML and FSL and then describe the general features, advantages, and main methods of FSL. FSL methods such as embedded learning, multitask learning, and generative models are applied to intrusion detection to improve the detection accuracy effectively. Then, the application of FSL to intrusion detection is reviewed in detail, including enriching the dataset by extracting intermediate features, using graph embedding and meta-learning methods to improve the model. Finally, the difficulties of this approach and its prospects for development in the field of intrusion detection are identified based on the previous discussion.