Abstract

۱٫ Introduction

۲٫ Related work

۳٫ Methodology

۴٫ Evaluation

۵٫ Conclusion

Declaration of competing interest

Acknowledgments

References

Abstract

     Dynamic traffic in a software-defined network (SDN) causes explosive data to flow from one system to another. The explosive data affects the functionality of system parameters, network-level configuration, routing parameters, network characteristics, and system load factors. Adapting to the traffic flow is a key research area in SDN in today’s big data world. Load balance vehicular sensor accessibility reduces delays, lowers energy consumption, and decreases the execution time. This paper combines the entropy-based active learning model to identify intrusion patterns efficiently, which is a packet-level intrusion detection model. The developed afterload balancing model can track the attack on the network. We then proposed a load balancing algorithm that optimizes the vehicular sensor usability by using sensor computing capability and source needs. We make use of a convergence-based mechanism to achieve high resource utilization. We then perform experiments on the state-of-the-art intrusion detection dataset. Our experimental results show that the load balancing mechanism can achieve  in performance improvements compared to traditional approaches. Thus, we can see that the designed model can help improve the decision boundary by increasing the training instance through pooling strategy and entropy uncertainty measure.

Introduction

     The application of the Internet of things (IoT) and distributed computing has enabled a massive growth of heterogeneous applications. The future of IoT will connect many heterogeneous devices with the ability to communicate with the network directly [1]. Billions of objects (i.e., sensors network) will be connected to the Internet in the next generation of networks. This will result in extensive amounts of data that give rise to data delivery issues. Objects may include home application, traffic flow analysis, irrigation systems—these objects are usually equipped with several sensors or nodes. The role of these sensors/nodes is to gather and analyze real-time environments.

     Connected sensors with autonomous vehicles will also grow and become the future of intelligent transportation systems. Travel comfort, road security and safety will depend upon high data rates and reliable connectivity among autonomous vehicles. Such a transformation will increase the need for a safe and convenient network environment from transportation and transport infrastructure. These sensors are designed to acquire real-time data, and efficient processing is required for better performance. The gathered data is then being used by cloud computing-based applications [2]. The applications can store, process, and update the data in real-time. The applications have centralized data centers that are distributed geographically. Fog computing-based application services are handled at the network’s edge.

Conclusion

     This study presented a novel load balancing algorithm to balance the load of SDN among different vehicular sensors by using network and application information. The proposed model can balance the batch of applications among the vehicular sensor connected over the SDN. We tested our approach on different datasets, and the outcome of the proposed model has been compared with well-known heuristic-based models. Moreover, we used an entropy-based active learning approach to classify intrusion attacks. The developed model can achieve high accuracy to identify the patterns in terms of sparse and dense datasets. The entropy-based active learning-based method significantly increases training instances for the deep feedforward model. In the future, the network will be optimized tuned to apply the active learning mechanism. A weighted-based method for each class sub-sample selection can also be considered as a further extension.