Security assurance in Vehicular Ad hoc Network (VANET) is a crucial and challenging task due to the open-access medium. One great threat to VANETs is Distributed Denial-of-Service (DDoS) attack because the target of this attack is to prevent authorized nodes from accessing the services. To provide high availability of VANETs, a scalable, reliable and robust network intrusion detection system should be developed to efficiently mitigate DDoS. However, big data from VANETs poses serious challenges to DDoS attack detection since the detection system require scalable methods to capture, store and process the big data. To overcome these challenges, this paper proposes a distributed DDoS network intrusion detection system based on big data technology. The proposed detection system consists of two main components: real-time network traffic collection module and network traffic detection module. To build our proposed system, we use Spark to speed up data processing and use HDFS to store massive suspicious attacks. In the network collection module, micro-batch data processing model is used to improve the real-time performance of traffic feature collection. In the traffic detection module, the classification algorithm based on Random Forest (RF) is adopted. In order to evaluate the accuracy of detection, the algorithm was evaluated and compared in the datasets, containing NSL-KDD and UNSW-NB15. The experimental results show that the proposed detection algorithm reached the accuracy rate of 99.95% and 98.75%, and the false alarm rate (FAR) of 0.05% and 1.08%, respectively, in two datasets.INDEX TERMS Artificial intelligence, distributed denial-of-services, intrusion detection, intelligent transportation systems, spark, vehicular ad hoc networks.

Aiming to dynamic optimization problems (DOPs), this paper develops a novel general distributed multiple populations (DMP) framework for evolutionary algorithms (EAs). DMP employs six strategies designed in three levels (i.e., population-level, subpopulation-level, and individual-level) to deal with different kinds of DOPs. First, the population-level subpopulation division estimation strategy in initialization phase rationally divides the whole population into several subpopulations to explore distinct subareas of search space sufficiently. Then, during the steady evolutionary process, diversity preservation in individual-level and population-level accelerates the responsiveness of the whole population to a new landscape, while subpopulation-level self-learning of elitist individuals promotes the exploitation of promising areas. Moreover, in subpopulation-level, the archive quality assurance technique avoids repeat exploring the same peaks by storing the locations of different peaks with low redundancy. When landscape variation occurs, in population-level, historical information containing excellent evolutionary pattern is recorded to guide the population evolution better in the new environment. DMP framework is easy to implement in various EAs due to its well generality and independence about operators and parameters of the embedded algorithm. Four DMP-EAs are accomplished in this paper whose basic algorithms are particle swarm optimization (PSO) and differential evolution (DE) with different settings. The performance of the four proposed DMP-EAs is evaluated on all the widely used complex DOP benchmarks from CEC 2009. The testing results indicate that the DMP-EAs generally significantly outperform many state-of-the-art dynamic EAs (DEAs) on most of DOP benchmarks.INDEX TERMS Dynamic optimization problem (DOP), distributed multiple population (DMP) framework, multi-level diversity preservation, adaptive historical information utilization, dynamic evolutionary algorithm (DEA)

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