With the advent of the "Internet plus" era, the Internet of Things (IoT) is gradually penetrating into various fields, and the scale of its equipment is also showing an explosive growth trend. The age of the "Internet of Everything" is coming. The integration and diversification of IoT terminals and applications make IoT more vulnerable to various intrusion attacks. Therefore, it is particularly important to design an intrusion detection model that guarantees the security, integrity and reliability of the IoT. Traditional intrusion detection technology has the disadvantages of low detection rate and poor scalability, which cannot adapt to the complex and changeable IoT environment. In this paper, we propose a particle swarm optimization-based gradient descent (PSO-LightGBM) for the intrusion detection. In this method, PSO-LightGBM is used to extract the features of the data and inputs it into one-class SVM (OCSVM) to discover and identify malicious data. The UNSW-NB15 dataset is applied to verify the intrusion detection model. The experimental results show that the model we propose is very robust in detecting either normal or various malicious data, especially small sample data such as Backdoor, Shellcode and Worms. INDEX TERMS Intrusion detection, Internet of Things (IoT), particle swarm optimization (PSO), oneclass SVM(OCSVM)

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Bretschneidera sinensis is an endangered woody species found in East and South China. Comprehensive intraspecies chloroplast genome studies have demonstrated novel genetic resources to assess the genetic variation and diversity of this species. Using genome skimming method, we assembled the whole chloroplast genome of 12 genotypes of B. sinensis from different geographical locations, covering most wild populations. The B. sinensis chloroplast genome size ranged from 158,959 to 159,045 base pairs (bp) and displayed a typical circular quadripartite structure. Comparative analyses of 12 B. sinensis chloroplast genome revealed 33 polymorphic simple sequence repeats (SSRs), 105 polymorphic single nucleotide polymorphisms (SNPs), and 55 indels. Phylogenetic analysis showed that the 12 genotypes were grouped into 2 branches, which is consistent with the geographical distribution (Eastern clade and Western clade). Divergence time estimates showed that the two clades were divergent from 0.6 Ma in the late Pleistocene. Ex situ conservation is essential for this species. In this study, we identified SNPs, indels, and microsatellites of B. sinensis by comparative analyses of chloroplast genomes and determined genetic variation between populations using these genomic markers. Chloroplast genomic resources are also important for further domestication, population genetic, and phylogenetic analysis, possibly in combination with molecular markers of mitochondrial and/or nuclear genomes.