Identifying cyber attacks traffic is very important for the Internet of things (IoT) security in smart city. Recently, the research community in the field of IoT Security endeavor hard to build ...anomaly, intrusion and cyber attacks traffic identification model using Machine Learning (ML) algorithms for IoT security analysis. However, the critical and significant problem still not studied in depth that is how to select an effective ML algorithm when there are numbers of ML algorithms for cyber attacks detection system for IoT security. In this paper, we proposed a new framework model and a hybrid algorithm to solve this problem. Firstly BoT-IoT identification dataset is applied and its 44 effective features are selected from a number of features for the machine learning algorithm. Then five effective machine learning algorithm is selected for the identification of malicious and anomaly traffic identification and also select the most widely ML algorithm performance evaluation metrics. To find out which ML algorithm is effective and should be used to select for IoT anomaly and intrusion traffic identification, a bijective soft set approach and its algorithm is applied. Then we applied the proposed algorithm based on bijective soft set approach. Our experimental results show that the proposed model with the algorithm is effective for the selection ML algorithm out of numbers of ML algorithms.
•This paper investigate Machine Learning (ML) algorithm and effective features.•This paper proposed a new framework model and a hybrid algorithm.•The basic technique used in this paper is bijective soft set and proposed new algorithm.•This paper selected effective ML algorithm and features for the identification attacks.•Finally, the paper validates the selected ML algorithm, Feature set for Attacks traffic.
Identification of anomaly and malicious traffic in the Internet-of-Things (IoT) network is essential for the IoT security to keep eyes and block unwanted traffic flows in the IoT network. For this ...purpose, numerous machine-learning (ML) technique models are presented by many researchers to block malicious traffic flows in the IoT network. However, due to the inappropriate feature selection, several ML models prone misclassify mostly malicious traffic flows. Nevertheless, the significant problem still needs to be studied more in-depth that is how to select effective features for accurate malicious traffic detection in the IoT network. To address the problem, a new framework model is proposed. First, a novel feature selection metric approach named CorrAUC is proposed, and then based on CorrAUC, a new feature selection algorithm named CorrAUC is developed and designed, which is based on the wrapper technique to filter the features accurately and select effective features for the selected ML algorithm by using the area under the curve (AUC) metric. Then, we applied the integrated TOPSIS and Shannon entropy based on a bijective soft set to validate selected features for malicious traffic identification in the IoT network. We evaluate our proposed approach by using the Bot-IoT data set and four different ML algorithms. The experimental results analysis showed that our proposed method is efficient and can achieve >96% results on average.
•This paper study Data Mining and Machine Learning Methods for Sustainable Smart Cities Traffic Classification: A Survey.•This survey paper describes the significant literature survey of Sustainable ...Smart Cities (SSC), Machine Learning (ML), Data Mining (DM), datasets, feature extraction and selection for network traffic classification.•In this paper, most cited methods and datasets of features were identified, read and summarized.•In this paper, different classification techniques for SSC network traffic classification are presented.•In this paper, in the end, challenges and recommendations for SSC network traffic classification with the dataset of features are presented.
This survey paper describes the significant literature survey of Sustainable Smart Cities (SSC), Machine Learning (ML), Data Mining (DM), datasets, feature extraction and selection for network traffic classification. Considering relevance and most cited methods and datasets of features were identified, read and summarized. As data and data features are essential in Internet traffic classification using machine learning techniques, some well-known and most used datasets with details statistical features are described. Different classification techniques for SSC network traffic classification are presented with more information. The complexity of data set, features extraction and machine learning methods are addressed. In the end, challenges and recommendations for SSC network traffic classification with the dataset of features are presented.
Edge computing provides high-class intelligent services and computing capabilities at the edge of the networks. The aim is to ease the backhaul impacts and offer an improved user experience. However, ...the edge artificial intelligence exacerbates the security of the cloud computing environment due to the dissociation of data, access control, and service stages. In order to prevent users from carrying out lateral movement attacks in an edge-cloud computing environment, in this paper we propose a real-time lateral movement detection method, named CloudSEC, based on an evidence reasoning network for the edge-cloud environment. First, the concept of vulnerability correlation is introduced. Based on the vulnerability knowledge and environmental information of the network system, the evidence reasoning network is constructed, and the lateral movement reasoning ability provided by the evidence reasoning network is then used. The experiment results show that CloudSEC provides a strong guarantee for the rapid and effective evidence investigation, as well as real-time attack detection.
Based on the gravity model of trade, this paper studies the trade creation effect of China-Asean free trade area. 23 trading countries in North America, South America, Asia, Europe and Oceania are ...selected as samples. Construct the trade gravity model of China-Asean free trade area and make the empirical analysis. The increase of GDP in trading countries will promote the growth of trade flow. Secondly, CAFTA will increase trade volume, but the effect is not significant. In addition, geographical distance has a negative impact on the increase of trade flows. In order to promote the trade development of China-Asean free trade area, differentiated cooperation is needed. It needs to carry out sub-regional cooperation with CAFTA and promote trade cooperation with One Belt and One Road.
OVATE gene was first identified as a key regulator of fruit shape in tomato. OVATE family proteins (OFPs) are characterized as plant-specific transcription factors and conserved in Arabidopsis, ...tomato, and rice. Roles of OFPs involved in plant development and growth are largely unknown. Brassinosteroids (BRs) are a class of steroid hormones involved in diverse biological functions. OsGKS2 plays a critical role in BR signaling by phosphorylating downstream components such as OsBZR1 and DLT. Here we report in rice that OsOFP8 plays a positive role in BR signaling pathway. BL treatment induced the expression of OsOFP8 and led to enhanced accumulation of OsOFP8 protein. The gain-of-function mutant Osofp8 and OsOFP8 overexpression lines showed enhanced lamina joint inclination, whereas OsOFP8 RNAi transgenic lines showed more upright leaf phenotype, which suggest that OsOFP8 is involved in BR responses. Further analyses indicated that OsGSK2 interacts with and phosphorylates OsOFP8. BRZ treatment resulted in the cytoplasmic distribution of OsOFP8, and bikinin treatment reduced the cytoplasmic accumulation of OsOFP8. Phosphorylation of OsOFP8 by OsGSK2 is needed for its nuclear export. The phospphorylated OsOFP8 shuttles to the cytoplasm and is targeted for proteasomal degradation. These results indicate that OsOFP8 is a substrate of OsGSK2 and the function of OsOFP8 in plant growth and development is at least partly through the BR signaling pathway.
Polymeric carbon nitrides (CNs) are regarded as the most sustainable materials for solar energy conversion via photocatalytic processes. However, the first-generation CNs suffered from imperfect ...charge separation and insufficient CO2 adsorption. Herein, the construction of a heterojunction material involving highly crystalline CN-nanorods with ordered alignment on graphene is delineated, which improves light harvesting, CO2 capture, and interface charge transfer. The graphene-supported 1D nano-arrays of crystalline CNs show a comparably high selectivity of CO2/N2 up to 44, with an isosteric heat of adsorption of 55.2 kJ/mol for CO2. The heterojunction material also drives the simple and efficient CO2 photoreduction in the gas phase, without the addition of any cocatalyst or sacrificial agent, even at the more relevant case of low concentrations of CO2. These findings provide a robust way for tailoring the performance of CN materials, with the aim of a practicable technological application for CO2 capture and photoreduction.
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•Ordered heterojunctions of 1D crystalline carbon nitride grown on 2D graphene•Heterojunction material with comparably high selectivity of CO2 capture•Heterojunction material as an excellent photocatalyst for gas-phase CO2 reduction
Excessive consumption of fossil fuels is the key contributor to climate change. Solar-powered artificial photosynthetic conversion of CO2, potentially direct from diluted sources, into value-added products is considered as a promising strategy to alleviate the problem. The exploitation of low-cost, sustainable, and highly active photocatalysts is critical to improve CO2 photoreduction for practical applications. CNs are one of the most simple and promising candidates so far. However, the efficiency of the first-generation CNs is still low because of poor CO2 adsorption and activation. The concerns are well addressed in the current work, in which a 1D nanocrystalline structure of CN is orderly grown on graphene via an ionothermal method. The heterojunction material of CN nano-arrays on graphene exhibits high CO2 capture ability and excellent CO2 photoreduction activity under the condition of low concentration of CO2 gas, in the absence of any cocatalyst or sacrificial agent.
A heterojunction material involving highly crystalline carbon nitride nanorods with ordered alignment on graphene is successfully synthesized, and it shows a comparably high selectivity of CO2/N2 up to 44, with an isosteric heat of adsorption of 55.2 kJ/mol for CO2. This material also shows remarkable improvements of light absorption, exciton splitting, and charge transport, which enables the efficient photochemical reduction of wet CO2 in the gas phase and without any sacrificial agent.
Plant growth and development are highly coordinated by hormones, including brassinosteroid (BR) and gibberellin (GA). Although much progress has been made in understanding the fundamental signaling ...transduction in BR and GA, their relationship remains elusive in rice.
Here, we show that BR suppresses the level of OsmiR159d, which cleaves the target OsGAMYBL2 gene. The OsmiR159d-OsGAMYBL2 pair functions as an early BR-responsive module regulating the expression of BU1, a BR-regulated gene involved in BR signaling, and CPS1 and GA3ox2, two genes in GA biosynthesis, by binding to the promoters of these genes.
Furthermore, OsGSK2, a key negative player in BR signaling, interacts with OsGAMYBL2 and prevents it from being degraded under 24-epibrassinolide treatment, whereas SLR1, a rice DELLA protein negatively regulating GA signaling, interacts with OsGAMYBL2 and prevents OsGAMYBL2 from binding to the target gene promoter. GA signaling induces degradation of OsGAMYBL2 and, consequently, enhances BR signaling.
These results demonstrate that a BR-responsive module acts as a common component functioning in both BR and GA pathways, which connects BR signaling and GA biosynthesis, and thus coordinates the regulation of BR and GA in plant growth and development.
A bottom-up method for the preparation of platinum nanoparticles (PtNPs)-decorated two-dimensional (2D) metal–organic framework (MOF) nanocomposites was developed with a maximum water decomposition ...rate of 3348 μmol g-1h−1 and sufficient stability.
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•The Zr-TCPP(Pd) and Zr-TCPP ultrathin nanosheets were successfully synthesized.•The nanosheets modified with Pt NPs show excellent photocatalytic performance.•DFT revealed the importance of Pd-doping position in Pt79 NPs on the activity.
Edge-doping engineering in metal nanoparticles (MNPs) is always hard to achieve due to the high surface energy of the hybrid MNPs, while porphyrin-based ultrathin two-dimensional (2D) metal–organic framework (MOF) is demonstrated the positive role in stabilize this structure. Herein, a bottom-up method was developed to prepare platinum nanoparticles (PtNPs)-decorated 2D MOF nanosheets, where a porphyrin ligand of Pd-metalized tetrakis(4-carboxyphenyl)porphyrin (PdTCPP) was applied to synthesize ultrathin MOF nanosheets as Zr-TCPP(Pd) in high yield. Attributing to the high superficial area of ultrathin Zr-TCPP(Pd) nanosheets, Pt NPs can well anchor uniformly with small nanoparticle size to obtain 2% Pt/Zr-TCPP(Pd) hybrid nanosheets, which showed a higher photocatalytic hydrogen production rate of 3348 μmol g-1h−1. This is attributed to the coordination between Zr4+ and C = O of PVP, which promotes the contact between PtNPs and Zr-TCPP(Pd) nanosheets. As a result, the long-life electrons of PdTCPP photosensitizers are rapidly transferred to the electron capture center PtNPs, and the photoelectron-hole recombination is effectively inhibited. The apparent quantum efficiency of 2% Pt/Zr-TCPP(Pd) reaches up to 1.56% at 420 nm. The density functional theory (DFT) calculations revealed the Pd-doped position in Pt79 nanoparticle is important that the Pt78Pdsurf. model (Pd atom was doped on the surface of Pt nanoparticle) showed the highest activity with abundant exposed active region.
In sensor-based systems, the data of an object is often provided by multiple sources. Since the data quality of these sources might be different, when querying the observations, it is necessary to ...carefully select the sources to make sure that high quality data is accessed. A solution is to perform a quality evaluation in the cloud and select a set of high-quality, low-cost data sources (i.e., sensors or small sensor networks) that can answer queries. This paper studies the problem of min-cost quality-aware query which aims to find high quality results from multi-sources with the minimized cost. The measurement of the query results is provided, and two methods for answering min-cost quality-aware query are proposed. How to get a reasonable parameter setting is also discussed. Experiments on real-life data verify that the proposed techniques are efficient and effective.
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