Efficient poisoning attacks and defenses for unlabeled data in DDoS prediction of intelligent transportation systems

Nowadays, large numbers of smart sensors (e.g., road-side cameras) which communicate with nearby base stations could launch distributed denial of services (DDoS) attack storms in intelligent transportation systems. DDoS attacks disable the services provided by base stations. Thus in this paper, considering the uneven communication traffic flows and privacy preserving, we give a hidden Markov model-based prediction model by utilizing the multi-step characteristic of DDoS with a federated learning framework to predict whether DDoS attacks will happen on base stations in the future. However, in the federated learning, we need to consider the problem of poisoning attacks due to malicious participants. The poisoning attacks will lead to the intelligent transportation systems paralysis without security protection. Traditional poisoning attacks mainly apply to the classification model with labeled data. In this paper, we propose a reinforcement learning-based poisoning method specifically for poisoning the prediction model with unlabeled data. Besides, previous related defense strategies rely on validation datasets with labeled data in the server. However, it is unrealistic since the local training datasets are not uploaded to the server due to privacy preserving, and our datasets are also unlabeled. Furthermore, we give a validation dataset-free defense strategy based on Dempster–Shafer (D–S) evidence theory avoiding anomaly aggregation to obtain a robust global model for precise DDoS prediction. In our experiments, we simulate 3000 points in combination with DARPA2000 dataset to carry out evaluations. The results indicate that our poisoning method can successfully poison the global prediction model with unlabeled data in a short time. Meanwhile, we compare our proposed defense algorithm with three popularly used defense algorithms. The results show that our defense method has a high accuracy rate of excluding poisoners and can obtain a high attack prediction probability

Efficient poisoning attacks and defenses for unlabeled data in DDoS prediction of intelligent transportation systems

Nowadays, large numbers of smart sensors (e.g., road-side cameras) which communicate with nearby base stations could launch distributed denial of services (DDoS) attack storms in intelligent transportation systems. DDoS attacks disable the services provided by base stations. Thus in this paper, considering the uneven communication traffic flows and privacy preserving, we give a hidden Markov model-based prediction model by utilizing the multi-step characteristic of DDoS with a federated learning framework to predict whether DDoS attacks will happen on base stations in the future. However, in the federated learning, we need to consider the problem of poisoning attacks due to malicious participants. The poisoning attacks will lead to the intelligent transportation systems paralysis without security protection. Traditional poisoning attacks mainly apply to the classification model with labeled data. In this paper, we propose a reinforcement learning-based poisoning method specifically for poisoning the prediction model with unlabeled data. Besides, previous related defense strategies rely on validation datasets with labeled data in the server. However, it is unrealistic since the local training datasets are not uploaded to the server due to privacy preserving, and our datasets are also unlabeled. Furthermore, we give a validation dataset-free defense strategy based on Dempster–Shafer (D–S) evidence theory avoiding anomaly aggregation to obtain a robust global model for precise DDoS prediction. In our experiments, we simulate 3000 points in combination with DARPA2000 dataset to carry out evaluations. The results indicate that our poisoning method can successfully poison the global prediction model with unlabeled data in a short time. Meanwhile, we compare our proposed defense algorithm with three popularly used defense algorithms. The results show that our defense method has a high accuracy rate of excluding poisoners and can obtain a high attack prediction probability

Reasons for East Siberia Winter Snow Water Equivalent Increase in the Recent Decades

With the rapid warming in the past few decades, the snow water equivalent (SWE) in winter and spring decreased generally over the Northern Hemisphere, but an increasing trend occurred in some areas, especially in east Siberia. In this paper, we analyze the sources and reasons for the SWE increase in east Siberia in winter since 1979 and document projected future SWE changes in this region. The winter SWE changes in east Siberia were not significant over the past four decades until the 2000s, and the SWE increased rapidly thereafter. The SWE increase after the 2000s is mainly contributed by SWE in November, followed by that in winter, and attributed to the increase in snowfall. With the moisture budget diagnosis, we found that the atmospheric dynamic-induced moisture convergence (vertical motion effect and horizontal advection of moisture) are the reasons that contributed to the winter snowfall increase in east Siberia. As east Siberia is cold in winter, even under the high radiative forcing scenario, precipitation in east Siberia will continue to increase and be dominated by snowfall until the 2060s. Thereafter, with the rainfall increase and the accelerated snowmelt due to rising temperature, precipitation will gradually shift to rainfall type and the SWE may turn to decrease

Facial expression recognition based on multi branch structure

Facial expression recognition (FER) is an important means for machines to perceive human emotions and interact with human beings. Most of the existing facial expression recognition methods only use a single convolutional neural network to extract the global features of the face. Some insignificant details and features with low frequency are easy to be ignored, and part of the facial features are lost. This paper proposes a facial expression recognition method based on multi branch structure, which extracts the global and detailed features of the face from the global and local aspects respectively, so as to make a more detailed representation of the facial expression and further improve the accuracy of facial expression recognition. Specifically, we first design a multi branch network, which takes Resnet-50 as the backbone network. The network structure after Conv Block3 is divided into three branches. The first branch is used to extract the global features of the face, and the second and third branches are used to cut the face into two parts and three parts after Conv Block5 to extract the detailed features of the face. Finally, the global features and detail features are fused in the full connection layer and input into the classifier for classification. The experimental results show that the accuracy of this method is 73.7%, which is 4% higher than that of traditional Resnet-50, which fully verifies the effectiveness of this method

Petrogenesis and metallogenic significance of multistage granites in Shimensi tungsten polymetallic deposit, Dahutang giant ore field, South China

The Shimensi tungsten polymetallic deposit, situated in the Dahutang ore field, South China, is one of the largest tungsten deposits in the world, with an estimated WO3 reserve of 0.74 million tons. Coarse-grained porphyritic biotite granite (CPBG), fine-grained porphyritic biotite granite (FPBG), fine-grained biotite granite (FBG) and biotite granite porphyry (BGP) are all ore-related, but their diagenetic relationships and contributions to W-Cu-Mo mineralization are still in dispute. LA-ICP-MS monazite U-Pb dating of the CPBG, FPBG, FBG and BGP yield emplacement ages of 147.9 ± 1.1 Ma, 146.4 ± 1.1 Ma, 138.6 ± 0.98 Ma and 142.8 ± 1.7 Ma, respectively. Whole-rock geochemical results indicate that the four granites should be classified as S-type granites, but BGP has distinct features transitional between S- and I-type granites. They were possibly generated by partial melting of upper crustal pelites and basic volcanic rocks with different proportion from the Neoproterozoic Shuangqiaoshan Group in the source. Proportional variation in the magmatic source (clay and basic basalts) induces the change of geochemical compositions of the Shimensi granites. Geochemical characteristics suggest that they were derived from two magma chambers (the CPBG, FPBG and FBG vs. the BGP) and experienced different evolutionary processes and different degree of magmatic differentiation during magmatic evolution. Chondrite-normalized REE patterns for the four granites display low total REE contents, variable and strongly enriched LREE relative to HREE and medium-strong negative Eu anomalies. They are enriched in Rb, Th, U, Ta and depleted in Ba, Nb, Sr, P, Ti. Biotites are iron-rich and aluminum-poor, and can be classified as ferro-biotite (CPBG, FPBG and FBG) and siderophyllite (BGP). The partial melting of tungsten-rich metasediments of the Shuangqiaoshan Group and high degree of fractional crystallization led to enrichment in tungsten in the magma suites. Oxygen fugacities of the CPBG and FPBG declined from early (most above the NNO buffers) to late stages of fractional crystallization (between the NNO and QFM buffers) because of the higher degree of magmatic differentiation in the late stages. In the early stages of fractionation, tungsten accumulated in the residual melts rather than partitioning into accessory minerals. In the late stages, lower oxygen fugacities and high fluorine contents promoted the removal of tungsten from the residual magma into reduced hydrothermal fluids. On the other hand, the FBG and BGP remained constant (above the NNO buffers) over the entire process of crystallization owning to the stable degree of magmatic differentiation, promoting retention of tungsten in the melt and resulting in low grade tungsten mineralization. Tungsten mineralization in the Shimensi deposit is greatly controlled by the redox states of the associated magma. The two porphyritic granites (the CPBG and FPBG) are most likely the main contributors of tungsten, while the FBG and BGP are mainly responsible for copper and molybdenum in the Shimensi deposit. Prolonged multiphase magmatism and prolonged W-Cu-Mo mineralization play important roles in the formation of Shimensi large tungsten polymetallic deposit.This research was financially supported by the China Geological Survey Project (No. DD20160123), the National Natural Science Foundation of China (No. 41503050), the Basic scientific research fund of the Institute of Geology, Chinese Academy of Geological Sciences with grant No. J1630 and the National Key Research and Development plan Project (No. 2016YFC0600203)

Improved Performance of NbOx Resistive Switching Memory by In-Situ N Doping

Valence change memory (VCM) attracts numerous attention in memory applications, due to its high stability and low energy consumption. However, owing to the low on/off ratio of VCM, increasing the difficulty of information identification hinders the development of memory applications. We prepared N-doped NbOx:N films (thickness = approximately 15 nm) by pulsed laser deposition at 200 °C. N-doping significantly improved the on/off ratio, retention time, and stability of the Pt/NbOx:N/Pt devices, thus improving the stability of data storage. The Pt/NbOx:N/Pt devices also achieved lower and centralized switching voltage distribution. The improved performance was mainly attributed to the formation of oxygen vacancy (VO) + 2N clusters, which greatly reduced the ionic conductivity and total energy of the system, thus increasing the on/off ratio and stability. Moreover, because of the presence of Vo + 2N clusters, the conductive filaments grew in more localized directions, which led to a concentrated distribution of SET and RESET voltages. Thus, in situ N-doping is a novel and effective approach to optimize device performances for better information storage and logic circuit applications

Magmatic processes recorded in plagioclase and the geodynamic implications in the giant Shimensi W–Cu–Mo deposit, Dahutang ore field, South China

The Shimensi W–Cu–Mo deposit is one of the largest tungsten deposits in the world. Despite numerous geochemical studies conducted on ore-related granites in the district, few studies have concerned magma chambers processes. In this study, systematic in-situ major- and trace-element studies across plagioclase crystals from the ore-related Mesozoic granites as well as whole-rock Sr–Nd isotopic compositions of such granites in the Shimensi deposit were used to constrain the sources of calcium, the dynamics of the magmatic system and the metallogenic geodynamic setting. In-situ analyses of plagioclase showed no obvious positive correlations between An and FeO, while Sr was positively correlated with Ba, indicating that the magma chambers in the Shimensi deposit may have experienced chemically-closed evolution affected only by thermal mixing and/or decompression, without chemical mixing with mafic magma from the mantle. This conclusion was also supported by whole-rock Sr–Nd isotopic characteristics of high (87Sr/86Sr)i (0.71664–0.73689) and negative εNd(t) values (−9.81 to −5.07). It was found that the calcium needed for scheelite mineralization may have been predominantly provided by biotite granodiorite (BG) because of its high calcium content and large size, while ore-forming metals should mainly have been derived from the magma sources of pelites and basic volcanic rocks in the Shuangqiaoshan Group instead of the recharging of mafic magma. Moreover, change of the stress environment likely facilitated the formation of long-term stable, large-volume, highly evolved felsic magma chambers in the shallow crust, which would have been critical to the formation of the giant Shimensi W–Cu–Mo deposit.This research was financially supported by the National Key Research and Development Plan Project of China (No. 2016YFC0600203), the National Natural Science Foundation of China (No. 42002101, 41873059, 92062104, 41503050), the Basic Scientific Research Foundation of the Institute of Geology, CAGS (No. J1630) and the China Geological Survey Project (No. DD20190001)

Two-Dimensional SnSe Films on Paper Substrates for Flexible Broadband Photodetectors

Paper-based devices have aroused researchers’ enormous interest due to the increasing need for disposable flexible optoelectronic devices. Here, we used a straightforward painting method to integrate semiconducting materials on an A4 paper substrate to create high-performance photodetectors. A solvent-free 2D SnSe film painted on paper allowed electrons to separate from holes more effectively, thereby reducing the level of recombination between holes and electrons. The paper-based SnSe photodetectors that we prepared demonstrated a robust, sensitive response over a wide range of spectral ranges from ultraviolet (254 nm) to near-infrared (1550 nm). The devices were characterized by fast response times (rise time of 0.066 s and fall time of 0.066 s). A high-performance photodetector was achieved by combining a photoconductive and pyroelectric effect. Additionally, we constructed the carbon nanotube (CNTs) films as electrodes in order to form an ohmic contact between the carbon nanotubes and the semiconductor, and the photogenerated electrons were able to efficiently move through the carbon nanotubes. Our research could contribute to the development of flexible photodetection devices that are low-cost, eco-friendly, and disposable