Non-Oscillatory Pattern Learning for Non-Stationary Signals

This paper proposes a novel non-oscillatory pattern (NOP) learning scheme for several oscillatory data analysis problems including signal decomposition, super-resolution, and signal sub-sampling. To the best of our knowledge, the proposed NOP is the first algorithm for these problems with fully non-stationary oscillatory data with close and crossover frequencies, and general oscillatory patterns. NOP is capable of handling complicated situations while existing algorithms fail; even in simple cases, e.g., stationary cases with trigonometric patterns, numerical examples show that NOP admits competitive or better performance in terms of accuracy and robustness than several state-of-the-art algorithms

A Secure and Efficient Multi-Object Grasping Detection Approach for Robotic Arms

Robotic arms are widely used in automatic industries. However, with wide applications of deep learning in robotic arms, there are new challenges such as the allocation of grasping computing power and the growing demand for security. In this work, we propose a robotic arm grasping approach based on deep learning and edge-cloud collaboration. This approach realizes the arbitrary grasp planning of the robot arm and considers the grasp efficiency and information security. In addition, the encoder and decoder trained by GAN enable the images to be encrypted while compressing, which ensures the security of privacy. The model achieves 92% accuracy on the OCID dataset, the image compression ratio reaches 0.03%, and the structural difference value is higher than 0.91

A DRDoS Detection and Defense Method Based on Deep Forest in the Big Data Environment

Distributed Denial of Service (DDoS) has developed multiple variants, one of which is Distributed Reflective Denial of Service (DRDoS). With the increasing number of Internet of Things (IoT) devices, the threat of DRDoS attack is growing, and the damage of a DRDoS attack is more destructive than other types. The existing DDoS detection methods cannot be generalized in DRDoS early detection, which leads to heavy load or degradation of service when deployed at the final point. In this paper, we propose a DRDoS detection and defense method based on deep forest model (DDDF), and then we integrate differentiated service into defense model to filter out DRDoS attack flow. Firstly, from the statistics perspective on different stages of DRDoS attack flow in the big data environment, we extract a host-based DRDoS threat index (HDTI) from the network flows. Secondly, using the HDTI feature we build a DRDoS detection and defense model based on the deep forest, which consists of 1 extreme gradient boost (XGBoost) forest estimator, 2 random forest estimators, and 2 extra random forest estimators in each layer. Lastly, the differentiated service procedure applies the detection result from DDDF to drop the traffic identified in different stages and different detection points. Theoretical analysis and experiments show that the method we proposed can effectively identify DRDoS attack with higher detection rate and a lower false alarm rate, the defense model also shows distinguishing ability to effectively eliminate the DRDoS attack flows, and dramatically mitigate the damage of a DRDoS attack

A Blockchain-Based Trust Model for Uploading Illegal Data Identification

Malicious users can upload illegal data to the blockchain to spread it, resulting in serious threats due to the tamper-proof characteristics of the blockchain. However, the existing methods for uploading illegal data identification cannot select trust nodes and ensure the credibility of the identification results, leading to a decrease in the credibility of the methods. To solve the problem, this paper proposes a blockchain-based trust model for uploading illegal data identification. The trust model mainly has the following two core modules: Reputation-based random selection algorithm (RBRSA) and incentive mechanism. By assigning reputation attributes to nodes, the proposed RBRSA will select nodes according to reputation values. RBRSA favors the nodes with high reputation value to ensure the randomness and credibility of the identification nodes. The incentive mechanism is designed to ensure the credibility of the identification results through the credibility analysis of the model based on game theory and Nash equilibrium. Identification nodes that identify illegal data correctly will obtain incentives. In order to obtain a higher income, the identification nodes must identify illegal data correctly. Credibility analysis and comparative experiments show that the probability of selecting credible nodes by RBRSA is up to 23% higher than the random selection algorithm. The probability of selecting the nodes with a reputation value of 20 by RBRSA is 27% lower than the random selection algorithm; that is, the probability that RBRSA selects untrusted nodes is lower. Therefore, the nodes selected by RBRSA have superior credibility compared with other methods. In terms of the effect of the incentive mechanism, the incentive mechanism can encourage nodes to identify data credibly and improve the credibility of identification results. All in all, the trusted model has higher credibility than other methods

A Blockchain-Based Trust Model for Uploading Illegal Data Identification

Malicious users can upload illegal data to the blockchain to spread it, resulting in serious threats due to the tamper-proof characteristics of the blockchain. However, the existing methods for uploading illegal data identification cannot select trust nodes and ensure the credibility of the identification results, leading to a decrease in the credibility of the methods. To solve the problem, this paper proposes a blockchain-based trust model for uploading illegal data identification. The trust model mainly has the following two core modules: Reputation-based random selection algorithm (RBRSA) and incentive mechanism. By assigning reputation attributes to nodes, the proposed RBRSA will select nodes according to reputation values. RBRSA favors the nodes with high reputation value to ensure the randomness and credibility of the identification nodes. The incentive mechanism is designed to ensure the credibility of the identification results through the credibility analysis of the model based on game theory and Nash equilibrium. Identification nodes that identify illegal data correctly will obtain incentives. In order to obtain a higher income, the identification nodes must identify illegal data correctly. Credibility analysis and comparative experiments show that the probability of selecting credible nodes by RBRSA is up to 23% higher than the random selection algorithm. The probability of selecting the nodes with a reputation value of 20 by RBRSA is 27% lower than the random selection algorithm; that is, the probability that RBRSA selects untrusted nodes is lower. Therefore, the nodes selected by RBRSA have superior credibility compared with other methods. In terms of the effect of the incentive mechanism, the incentive mechanism can encourage nodes to identify data credibly and improve the credibility of identification results. All in all, the trusted model has higher credibility than other methods

A multi-classification detection model for imbalanced data in NIDS based on reconstruction and feature matching

Abstract With the exponential growth of various data interactions on network systems, network intrusions are also increasing. The emergence of edge computing technology brings a new solution to network security. However, due to the difficulty of processing massive and unbalanced data at the edge, higher accuracy requirements are necessary for deployed detection models. This paper proposes a multi-classification model for network intrusion detection based on reconstruction and feature matching. This model can be deployed on small-scale edge nodes, effectively identifying various attack behaviors through the utilization of reconstruction errors and adaptive scaling. Furthermore, we proposed a model transfer method based on feature matching to enhance the training and detection efficiency of multi-classification models under different data distribution conditions. The proposed model has been evaluated on the CICIDS2017 dataset in terms of accuracy, recall, precision and F1 score. The model demonstrates high accuracy for normal flows in the network, majority class attacks, and minority class attacks, achieving an overall multi-class accuracy of 99.81%, outperforming similar models. Furthermore, this model demonstrates faster convergence and training speed after feature matching, exhibiting better robustness and outstanding performance at the edge

A Study of the Mechanical and Thermal Characteristics of an Al-Si-Fe Alloy Fabricated by Rolling and Heat Treatment

The effects of a rolling process and heat treatment on the mechanical and thermal properties of an Al-Si-Fe alloy were studied. The achieved thermal conductivity of the as-rolled alloy treated by a T6 heat treatment was 188.22 W/(m·K), which is as good as that of the as-cast alloy treated by the T6 heat treatment directly, mostly because of changes in the silicon morphology. The results also revealed that the lower quantity of precipitated Al8Fe2Si and Mg2Si phases had no obvious effect on the thermal properties of the material because the interphase spaces between precipitated phases were larger than the mean free path of electrons. However, the precipitated second phases influenced the elongation. The best mechanical properties of the Al-Si-Fe alloy were obtained by rolling and T6 treatment. The corresponding best tensile strength, yield strength, and elongation were 244 MPa, 295 MPa, and 9.56%, respectively, which are attributed to the near-spherical shape, small size, homogeneous distribution of the Si particles, and the precipitation strengthening of Mg2Si

Effect of cellular structure on the mechanical properties of 316L stainless steel fabricated by EBF3

The cellular structure formed in additive manufactured 316L stainless steel (316L SS) plays a crucial role in achieving strength–ductility synergy. The cellular structure was also found in the 316L SS fabricated by electron beam freeform fabrication process (EBF3), and its microstructural characterization was analyzed. Interrupted tensile tests were carried out to demonstrate the effect of the cellular structure on the mechanical properties. The results showed that the microstructure of the as-deposited 316L SS consisted of the single austenite phase, and the enrichment of Cr and Mo elements was found in the cellular boundary. The orientation of the cellular structure and boundary was similar, and the obvious {100} preferred orientation was parallel to the building direction. The yield strength and fracture elongation of the as-deposited 316L SS parts were 316 MPa and 37.2%, respectively. Its ultimate tensile strength with a value of 632 MPa, was higher than that manufactured by conventional methods. The hardness and elastic modulus in the cellular interior were 2.88 ± 0.11 GPa and 227.45 ± 10.66 GPa, respectively. The cellular boundary had a higher hardness and a lower elastic modulus than the cellular interior. At the initial stage of tensile deformation, the cellular boundary deforms first due to its lower elastic modulus, causing the dislocation pileup and orientation change. The high dislocation density improved the slip resistance leading to the appearance of deformation twins after necking. The micron-dimples and nano-dimples formed in the fracture surface with the deformation of the cellular boundary and interior. Different from 316L SS fabricated by other additive manufacturing processes, the cellular structure improved the strain hardening capacity of the 316L SS fabricated by the EBF3 process