Internet of things for smart ports: Technologies and challenges

In order to enhance the recall and the precision performance of data integrity detection, a method to detect the network storage data integrity based on symmetric difference was proposed. Through the complete automatic image annotation system, the crawler technology was used to capture the image and related text information. According to the automatic word segmentation, pos tagging and Chinese word segmentation, the feature analysis of text data was achieved. Based on the symmetrical difference algorithm and the background subtraction, the feature extraction of image data was realized. On the basis of data collection and feature extraction, the sentry data segment was introduced, and then the sentry data segment was randomly selected to detect the data integrity. Combined with the accountability scheme of data security of the trusted third party, the trusted third party was taken as the core. The online state judgment was made for each user operation. Meanwhile, credentials that cannot be denied by both parties were generated, and thus to prevent the verifier from providing false validation results. Experimental results prove that the proposed method has high precision rate, high recall rate, and strong reliability.

Section: Start Challengementioning

confidence: 99%

Detection Method of Data Integrity in Network Storage Based on Symmetrical Difference

Ding¹

2020

“…Therefore, it is necessary to adjust the p, q value appropriately in order to make that the feature of symmetric Holder coefficient not only have a better degree of clustering within the class, but also have a larger distance between the classes. At the same time, it can be clearly seen that the complexity of the symmetric Holder coefficient method in type selection will be reduced by half [11][12][13][14]. As shown in Figure 1, it is the process flow of symmetric coefficient method.…”

Section: Recognition Of Signal Sequencementioning

confidence: 99%

Feature Extraction Method of Transmission Signal in Electronic Communication Network Based on Symmetric Algorithm

Song

2019

Because the existing methods extract the signal characteristics of electronic communication networks, there is a problem of poor extraction. In this paper, a feature extraction method based on symmetric algorithm for transmission signals in electronic communication networks is proposed. The transmission signal in the time domain is decomposed by three-layer wavelet packet decomposition through threshold denoising and data dimension reduction. The adaptive floating threshold is used as a threshold to quantify the wavelet coefficients of the signal, which can effectively remove noise while retaining valuable transmission signal. Secondly, the feature extraction algorithm based on symmetric Holder coefficient is used to transform the transmitted signal from time domain to frequency domain, identify the signal sequence, and classify the signal sequence using neural network classifier. The simulation results show that the proposed method can extract the transmission signal of electronic communication network with the highest accuracy of 98.21%. This method can extract the amplitude and frequency characteristics of the transmission signal accurately under strong vibration environment. It is an efficient method for feature extraction of transmission signal.

“…However, most of these methods do not consider the analysis of intrusion characteristics because they are designed to improve detection accuracy and reduce false alarm rate [5,6]. In practice, although all input features can be used as the input of the classifier, irrelevant or redundant features may have a negative impact on the generalization ability of the classifier [7][8][9].…”

Section: Introductionmentioning

confidence: 99%

Key Feature Recognition Algorithm of Network Intrusion Signal Based on Neural Network and Support Vector Machine

Yang¹

2019

When identifying the key features of the network intrusion signal based on the GA-RBF algorithm (using the genetic algorithm to optimize the radial basis) to identify the key features of the network intrusion signal, the pre-processing process of the network intrusion signal data is neglected, resulting in an increase in network signal data noise, reducing the accuracy of key feature recognition. Therefore, a key feature recognition algorithm for network intrusion signals based on neural network and support vector machine is proposed. The principal component neural network (PCNN) is used to extract the characteristics of the network intrusion signal and the support vector machine multi-classifier is constructed. The feature extraction result is input into the support vector machine classifier. Combined with PCNN and SVM (Support Vector Machine) algorithms, the key features of network intrusion signals are identified. The experimental results show that the algorithm has the advantages of high precision, low false positive rate and the recognition time of key features of R2L (it is a common way of network intrusion attack) data set is only 3.18 ms.Symmetry 2019, 11, 380 2 of 15 features and remove secondary features, which is often conducive to shortening the detection time and discovering the intrinsic features of a certain type of attack [10]. Feature extraction is not to explicitly remove some features from the input features, but to carry out linear or nonlinear transformation of the input features, extracting from them to replace the table components, using these components instead of the original input features, so as to achieve dimensionality reduction and feature space conversion [11][12][13].At present, there are few researches on intrusion detection from the perspective of feature extraction. Authors in [14] constructed a multi-layer hybrid intrusion detection model, using support vector machine and extreme learning machine to improve the efficiency of detection of known and unknown attacks; then proposed an improved k-means method, established a new small training data set representing the entire training data set, greatly shortened the training time of classifier, and improved the performance of the intrusion detection system. However this method has the problem of low accuracy. Authors in [15] constructed a wireless grid intrusion detection system based on genetic algorithm and multi-support vector machine classifier. The system chooses the information characteristics of each type of attack, rather than the common features of all attacks. The network simulator is used to simulate the intrusion data set generated by wireless mesh network. The system is evaluated with the parameters of packet transmission rate and delay. However, the system has the problem of high false alarm rate.It can be mentioned that dimensionality reduction in NN inputs and topology to enhance generalisation has been applied in other fields. Authors in [16] constructed a spectrum prediction method based on back propagation-training...