Biblio

The selection of distribution network faults is of great significance to accurately identify the fault location, quickly restore power and improve the reliability of power supply. This paper mainly studies the fault phase selection method of distribution network based on wavelet singular entropy and deep belief network (DBN). Firstly, the basic principles of wavelet singular entropy and DBN are analyzed, and on this basis, the DBN model of distribution network fault phase selection is proposed. Firstly, the transient fault current data of the distribution network is processed to obtain the wavelet singular entropy of the three phases, which is used as the input of the fault phase selection model; then the DBN network is improved, and an artificial neural network (ANN) is introduced to make it a fault Select the phase classifier, and specify the output label; finally, use Simulink to build a simulation model of the IEEE33 node distribution network system, obtain a large amount of data of various fault types, generate a training sample library and a test sample library, and analyze the neural network. The adjustment of the structure and the training of the parameters complete the construction of the DBN model for the fault phase selection of the distribution network.

Many smartphones are lost every year, with a meager percentage recovered. In many cases, users with malicious intent access these phones and use them to acquire sensitive data. There is a need for continuous monitoring and surveillance in smartphones, and keystroke dynamics play an essential role in identifying whether a phone is being used by its owner or an impersonator. Also, there is a growing need to replace expensive 2-tier authentication methods like One-time passwords (OTP) with cheaper and more robust methods. The methods proposed in this paper are applied to existing data and are proven to train more robust classifiers. A novel feature extraction method by wavelet transformation is demonstrated to convert keystroke data into features. The comparative study of classifiers trained on the extracted features vs. features extracted by existing methods shows that the processes proposed perform better than the state-of-art feature extraction methods.

The core looseness fault is an important part of transformer fault. The state of the core can be obtained by analyzing the vibration signal. Vibration analysis method has been used in transformer condition monitoring and fault diagnosis for many years, while different methods produce different results. In order to select the correct method in engineering application, five kinds of joint time-frequency analysis methods, such as short-time Fourier transform, Wigner-Ville distribution, S transform, wavelet transform and empirical mode decomposition are compared, and the advantages and disadvantages of these methods for dealing with the vibration signal of transformer core are analyzed in this paper. It indicates that wavelet transform and empirical mode decomposition have more advantages in the diagnosis of core looseness fault. The conclusions have referential significance for the diagnosis of transformer faults in engineering.

A THz image edge detection approach based on wavelet and neural network is proposed in this paper. First, the source image is decomposed by wavelet, the edges in the low-frequency sub-image are detected using neural network method and the edges in the high-frequency sub-images are detected using wavelet transform method on the coarsest level of the wavelet decomposition, the two edge images are fused according to some fusion rules to obtain the edge image of this level, it then is projected to the next level. Afterwards the final edge image of L-1 level is got according to some fusion rule. This process is repeated until reaching the 0 level thus to get the final integrated and clear edge image. The experimental results show that our approach based on fusion technique is superior to Canny operator method and wavelet transform method alone.

We are exploring new ways to analyze phishing attacks. To do this, we investigate the change in the dynamics of the power of phishing attacks. We also analyze the effectiveness of detection of phishing attacks. We are considering the possibility of using new tools for analyzing phishing attacks. As such tools, the methods of chaos theory and the ideology of wavelet coherence are used. The use of such analysis tools makes it possible to investigate the peculiarities of the phishing attacks occurrence, as well as methods for their identification effectiveness. This allows you to expand the scope of the analysis of phishing attacks. For analysis, we use real data about phishing attacks.

To identify potential risks to the system security presented by time series it is offered to use wavelet analysis, the indicator of time-and-frequency distribution, the correlation analysis of wavelet-spectra for receiving rather complete range of data about the process studied. The indicator of time-and-frequency localization of time series was proposed allowing to estimate the speed of non-stationary changing. The complex approach is proposed to use the wavelet analysis, the time-and-frequency distribution of time series and the wavelet spectra correlation analysis; this approach contributes to obtaining complete information on the studied phenomenon both in numerical terms, and in the form of visualization for identifying and predicting potential system security threats.

Challenges associated with developing analytics solutions at the edge of large scale Industrial Internet of Things (IIoT) networks close to where data is being generated in most cases involves developing analytics solutions from ground up. However, this approach increases IoT development costs and system complexities, delay time to market, and ultimately lowers competitive advantages associated with delivering next-generation IoT designs. To overcome these challenges, existing, widely available, hardware can be utilized to successfully participate in distributed edge computing for IIoT systems. In this paper, an osmotic computing approach is used to illustrate how distributed osmotic computing and existing low-cost hardware may be utilized to solve complex, compute-intensive Explainable Artificial Intelligence (XAI) deep learning problem from the edge, through the fog, to the network cloud layer of IIoT systems. At the edge layer, the C28x digital signal processor (DSP), an existing low-cost, embedded, real-time DSP that has very wide deployment and integration in several IoT industries is used as a case study for constructing real-time graph-based Coiflet wavelets that could be used for several analytic applications including deep learning pre-processing applications at the edge and fog layers of IIoT networks. Our implementation is the first known application of the fixed-point C28x DSP to construct Coiflet wavelets. Coiflet Wavelets are constructed in the form of an osmotic microservice, using embedded low-level machine language to program the C28x at the network edge. With the graph-based approach, it is shown that an entire Coiflet wavelet distribution could be generated from only one wavelet stored in the C28x based edge device, and this could lead to significant savings in memory at the edge of IoT networks. Pearson correlation coefficient is used to select an edge generated Coiflet wavelet and the selected wavelet is used at the fog layer for pre-processing and denoising IIoT data to improve data quality for fog layer based deep learning application. Parameters for implementing deep learning at the fog layer using LSTM networks have been determined in the cloud. For XAI, communication network noise is shown to have significant impact on results of predictive deep learning at IIoT network fog layer.

Poisoning attack in which an adversary misleads the learning process by manipulating its training set significantly affect the performance of classifiers in security applications. This paper proposed a robust learning method which reduces the influences of attack samples on learning. The sensitivity, defined as the fluctuation of the output with small perturbation of the input, in Localized Generalization Error Model (L-GEM) is measured for each training sample. The classifier's output on attack samples may be sensitive and inaccurate since these samples are different from other untainted samples. An import score is assigned to each sample according to its localized generalization error bound. The classifier is trained using a new training set obtained by resampling the samples according to their importance scores. RBFNN is applied as the classifier in experimental evaluation. The proposed model outperforms than the traditional one under the well-known label flip poisoning attacks including nearest-first and farthest-first flips attack.

Among many research efforts devoted to automated art investigations, the problem of quantification of literary style remains current. Meanwhile, linguists and computer scientists have tried to sort out texts according to their types or authors. We use the recently-introduced p-leader multifractal formalism to analyze a corpus of novels written for adults and young adults, with the goal of assessing if a difference in style can be found. Our results agree with the interpretation that novels written for young adults largely follow conventions of the genre, whereas novels written for adults are less homogeneous.

As increasingly more enterprises are deploying cloud file-sharing services, this adds a new channel for potential insider threats to company data and IPs. In this paper, we introduce a two-stage machine learning system to detect anomalies. In the first stage, we project the access logs of cloud file-sharing services onto relationship graphs and use three complementary graph-based unsupervised learning methods: OddBall, PageRank and Local Outlier Factor (LOF) to generate outlier indicators. In the second stage, we ensemble the outlier indicators and introduce the discrete wavelet transform (DWT) method, and propose a procedure to use wavelet coefficients with the Haar wavelet function to identify outliers for insider threat. The proposed system has been deployed in a real business environment, and demonstrated effectiveness by selected case studies.

In order to realize the accurate positioning and recognition effectively of the analog circuit, the feature extraction of fault information is an extremely important port. This arrival based on the experimental circuit which is designed as a failure mode to pick-up the fault sample set. We have chosen two methods, one is the combination of wavelet transform and principal component analysis, the other is the factorial analysis for the fault data's feature extraction, and we also use the extreme learning machine to train and diagnose the data, to compare the performance of these two methods through the accuracy of the diagnosis. The results of the experiment shows that the data which we get from the experimental circuit, after dealing with these two methods can quickly get the fault location.

More and more advanced persistent threat attacks has happened since 2009. This kind of attacks usually use more than one zero-day exploit to achieve its goal. Most of the times, the target computer will execute malicious program after the user open an infected compound document. The original detection method becomes inefficient as the attackers using a zero-day exploit to structure these compound documents. Inspired by the detection method based on structural entropy, we apply wavelet analysis to malicious document detection system. In our research, we use wavelet analysis to extract features from the raw data. These features will be used todetect whether the compound document was embed malicious code.

In view of the difficulty in selecting wavelet base and decomposition level for wavelet-based de-noising method, this paper proposes an adaptive de-noising method based on Ensemble Empirical Mode Decomposition (EEMD). The autocorrelation, cross-correlation method is used to adaptively find the signal-to-noise boundary layer of the EEMD in this method. Then the noise dominant layer is filtered directly and the signal dominant layer is threshold de-noised. Finally, the de-noising signal is reconstructed by each layer component which is de-noised. This method solves the problem of mode mixing in Empirical Mode Decomposition (EMD) by using EEMD and combines the advantage of wavelet threshold. In this paper, we focus on the analysis and verification of the correctness of the adaptive determination of the noise dominant layer. The simulation experiment results prove that this de-noising method is efficient and has good adaptability.
 

Reduction of Quality (RoQ) attack is a stealthy denial of service attack. It can decrease or inhibit normal TCP flows in network. Victims are hard to perceive it as the final network throughput is decreasing instead of increasing during the attack. Therefore, the attack is strongly hidden and it is difficult to be detected by existing detection systems. Based on the principle of Time-Frequency analysis, we propose a two-stage detection algorithm which combines anomaly detection with misuse detection. In the first stage, we try to detect the potential anomaly by analyzing network traffic through Wavelet multiresolution analysis method. According to different time-domain characteristics, we locate the abrupt change points. In the second stage, we further analyze the local traffic around the abrupt change point. We extract the potential attack characteristics by autocorrelation analysis. By the two-stage detection, we can ultimately confirm whether the network is affected by the attack. Results of simulations and real network experiments demonstrate that our algorithm can detect RoQ attacks, with high accuracy and high efficiency.

Biometrics is attracting increasing attention in privacy and security concerned issues, such as access control and remote financial transaction. However, advanced forgery and spoofing techniques are threatening the reliability of conventional biometric modalities. This has been motivating our investigation of a novel yet promising modality transient evoked otoacoustic emission (TEOAE), which is an acoustic response generated from cochlea after a click stimulus. Unlike conventional modalities that are easily accessible or captured, TEOAE is naturally immune to replay and falsification attacks as a physiological outcome from human auditory system. In this paper, we resort to wavelet analysis to derive the time-frequency representation of such nonstationary signal, which reveals individual uniqueness and long-term reproducibility. A machine learning technique linear discriminant analysis is subsequently utilized to reduce intrasubject variability and further capture intersubject differentiation features. Considering practical application, we also introduce a complete framework of the biometric system in both verification and identification modes. Comparative experiments on a TEOAE data set of biometric setting show the merits of the proposed method. Performance is further improved with fusion of information from both ears.