Biblio

Previous research on frequency hopping (FH) signal recognition utilizing deep learning only focuses on single-label signal, but can not deal with overlapped FH signal which has multi-labels. To solve this problem, we propose a new FH signal recognition method based on fully convolutional networks (FCN). Firstly, we perform the short-time Fourier transform (STFT) on the collected FH signal to obtain a two-dimensional time-frequency pattern with time, frequency, and intensity information. Then, the pattern will be put into an improved FCN model, named FH-FCN, to make a pixel-level prediction. Finally, through the statistics of the output pixels, we can get the final classification results. We also design an algorithm that can automatically generate dataset for model training. The experimental results show that, for an overlapped FH signal, which contains up to four different types of signals, our method can recognize them correctly. In addition, the separation of multiple FH signals can be achieved by a slight improvement of our method.

Specific emitter identification (SEI) is a physical-layer-based approach for enhancing wireless communication network security. A well-done SEI method can be widely applied in identifying the individual wireless communication device. In this paper, we propose a novel specific emitter identification method based on variational mode decomposition and histogram of oriented gradient (VMD-HOG). The signal is decomposed into specific temporal modes via VMD and HOG features are obtained from the time-frequency spectrum of temporal modes. The performance of the proposed method is evaluated both in single hop and relaying scenarios and under three channels with the number of emitters varying. Results depict that our proposed method provides great identification performance for both simulated signals and realistic data of Zigbee devices and outperforms the two existing methods in identification accuracy and computational complexity.

The spatial-temporal random variation characteristics of underwater acoustic channel make the difference among the underwater acoustic communication network link channels, which make network performance difficult to predict. In order to better understand the fluctuation and difference of network link channel, we analyze the measured channel data of five links in the Qiandao Lake underwater acoustic communication network experiment. This paper first estimates impulse response, spread function, power delay profile and Doppler power spectrum of the time-varying channel in a short detection time, and compares the time-frequency energy distribution characteristics of each link channel. Then, we statistically analyze the discreteness of the signal to noise ratio, multipath spread and Doppler spread parameter distributions for a total of145 channels over a long observation period. The results show that energy distribution structure and fading fluctuation scale of each link channel in underwater acoustic communication network are obviously different.

A comparative analysis of the classical and approximation methods of spectral analysis of fast-variable processes in technical systems is carried out. It is shown that the approximation methods make it possible to substantially remove the contradiction between the requirements for spectrum smoothing and its frequency resolution. On practical examples of vibroacoustic signals, the effectiveness of approximation methods is shown. The Prony method was used to process the time series. The interactive frequency segmentation method and the direct identification method were used for approximation and frequency characteristics.

The growing internet-based services such as banking and shopping have brought both ease to human's lives and challenges in user identity authentication. Different methods have been investigated for user authentication such as retina, finger print, and face recognition. This study introduces a photoplethysmogram (PPG) based user identity authentication relying on textural features extracted from time-frequency image. The PPG signal is segmented into segments and each segment is transformed into time-frequency domain using continuous wavelet transform (CWT). Then, the textural features are extracted from the time-frequency images using Haralick's method. Finally, a classifier is employed for identity authentication purposes. The proposed system achieved an average accuracy of 99.14% and 99.9% with segment lengths of one and tweeny seconds, respectively, using random forest classifier.

The accurate classification of WiFi-based activity patterns is still an open problem and is critical to detect behavior for non-visualization applications. This paper proposes a novel approach that uses WiFi-based IQ data and short-time Fourier transform (STFT) time-frequency images to automatically and accurately classify human activities. The offsets features, calculated from time-domain values and one-dimensional principal component analysis (1D-PCA) values and two-dimensional principal component analysis (2D-PCA) values, are applied as features to input the classifiers. The machine learning methods such as the bagging, boosting, support vector machine (SVM), random forests (RF) as the classifier to output the performance. The experimental data validate our proposed method with 15000 experimental samples from five categories of WiFi signals (empty, marching on the spot, rope skipping, both arms rotating;singlearm rotating). The results show that the method companying with the RF classifier surpasses the approach with alternative classifiers on classification performance and finally obtains a 62.66% classification rate, 85.06% mean accuracy, and 90.67% mean specificity.

This work describes a concept for extending the Network Time Security (NTS) protocol to enable implementation- independent communication between the NTS key establishment (NTS-KE) server and the connected time server(s). It Alls a specification gap left by RFC 8915 for securing the Network Time Protocol (NTP) and enables the centralized and public deployment of an NTS key management server that can support both secured NTP and secured PTP.

We present a collected overview on how to assess both the accuracy and reliability levels and relate them to the required effort, for different digital methods of synchronizing clocks. The presented process is intended for end users who require time synchronization but are not certain about how to judge at least one of the aspects. It can not only be used on existing technologies but should also be transferable to many future approaches. We further relate this approach to several examples. We discuss in detail the approach of medium-range White Rabbit connections over dedicated fibers, a method that occupies an extreme corner in the evaluation, where the effort is exceedingly high, but also yields excellent accuracy and significant reliability.

Recently, the informatics infrastructure of INRiM Time and Frequency Laboratory has been completely renewed with particular attention to network security and software architecture aspects, with the aims to improve the reliability, robustness and automation of the overall set-up. This upgraded infrastructure has allowed, since January 2020, a fully automated generation and monitoring of the Italian time scale UTC(IT), based on dedicated software developed in-house [1]. We focus in this work on the network and software aspects of our set-up, which enable a robust and reliable automatic time scale generation with continuous monitoring and minimal human intervention.

The classification of encrypted traffic has received increased attention by the research community in the cyber-security domains and network management domains. Classification of encrypted traffic can also expose privacy threats as the activities of an user can be detected and identified. This paper investigates the novel application of Time Frequency analysis to encrypted traffic classification. Features extracted from encrypted traffic are normalized and transformed to time series on which different time frequency transforms are applied. In particular, the constant-Q transform (CQT), the Continuous Wavelet Transform and the Wigner-Ville distribution are used. Then, different machine learning algorithms are applied to identify the different types of traffic. This approach is validated with the public ISCX VPN-nonVPN traffic dataset with time-based features extracted from the encrypted traffic. The results show the superior classification performance (evaluated using identification, precision and recall metrics) of the time frequency approach across different machine learning algorithms. Because analysis of encrypted traffic can also generate privacy threats, a technique to obfuscate the time based features and reduce the classification performance is also applied and successfully validated.

In the process of informationization and networking of smart grids, the original physical isolation was broken, potential risks increased, and the increasingly serious cyber security situation was faced. Therefore, it is critical to develop accuracy and efficient anomaly detection methods to disclose various threats. However, in the industry, mainstream security devices such as firewalls are not able to detect and resist some advanced behavior attacks. In this paper, we propose a time series anomaly detection model, which is based on the periodic extraction method of discrete Fourier transform, and determines the sequence position of each element in the period by periodic overlapping mapping, thereby accurately describe the timing relationship between each network message. The experiments demonstrate that our model can detect cyber attacks such as man-in-the-middle, malicious injection, and Dos in a highly periodic network.

In order to solve the problems of the existing speech content authentication algorithm, such as single format, ununiversal algorithm, low security, low accuracy of tamper detection and location in small-scale, a multi-format speech perception hashing based on time-frequency parameter fusion of energy zero ratio and frequency band bariance is proposed. Firstly, the algorithm preprocesses the processed speech signal and calculates the short-time logarithmic energy, zero-crossing rate and frequency band variance of each speech fragment. Then calculate the energy to zero ratio of each frame, perform time- frequency parameter fusion on time-frequency features by mean filtering, and the time-frequency parameters are constructed by difference hashing method. Finally, the hash sequence is scrambled with equal length by logistic chaotic map, so as to improve the security of the hash sequence in the transmission process. Experiments show that the proposed algorithm is robustness, discrimination and key dependent.

Over the years, technology has reformed the perception of the world related to security concerns. To tackle security problems, we proposed a system capable of detecting security alerts. System encompass audio events that occur as an outlier against background of unusual activity. This ambiguous behaviour can be handled by auditory classification. In this paper, we have discussed two techniques of extracting features from sound data including: time-based and signal based features. In first technique, we preserve time-series nature of sound, while in other signal characteristics are focused. Convolution neural network is applied for categorization of sound. Major aim of research is security challenges, so we have generated data related to surveillance in addition to available datasets such as UrbanSound 8k and ESC-50 datasets. We have achieved 94.6% accuracy for proposed methodology based on self-generated dataset. Improved accuracy on locally prepared dataset demonstrates novelty in research.

The CPS-featured modern asynchronous grids interconnected with HVDC tie-lines facing the hazards from bulk power imbalance shock. With the aid of cyber layer, the SCPIFS incorporates the frequency stability constrains is put forwarded. When there is bulk power imbalance caused by HVDC tie-lines block incident or unplanned loads increasing, the proposed SCPIFS ensures the safety and frequency stability of both grids at two terminals of the HVDC tie-line, also keeps the grids operate economically. To keep frequency stability, the controllable variables in security control strategy include loads, generators outputs and the power transferred in HVDC tie-lines. McCormick envelope method and ADMM are introduced to solve the proposed SCPIFS optimization model. Case studies of two-area benchmark system verify the safety and economical benefits of the SCPFS. HVDC tie-line transferred power can take the advantage of low cost generator resource of both sides utmost and avoid the load shedding via tuning the power transferred through the operating tie-lines, thus the operation of both connected asynchronous grids is within the limit of frequency stability domain.

The digital low dropout regulators are widely used because it can operate at low supply voltage. In the digital low drop-out regulators, the high sampling frequency circuit has a short setup time, but it will produce overshoot, and then the output can be stabilized; although the low sampling frequency circuit output can be directly stabilized, the setup time is too long. This paper proposes a two sampling frequency circuit model, which aims to include the high and low sampling frequencies in the same circuit. By controlling the sampling frequency of the circuit under different conditions, this allows the circuit to combine the advantages of the circuit operating at different sampling frequencies. This shortens the circuit setup time and the stabilization time at the same time.

In this study we propose a novel method for drone surveillance that can simultaneously analyze time-frequency responses in all pixels of a high-frame-rate video. The propellers of flying drones rotate at hundreds of Hz and their principal vibration frequency components are much higher than those of their background objects. To separate the pixels around a drone's propellers from its background, we utilize these time-series features for vibration source localization with pixel-level short-time Fourier transform (STFT). We verify the relationship between the number of taps in the STFT computation and the performance of our algorithm, including the execution time and the localization accuracy, by conducting experiments under various conditions, such as degraded appearance, weather, and defocused blur. The robustness of the proposed algorithm is also verified by localizing a flying multi-copter in real-time in an outdoor scenario.

Attacks by Jamming on wireless communication network can provoke Denial of Services. According to the communication system which is affected, the consequences can be more or less critical. In this paper, we propose to develop an algorithm which could be implemented at the reception stage of a communication terminal in order to detect the presence of jamming signals. The work is performed on Wi-Fi communication signals and demonstrates the necessity to have a specific signal processing at the reception stage to be able to detect the presence of jamming signals.

Aiming at the problems of poor stability and low accuracy of current communication data informatization processing methods, this paper proposes a research on nonlinear frequency hopping communication data informatization under the framework of big data security evaluation. By adding a frequency hopping mediation module to the frequency hopping communication safety evaluation framework, the communication interference information is discretely processed, and the data parameters of the nonlinear frequency hopping communication data are corrected and converted by combining a fast clustering analysis algorithm, so that the informatization processing of the nonlinear frequency hopping communication data under the big data safety evaluation framework is completed. Finally, experiments prove that the research on data informatization of nonlinear frequency hopping communication under the framework of big data security evaluation could effectively improve the accuracy and stability.

Since 2016, the northwest power grid has organized new energy sources to participate in the rapid frequency regulation research and carried out pilot test work at the sending end large power grid. The experimental results show that new energy generator has the ability to participate in the grid's rapid frequency regulation, and its performance is better than that of conventional power supply units. This paper analyses the requirements for fast frequency control of the sending end large power grid in northwest China, and proposes the segmented participation indexes of photovoltaic and wind power in the frequency regulation of power grids. In accordance with the idea of "clear responsibilities, various types of unit coordination", the parameter setting of new energy sources rapid frequency regulation is completed based on the coordinated control based on multi-frequency regulation resources in northwest power grid. The new energy fast frequency regulation model was established, through the PSASP power grid stability simulation program and the large-scale power grid stability simulation analysis was completed. The simulation results show that the wind power and photovoltaic adopting differential rapid frequency regulation parameters can better utilize the rapid frequency regulation capability of various types of power sources, realize the coordinated rapid frequency regulation of all types of units, and effectively improve the frequency security prevention and control level of the sending end large power grid.

A new approach to micro-Doppler signal analysis is presented in this article. Novel chirp rate estimators in the time-frequency domain were used for this purpose, which provided the chirp rate of micro-Doppler signatures, allowing the classification of objects in the urban environment. As an example verifying the method, a signal from a high-resolution radar with a linear frequency modulated continuous wave (FMCW) recording an echo reflected from a pedestrian was used to validate the proposed algorithms for chirp rate estimation. The obtained results are plotted on saturated accelerograms, giving an additional parameter dedicated for target classification in security systems utilizing radar sensors for target detection.

Multipath propagation of radio waves negatively affects to the performance of telecommunications and radio navigation systems. When performing time and frequency synchronization tasks of spatially separated standards, the multipath signal propagation aggravates the probability of a correct synchronization and introduces an error. The presence of a multipath signal reduces the signal-to-noise ratio in the received signal, which in turn causes an increase in the synchronization error. If the time delay of the additional beam (s) is less than the useful signal duration, the reception of the useful signal is further complicated by the presence of a partially correlated interference, the level and correlation degree of which increases with decreasing time delay of the interference signals. The article considers with the method of multi-path interference compensation in a multi-position (telecommunication or radio navigation system) or a time and frequency synchronization system for the case if at least one of the receiving positions has no noise signal or does not exceed the permissible level. The essence of the method is that the interference-free useful signal is transmitted to other points in order to pick out the interference component from the signal / noise mix. As a result, an interference-free signal is used for further processing. The mathematical models of multipath interference suppressors in the temporal and in the frequency domain are presented in the article. Compared to time processing, processing in the frequency domain reduces computational costs. The operation of the suppressor in the time domain has been verified experimentally.

Current BLE transmitters are susceptible to selective jamming due to long dwell times in a channel. To mitigate these attacks, we propose physical-layer security through an ultra-fast bit-level frequency-hopping (FH) scheme by exploiting the frequency agility of bulk acoustic wave resonators (BAW). Here we demonstrate the first integrated bit-level FH transmitter (TX) that hops at 1$μ$s period and uses data-driven random dynamic channel selection to enable secure wireless communications with additional data encryption. This system consists of a time-interleaved BAW-based TX implemented in 65nm CMOS technology with 80MHz coverage in the 2.4GHz ISM band and a measured power consumption of 10.9mW from 1.1V supply.

Modern embedded computing devices are vulnerable against malware and software piracy due to insufficient security scrutiny and the complications of continuous patching. To detect malicious activity as well as protecting the integrity of executable software, it is necessary to monitor the operation of such devices. In this paper, we propose a disassembler based on power-based side-channel to analyze the real-time operation of embedded systems at instruction-level granularity. The proposed disassembler obtains templates from an original device (e.g., IoT home security system, smart thermostat, etc.) and utilizes machine learning algorithms to uniquely identify instructions executed on the device. The feature selection using Kullback-Leibler (KL) divergence and the dimensional reduction using PCA in the time-frequency domain are proposed to increase the identification accuracy. Moreover, a hierarchical classification framework is proposed to reduce the computational complexity associated with large instruction sets. In addition, covariate shifts caused by different environmental measurements and device-to-device variations are minimized by our covariate shift adaptation technique. We implement this disassembler on an AVR 8-bit microcontroller. Experimental results demonstrate that our proposed disassembler can recognize test instructions including register names with a success rate no lower than 99.03% with quadratic discriminant analysis (QDA).

Recent advances in wireless sensors for personal healthcare allow to recognise human real-time activities with mobile devices. While the analysis of those datastream can have many benefits from a health point of view, it can also lead to privacy threats by exposing highly sensitive information. In this paper, we propose a privacy-preserving framework for activity recognition. This framework relies on a machine learning technique to efficiently recognise the user activity pattern, useful for personal healthcare monitoring, while limiting the risk of re-identification of users from biometric patterns that characterizes each individual. To achieve that, we first deeply analysed different features extraction schemes in both temporal and frequency domain. We show that features in temporal domain are useful to discriminate user activity while features in frequency domain lead to distinguish the user identity. On the basis of this observation, we second design a novel protection mechanism that processes the raw signal on the user's smartphone and transfers to the application server only the relevant features unlinked to the identity of users. In addition, a generalisation-based approach is also applied on features in frequency domain before to be transmitted to the server in order to limit the risk of re-identification. We extensively evaluate our framework with a reference dataset: results show an accurate activity recognition (87%) while limiting the re-identifation rate (33%). This represents a slightly decrease of utility (9%) against a large privacy improvement (53%) compared to state-of-the-art baselines.

The non-stationary nature of the human Electroencephalogram (EEG) has caused problems in EEG based biometrics. Stationary signals analysis is done simply with Discrete Fourier Transform (DFT), while it is not possible to analyze non-stationary signals with DFT, as it does not have the ability to show the occurrence time of different frequency components. The Fractional Fourier Transform (FrFT), as a generalization of Fourier Transform (FT), has the ability to exhibit the variable frequency nature of non-stationary signals. In this paper, Discrete Fractional Fourier Transform (DFrFT) with different fractional orders is proposed as a novel feature extraction technique for EEG based human verification with different number of channels. The proposed method in its' best performance achieved 0.22% Equal Error Rate (EER) with three EEG channels of 104 subjects.