Intrusion Signal Research Articles

Small area purification and recognition of network intrusion signals based on the second-order matching filter detection

Small area purification and recognition of network intrusion signals based on the second-order matching filter detection

Small area purification and recognition of network intrusion signals based on the second-order matching filter detection

Small area purification and recognition of network intrusion signals based on the second-order matching filter detection

Semi-supervised learning for optical fiber sensor road intrusion signal detection.

This paper proposes a road intrusion detection model based on distributed optical fiber vibration sensors signals. Considering that the existing unsupervised classification method often has a high false alarm rate when meeting the new non-intrusion samples, we propose a one-dimensional semi-supervised generative adversarial network (1D-SSGAN) model for intrusion signal recognition. The 1D-SSGAN is composed of a generator and a discriminator. The output layer of the discriminator is mapped to N+1 classes, and the generator and discriminator are trained on the N class dataset. During the learning process of the generator against the discriminator, many new samples are generated based on a small number of samples, which effectively expands the datasets and assists the training of the discriminator. Experimental result analysis demonstrates the effectiveness of the proposed model.

Event Recognition System Based on Fiber Bragg Grating and mRMR-CWCs-SCN

Traditional algorithm lacks the ability to generalize across different application scenarios and how to set the stopping conditions of network iteration in Stochastic Configuration Network (SCN) is a problem. This paper proposes an event recognition system based on minimum redundancy, maximum relevance (mRMR) and a modified Stochastic Configuration Network (CWCs-SCN) for the fiber Bragg grating (FBG) sensor system. mRMR is used to analyze the importance of 14th time-frequency domain features in different application scenarios. After that, a modified SCN (CWCs-SCN) neural network is used to solve the stopping condition of the network iteration. The experiments demonstrated that the average recognition accuracy of four kind of human intrusion signal can reach to 96.7%, which is better than others traditional methods. In addition, a more compact neural network is obtained compared to SCN. To verify the generalization ability of the proposed model, it is applied to gear fault diagnosis with an accuracy of 96.3%. This imply that the proposed method in this paper has a wide application prospect.

A Fiber Vibration Signal Identification Method Based on the Combination of EWT-FE and LSTM

As the optical fiber perimeter security system is widely used in real life, how to identify the types of intrusion events in a timely and effective manner is becoming a major research hotspot. At present, in this field, various signal feature extraction algorithms are usually used to extract intrusion signal features to form feature vectors, and then machine learning algorithms are used to classify the feature vectors to achieve the role of identifying the types of intrusion events. As a common signal feature extraction algorithm, the EMD algorithm has been widely used in the feature extraction of various vibration signals, but it will have the problem of modal aliasing and affect the feature extraction effect of the signal. Therefore, EWT, VMD and other algorithms have been successively used proposed to improve modal aliasing. On the basis of fully comparing the existing algorithms, this paper proposes a fiber vibration signal identification method that decomposes the signal through the empirical wavelet transform (EWT) algorithm and then extracts the fuzzy entropy (FE) of each component, and uses LSTM for classification. The final experiment shows that the method can identify four kinds of fiber intrusion signals in time and effectively, with an average recognition accuracy rate of 97.87%, especially for flap and knock recognition rate of 100%.

A 350-year multiproxy record of climate-driven environmental shifts in the Amundsen Sea Polynya, Antarctica

With a growing concern over rapid Antarctic ice loss in recent years, the Amundsen Sea, one of the fastest-melting areas in Antarctica, currently becomes a hotspot for the Earth sciences in the context of its linkage to global climate. As a center of strong physical and biological coupling processes, polynyas of the Amundsen Sea could act as sentinels of changes in atmosphere–ice–ocean interactions, offering a unique perspective into its sensitivity to climate variability. Here, we present a new, multiproxy-based high-resolution sedimentary record from the Amundsen Sea polynya, which provides new insights into environmental conditions of the region over the last 350 years and their linkages to climatic factors. Our results show that the polynya witnessed step-wise environmental shifts in parallel with the phases and strength of large-scale climate patterns, i.e., the Southern Annular Mode (SAM) and El Niño–Southern Oscillation (ENSO). Notably, intersite correlation of on-shelf Circumpolar Deep Water (CDW) intrusion signals at different locals suggests that the CDW may have gained increased access to the shelves at the time of a strong coupling of positive SAM and El Niño states. We tentatively speculate that anomalous large-scale atmospheric and oceanic circulation patterns over the Southern Hemisphere, forced by increasing greenhouse gas levels, were strongly involved in the mid-20 th century CDW invigoration, which may be greater in scale that goes well beyond the Amundsen Sea region. This result is relevant to the current debate on spatial heterogeneity in the timing and phasing of major climatic events in Antarctica, underscoring an unambiguous connection of the Antarctic climate state to the large-scale ocean–atmosphere reorganizations. Our study also extends a growing evidence that today's global warming trend is expected to have a severe effect on future configuration of Antarctic continental ice-shelf environment. • Sedimentary data capture major shifts in the Amundsen Sea polynya environment. • Stepwise environmental shifts are linked to large-scale climate variabilities. • The mid-20th century's continental-scale CDW increase under anthropogenic forcings. • Today's global warming trend may severely affect future configuration of Antarctica.

Detection and recognition of distributed optical fiber intrusion signal

Distributed acoustic sensing (DAS) technology can detect acoustic or vibration signals with high sensitivity and wide dynamic range by receiving the phase information from coherent Rayleigh scattered light. Linear quanti-zation is used to measure high fidelity restoration of the signals. With the increasing demand of practical applications, the optical fiber intrusion detection field has put forward higher requirements for event location and identification, which is manifested as the accurate classification of intrusion events. Therefore, the combination of distributed acoustic sensing and pattern recognition (PR) technology is a hot research topic at present. This is beneficial to promote the application and development of distributed optical fiber sensing technology. The research progress of the pattern recognition technology applied to distributed optical fiber intrusion detection in recent years is summarized in this paper, which can be used for feature extraction and classification algorithm research progress. In this paper, several feature extraction methods for realizing intrusion event signal recognition and their feature selection difficulties in different application situations are reviewed. Meanwhile, the advantages and disadvantages of specific event recognition algorithm are analyzed and summarized.

An Environmental Intrusion Detection Technology Based on WiFi

The traditional intrusion detection technology has some shortcomings, such as high hardware requirements and harsh detection conditions etc. This paper proposes an environment intrusion detection technology based on WiFi, which uses the existing WiFi network to realize security monitoring function, covers a wide range and does not expose privacy. Firstly, the technology uses median filtering to denoise the subcarriers in the channel, and then using the self-organizing competitive neural network algorithm for fingerprint feature extraction and establish the intrusion signal. Finally, the statistical model of the nonlinear dependence between the intrusion and the fingerprint database is obtained by using the classification of normalized exponential function, to achieve the purpose of intrusion detection. The experimental results show that the recognition rate of this technology is improved by nearly 8% compared with the existing methods, reaching 98%, which has a good development prospect.

Siamese Neural Network Based Few-Shot Learning for Anomaly Detection in Industrial Cyber-Physical Systems

With the increasing population of Industry 4.0, both AI and smart techniques have been applied and become hotly discussed topics in industrial cyber-physical systems (CPS). Intelligent anomaly detection for identifying cyber-physical attacks to guarantee the work efficiency and safety is still a challenging issue, especially when dealing with few labeled data for cyber-physical security protection. In this article, we propose a few-shot learning model with Siamese convolutional neural network (FSL-SCNN), to alleviate the over-fitting issue and enhance the accuracy for intelligent anomaly detection in industrial CPS. A Siamese CNN encoding network is constructed to measure distances of input samples based on their optimized feature representations. A robust cost function design including three specific losses is then proposed to enhance the efficiency of training process. An intelligent anomaly detection algorithm is developed finally. Experiment results based on a fully labeled public dataset and a few labeled dataset demonstrate that our proposed FSL-SCNN can significantly improve false alarm rate (FAR) and F1 scores when detecting intrusion signals for industrial CPS security protection.

Pattern Recognition of Grating Perimeter Intrusion Behavior in Deep Learning Method

An intrusion behavior recognition method based on deep learning is proposed in this paper in order to improve the recognition accuracy of raster perimeter intrusion behavior. The Mach–Zehnder fiber optic interferometer was used to collect the external vibration signal sensing unit, capture the external vibration signal, use the cross-correlation characteristic method to obtain the minimum frame length of the fiber vibration signal, and preprocess the intrusion signal according to the signal strength. The intrusion signals were superimposed and several sections of signals were intercepted by fixed window length; the spectrum information is obtained by Fourier transform of the intercepted stationary signals. The convolution neural network was introduced into the pattern recognition of the intrusion signals in the optical fiber perimeter defense zone, and the different characteristics of the intrusion signals were extracted, so as to realize the accurate identification of different intrusion signals. Experimental results showed that this method was highly sensitive to intrusion events, could effectively reduce the false alarm rate of intrusion signals, and could improve the accuracy and efficiency of intrusion signal recognition.

Robust Intrusion Events Recognition Methodology for Distributed Optical Fiber Sensing Perimeter Security System

Accurately detecting man-made intrusion from different events is of great significance for distributed optical fiber sensing perimeter security system. Most traditional algorithms lack the ability to reject various events of unknown class which are mainly from natural disturbance, and greatly decline the accuracy of intrusion recognition in field application. In order to solve this problem, we proposed a novel robust intrusion event recognition scheme based on convolutional prototype network (CPL), which realized end-to-end feature extraction and recognition based on the similarity of intrusion signals by integrating relevant variables of prototype learning into the training process of multiscale convolutional neural network (MSCNN) as trainable parameters, and had the ability to recognize and reject the unknown disturbance events. In field experiments, the average recognition accuracy of intrusion events as known class can reach 84.67%, with the rejection rate of disturbance events as unknown class is about 83.75%, which ensure the accuracy of intrusion events monitoring in complex field environments. And the recognition response time is about 17 ms, which also meets the need of real-time monitoring.

A Weighted Partial Domain Adaptation for Acoustic Scene Classification and Its Application in Fiber Optic Security System

Domain adaptation (DA) is a technology that transfers knowledge from the source domain to the target domain. General domain adaptation assume that the source and the target domain have the same label space. However, in practical application tasks, the label of the target domain is often only a subset of the source label. For this situation, partial domain adaptation is usually used as an effective solution to transfer knowledge from a large number of labeled data sets to unlabeled micro data sets. In this article, a weighted partial domain adaptation method is proposed to solve the Acoustic Scene Classification (ASC) problem. Our method establish a connection between source and target domains to do the partial domain adaptation. Experiments are carried out on TUT and ESC-50 datasets which show that our method achieves state-of-the-art results. What is more, we apply the algorithms to an optical fiber perimeter security system to complete early warning by identifying intrusion signals.

Open-Set Events Identification Based on Deep Metric-Learning for DMZI Perimeter System

Intrusion monitoring based on optical fiber distributed sensing is a pattern recognition problem of target events. Due to the addition of uncontrollable unknown events in practical applications, the open-set problem brought about greatly reduces the accuracy of traditional events recognition methods. In order to solve this problem, this paper design a deep metric-learning network, combined with Recurrent Plot (RP) coding to improve the accuracy of target events recognition in an open environment. In this paper, the RP algorithm is used to encode the intrusion signals into images that reflect the signal’s motion. The deep metric-learning network is used to project the image into the feature space. And feature centers of each class can be calculated rely on the training samples. So, the network identifies the intrusion event according to the distance between the test sample and the feature center. By setting the appropriate threshold, the samples whose distance exceeds the threshold are identified as unknown class to solve the open-set problem. In the experiments, the dual Mach-Zehnder interferometry (DMZI) distributed optical fiber perimeter system is built to collect event signals. For 7 known event classes the highest identification accuracy can reach 99.7%. After adding 3 unknown event classes, the accuracy rate can exceed 93.3% with appropriate threshold. And the total response time is only 0.4 s. This novel method guarantees the accuracy of target identification under the introduction of unknown events and improves the robustness of the distributed optical fiber perimeter security system.

An anti-noise ϕ-OTDR based distributed acoustic sensing system for high-speed railway intrusion detection

We present an anti-noise ϕ-optical time-domain reflectometer-based distributed acoustic sensing system that can effectively differentiate noise and interference for high-speed railway intrusion detection. A distributed acoustic sensing interrogator unit, based on digital heterodyne detection, was deployed in a real field railway station and three types of intrusion signals were collected, including wall climbing, wall breaking, and barbed wire crossing. Sensing signals were analyzed and identified by a comprehensive deep model which consisted of a temporal relation extraction module and a spatial feature encoding module. A novel hierarchical structure of the convolutional long short-term memory network was designed for temporal pattern analysis and spatial features were extracted by a convolution neural network. To accelerate computation, signals with lower energy were filtered out and the combined spatial-temporal features were used for classification. The experiment on real field data achieved over 90% of the threat detection rate, with an approximately 10% false alarm rate, under various parameter settings. The anti-noise performance was compared with the latest high-speed railway intrusion system and it demonstrated a significant improvement of noise and threat identification.

Distributed Optical Fiber Sensing Intrusion Pattern Recognition Based on GAF and CNN

This article proposes an intrusion pattern recognition scheme based on Gramian Angular Field (GAF) and convolutional neural network (CNN) for the dual Mach-Zehnder Interference (DMZI) distributed fiber perimeter security system, which has the advantages of fast recognition speed and high recognition accuracy rate. Compared with traditional recognition algorithms lack the ability to extract deep features, GAF algorithm is used to transform the intrusion signals of 1-D time series to 2-D images which can present more deep features and maintain the time domain dependence of the signals, meanwhile, each intrusion signal corresponds to a unique fingerprint. Then, this GAF algorithm combined with the appropriate CNN model is able to extract deep features and recognize the intrusion patterns accurately. Besides, GAF algorithm is insensitive to the fluctuation of power source in the optical path, so the robustness as well as the practicability of the system are improved effectively. The experiments demonstrated that the average recognition accuracy rate of three common natural intrusion events and three human intrusion events on fence (wind blowing, light rain, heavy rain, knocking, impacting and slapping) can reach to 97.67%, and the detection response time is only about 0.58 s, which can meet the requirements of real-time emergency monitoring.

Event classification using improved salp swarm algorithm based probabilistic neural network in fiber-optic perimeter intrusion detection system

Abstract To remarkably upgrade the performance of the intrusion detection system based on single-mode—multimode—single-mode (SMS) fiber structure which can effectively discriminate man-made event and natural event in zone perimeter, a novel event classification approach is presented in this paper. Firstly, the original intrusion signal is decomposed by the filter bank into four frequency channels with an interval of 11745 Hz. Then, the singular value and kurtosis extracted from each frequency channel as combinations of eigenvectors, being transferred to the probabilistic neural network (PNN) for training and classification. Finally, the weight factor and adaptive mutation operator are embedded in the salp swarm algorithm (SSA), which is utilized to optimize and search for the optimal smoothing factor of the PNN classifier and compared with the other four metaheuristic (MH) algorithms under five algorithm performance evaluation metrics. Large practical experiments exhibit that the presented scheme can accurately recognize man-made events (knocking, rattling) and natural events (wind, rain) respectively, shedding light on the feasibility and applicability of SMS fiber structure in zone perimeter.

User information intrusion prediction method based on empirical mode decomposition and spectrum feature detection

In distributed intelligent computing environment, user information is vulnerable to plaintext intrusion, resulting in information leakage. In order to ensure the security of user information, a user information intrusion prediction method based on empirical mode decomposition and spectrum feature detection in distributed intelligent computing is proposed in this paper. Firstly, a model of user information and intrusion signal in distributed intelligent computing is established; then an intrusion detection model is established with signal processing method; finally, time-frequency analysis and feature decomposition are conducted for intrusion information in distributed intelligent computing with empirical mode decomposition method, and accurate prediction of user intrusion information is achieved based on joint probability density distribution of spectrum feature, so as to improve the algorithm design. The simulation results show that when the signal to noise ratio is 12.4 dB, the detection probability of the method proposed in this paper is 1, and then the false alarm probability can be 0, which indicates that this method can provide good intrusion detection probability and low false alarm probability even at relatively low signal to noise ratio. Therefore, the method proposed in this paper has good intrusion interception and prediction ability.

User information intrusion prediction method based on empirical mode decomposition and spectrum feature detection

In distributed intelligent computing environment, user information is vulnerable to plaintext intrusion, resulting in information leakage. In order to ensure the security of user information, a user information intrusion prediction method based on empirical mode decomposition and spectrum feature detection in distributed intelligent computing is proposed in this paper. Firstly, a model of user information and intrusion signal in distributed intelligent computing is established; then an intrusion detection model is established with signal processing method; finally, time-frequency analysis and feature decomposition are conducted for intrusion information in distributed intelligent computing with empirical mode decomposition method, and accurate prediction of user intrusion information is achieved based on joint probability density distribution of spectrum feature, so as to improve the algorithm design. The simulation results show that when the signal to noise ratio is 12.4 dB, the detection probability of the method proposed in this paper is 1, and then the false alarm probability can be 0, which indicates that this method can provide good intrusion detection probability and low false alarm probability even at relatively low signal to noise ratio. Therefore, the method proposed in this paper has good intrusion interception and prediction ability.

Research on network intrusion detection security based on improved extreme learning algorithms and neural network algorithms

In order to improve the ability of network fuzzy intrusion detection, a network intrusion detection method based on improved extreme learning algorithm and neural network algorithm is proposed to improve the security of the network. ARMA and other linear detection methods are used to construct the network intrusion signal model, and the nonlinear time series and chaos analysis methods are used to extract the feature of network intrusion and big data information analysis. The limit learning method is used for active detection of network intrusion; the adaptive learning method is used for iterative analysis of network intrusion detection, and the correlation characteristic decomposition method is used to improve the convergence of network intrusion detection. The fuzzy neural network algorithm is used to classify the network intrusion features to improve the intrusion detection performance. The simulation results show that this method has high accuracy and strong anti-jamming ability; it has good application value in network security.

Research on network intrusion detection security based on improved extreme learning algorithms and neural network algorithms

In order to improve the ability of network fuzzy intrusion detection, a network intrusion detection method based on improved extreme learning algorithm and neural network algorithm is proposed to improve the security of the network. ARMA and other linear detection methods are used to construct the network intrusion signal model, and the nonlinear time series and chaos analysis methods are used to extract the feature of network intrusion and big data information analysis. The limit learning method is used for active detection of network intrusion; the adaptive learning method is used for iterative analysis of network intrusion detection, and the correlation characteristic decomposition method is used to improve the convergence of network intrusion detection. The fuzzy neural network algorithm is used to classify the network intrusion features to improve the intrusion detection performance. The simulation results show that this method has high accuracy and strong anti-jamming ability; it has good application value in network security.