Intrusion Signal Research Articles

Distributed fiber optic sensing signal recognition based on class-incremental learning

Distributed fiber optic sensing (DFOS) based on phase-sensitive optical time-domain reflectance (φ-OTDR) technology has outstanding performance in pipeline safety monitoring and perimeter security detection. Accurate identification of new events remains challenging due to environmental variability and emerging forms of intrusions. In order to solve the problem of failing to accurately identify new events due to the inability to obtain all samples at once in real-time monitoring, this paper proposes an incremental learning network framework for distributed fiber-optic sensing signal recognition. This framework integrates an optimized Learning without Memorizing (LwM) algorithm with an improved ConvNeXt network for dynamic training of new events. An improved Efficient Channel Attention (HECA) is incorporated to thoroughly extract the spatio-temporal features of the intrusion signals collected by the DFOS. The forgetting problem is mitigated during incremental learning using knowledge distillation and optimized Gradient Weighted Class Activation Mapping to generate an attention map. A linear correction layer is added after the output layer to correct the bias towards new classes by rebalancing the information between new and old classes. Experimental comparisons show that the recognition rate for 10 different intrusion signals exceeds 93 %, while the forgetting rate is reduced from a peak of 41.44 % to 5.25 %. The time required to process and train incremental learning for 1000 samples in real time on an edge device (NVIDIA 3050 GPU) is approximately 1060 s, its ability to demonstrating its suitability for deployment in resource-constrained.

High generalization identification method based on MI-SI distributed optical fiber sensor and video signals

A pattern recognition method for intrusion signals is introduced, leveraging distributed optical fiber sensors and convolutional neural networks. To enhance the model's generalization capabilities, two strategies are employed. Firstly, the sensor structure is refined, incorporating the strengths of both the Sagnac and Michelson interferometers to create a hybrid design with a wide frequency response. Comparative experiments confirm that this exceptional frequency response significantly boosts the model's generalization power. Secondly, to address the limitations of distributed fiber optic sensors, a multimodal approach is proposed, integrating video frame difference and fiber optic pattern recognition. The trained convolutional neural network model exhibits low computational overhead, robust generalization, and practical applicability. When applied to a dataset collected in diverse scenarios not included in the model training, the model achieved a CNR of 0% and a FPR of only 0.26% for the intrusion behavior of climbing fences.

Distributed fiber optic acoustic sensing system intrusion full event recognition based on 1-D MFEWnet

Proper detection of the full range of intrusion events is of paramount significance to distributed fiber optic sensing perimeter security systems. Traditional neural networks for intrusion event recognition are constrained by the training dataset, that is, they cannot detect intrusions outside of the training dataset. However, in real complex environments, the dataset by manually obtained is far fall short of encompassing all possible real-world data. This limitation can lead to inaccuracies of identification in the distributed fiber optic sensing system not being able to identify correctly, which causes immeasurable losses. In order to address the aforementioned issues, this paper presents a 1D MFEWnet model, which completes the effective differentiation of all datasets by means of a Multi-Feature branch 1-dimensional Convolution Neural Network, followed by fitting the activation vectors after the recognition of known datasets to a Weibull distribution, through the improved Euclidean distance tracing algorithm. This approach allows for the extraction and identification of additional intrusion signals while providing the ability to recognize and reject unknown interference events. In the experiments, a distributed fiber optic sensing system was established to collect event signals. For three known event categories, the highest recognition accuracy is up to 99.6%. After adding 2 unknown event categories randomly, the accuracy remained at a commendable 96.9%. This innovative methodology ensures the accuracy of target recognition under the introduction of all conceivable events and improves the robustness of the distributed fiber optic perimeter security system.

Identification of pipeline intrusion signals based on ICEEMDAN-FE-AIT and F-ELM in the uwDAS system

Identification of pipeline intrusion signals based on ICEEMDAN-FE-AIT and F-ELM in the uwDAS system

A spatial and temporal signal fusion based intelligent event recognition method for buried fiber distributed sensing system

Due to the characteristics of high sensitivity, fast response speed and multi-point monitoring, Phase-sensitive optical time-domain reflectometry (Φ-OTDR) has attracted attention in the field of perimeter security. However, the intrusion signals are susceptible to interference by ambient signals and difficult to be recognized. In the field of the vibration event recognition of Φ-OTDR system, the deep-learning based methods achieve great recognition ability. However, the previous works are mostly built on single signal source like temporal signal and may not completely adapt to different environment. In this work, a novel phenomenon is reported that a specific variation pattern of light intensity, which is related to the type of vibration source, is hided in the backscattering traces in spatial domain. Inspired by this, a lightweight model and data composition method is proposed to fuse spatial information with temporal correlation information based on end-to-end CNN-LSTM combined model and bicubic scaling. The experiment is conducted on a portable computer (a Nvidia GPU RTX 2080 with 2944 compute unified device architecture cores) with 8 event types and shows that this method can achieve 95.56% validation accuracy through less than 6 min training. Compared with previous method trained by single image structure signal, this lightweight work can achieve higher validation accuracy faster.

Smart robots’ virus defense using data mining technology

Abstract In order to realize online detection and control of network viruses in robots, the authors propose a data mining-based anti-virus solution for smart robots. First, using internet of things (IoT) intrusion prevention system design method based on network intrusion signal detection and feedforward modulation filtering design, the overall design description and function analysis are carried out, and then the intrusion signal detection algorithm is designed, and finally, the hardware design and software development for a breach protection solution for the IoT are completed, and the integrated design of the system is realized. The findings demonstrated that based on the mean value of 10,000 tests, the IoT’s average packet loss rate is 0. Conclusion: This system has high accuracy, good performance, and strong compatibility and friendliness.

Multichannel Classifier for Recognizing Acoustic Impacts Recorded with a phi-OTDR.

The purpose of this work is to increase the security of the perimeter of an area from unauthorized intrusions by creating an improved algorithm for classifying acoustic impacts recorded with a sensor system based on a phase-sensitive optical time reflectometer (phi-OTDR). The algorithm includes machine learning, so a dataset consisting of two classes was assembled. The dataset consists of two classes. The first class is the data of the steps, and the second class is other non-stepping influences (engine noise, a passing car, a passing cyclist, etc.). As an intrusion signal, a human walking signal is analyzed and recorded in frames of 5 s, which passed the threshold condition. Since, in most cases, the intruder moves on foot to overcome the perimeter, the analysis of the acoustic effects generated during the step will increase the efficiency of the perimeter detection tools. When walking quietly, step signals can be quite weak, and background signals can contain high energy and visually resemble the signals you are looking for. Therefore, an algorithm was created that processes space-time diagrams developed in real time, which are grayscale images. At the same time, during the processing of one image, two more images are calculated, which are the result of processing the denoised autoencoder and the created mathematical model of the adaptive correlation. Then, the three obtained images are fed to the input of the created three-channel neural network classifier, which includes convolutional layers for the automatic extraction of spatial features. The probability of correctly detecting steps is 98.3% and that of background actions is 97.93%.

Research on identification of multiple target signals based on φ-OTDR

Phase sensitive optical time domain reflectometer (φ-OTDR) is widely employed in pipeline safety early warning, perimeter security, Pipeline inspection gauge (PIG) positioning and tracking, etc. Generally, equipment is applied in outdoor environments. There is a high probability that multiple intrusion signals exist simultaneously at a common position. Therefore, it is significant to unmix and identify each signal. In this paper, a multi-target signal recognition method based on φ-OTDR is proposed. The basic principles of φ-OTDR system and fixed point algorithm based on negative entropy maximization (FASTICA) are briefly introduced. The blind source separation and recognition of the mixed signals of hammer and motor are carried out, which proves the effectiveness of the method. In order to further quantitatively evaluate the effect of separation, the feature extraction of hammer signal and motor signal is carried out from two aspects respectively from time and frequency domain. The feature vector describing signal characteristics in terms of both time and frequency domain is proposed and used as the evaluation criterion of the separation performance. The experimental results show that the separated signal basically conforms to the feature vector, and the time domain feature is the most stable, with a maximum error of only 3.08%.

Identification Method of Optical Fiber Perimeter Intrusion Signal Based on MATCN

Identification Method of Optical Fiber Perimeter Intrusion Signal Based on MATCN

Automatic anomaly identification of wireless communication signal based on nonlinear analysis

In order to effectively reduce the risk of high-risk intrusion signals to wireless communication networks, an automatic anomaly identification of wireless communication signals based on nonlinear analysis is proposed. The fuzzy phase frequency feature of denoised abnormal signal is extracted and the wireless communication signal parameters are estimated by optimizing the fuzzy network abnormal signal feature extraction method of particle filter. The wireless communication signal is collected based on nonlinear analysis, the wireless communication signal network model is constructed, and the Back Propagation Neural Network—Dempster Shafer method is used to classify and fuse the results of feature extraction to realize the automatic identification of wireless communication signal anomalies. The experimental results show that the signal initial frequency estimation and signal slope estimation obtained by the research method have high accuracy, strong coverage performance, large total amount of automatic identification abnormal signals and high compatibility level.

Identification of Intrusion Events Based on Distributed Optical Fiber Sensing in Complex Environment

With the development of economy, the construction of modern infrastructures has been gradually improved. Some infrastructures, such as factories and airports, have large construction volume and high security requirements. Once an intrusion occurs, it will pose a great threat to the safety of personnel and property. Therefore, the perimeter security system based on Internet of Things (IoT) devices and technology is important. Distributed optical fiber sensor (DOFS) is an emerging infrastructure for IoT. Owing to multipoint sensing at the same time, it has attracted extensive attention in the field of perimeter security. However, with the ambient interference, intrusion signals and ambient signals form mixed signals, which brings great difficulty to the efficient and correct identification of intrusion signals. In order to establish a perimeter security system in complex environment, this article proposes an intrusion detection scheme based on DOFS, IoT, and pattern separation technology. The scheme realizes the separation of ambient signals and intrusion signals for the first time. Experiments have proved that this method can separate and recover intrusion signals from mixtures with the average SDR and SI-SDR of 12.35 dB and 11.61 dB, which effectively lays a foundation for subsequent feature extraction and identification. Besides, the response time is only about 0.5 s. This work makes a significant step toward achieving high performance of the perimeter security system in complex environment.

A Fiber Vibration Signal Recognition Method Based on CNN-CBAM-LSTM

By trying to solve the issue of identifying multiple types of intrusion vibration signals collected by distributed vibrating fiber optic sensors, this study investigates the signal identification and feature extraction of intrusion signals, and proposes an optical fiber vibration signal (OFVS) identification method based on deep learning. The external vibration signal is collected by the Sagnac fiber optic interferometer, and then denoised by spectral subtraction. Endpoint detection is carried out by combining the short-time logarithmic energy method and the spectral entropy method. Finally, the equal-length signal containing valid information is intercepted and the corresponding preprocessing is carried out. The method for feature processing incorporates the strong feature learning capability of the long-short-term memory (LSTM) and the great short-term feature extraction capability of the convolutional neural network (CNN). At the same time, to further enhance the signal feature identification, a convolutional block attention module (CBAM) is introduced to perform adaptive feature refinement on the signal. In summary, a network model combining CNN, LSTM, and CBAM is proposed to process the signal features, and finally, the multi-layer perceptron (MLP) is used to complete the task of classification and recognition of multi-type intrusion signals. The experimental findings indicate that the OFVS method of CNN-CBAM-LSTM can effectively identify four kinds of OFVS, and the overall average recognition accuracy reaches 97.9%. Walking and knocking signals among them are recognized with over 99% accuracy.

Event recognition method based on dual-augmentation for a Φ-OTDR system with a few training samples.

Thanks to the development of machine learning and deep learning, data-driven pattern recognition based on neural network is a trend for Φ-OTDR system intrusion event recognition. The data-driven pattern recognition needs a large number of samples for training. However, in some scenarios, intrusion signals are difficult to collect, resulting in the lack of training samples. At the same time, labeling a large number of samples is also a very time-consuming work. This paper presents a few-shot learning classification method based on time series transfer and cycle generative adversarial network (CycleGAN) data augmentation for Φ-OTDR system. By expanding the rare samples based on time series transfer and CycleGAN, the number of samples in the dataset can finally meet the requirement of network training. The experimental result shows that even when the training set has two minor classes with only two samples, the average accuracy of the validation set with 5 classification tasks can still reach 90.84%, and the classification accuracy of minor classes can reach 79.28% with the proposed method.

Electronic Information Signal Recognition Based on a Stochastic Neural Network Algorithm

In order to improve the recognition accuracy of SCN for optical fiber data, a method of optical fiber intrusion signal recognition based on SCN (TSVD-SCN) based on truncated singular value decomposition (TSVD) is proposed in this paper. TSVD-SCN performs SVD decomposition on the hidden layer output of the network and sets a threshold to remove the smaller singular values, so as to reduce the number of conditions of the hidden layer output matrix and improve the network recognition rate. This paper uses the method of duty cycle, average amplitude difference function, and FFT to calculate the energy duty cycle for feature extraction and uses TSVD-SCN algorithm to classify and recognize different intrusion vibration feature vectors. The experimental results show that the root mean square errors of TSVD-SCN and SCN networks are significantly less than RVFL. After the hidden layer node L = 20 , the training error decline speed of RVFL tends to be gentle. When LRVFL = L max , the learning effect is the best, and RMSERVFL = 0.3 . With the continuous increase of L, the training error of SCN network and TSVD-SCN network will be reduced to very small, and the training error of TSVD-SCN network is also less than SCN. Conclusion. The accuracy of the algorithm model proposed in this paper is higher than that of the SCN model. It can accurately identify the types of optical fiber intrusion signals, which is of great significance to improve the classification accuracy of the SCN network in practical applications.

Photoelectric Vibration Monitoring System Using Optical Fiber Sensing/Mathematical Modeling

The optical fiber vibration monitoring system is designed, and the optical fiber with strong anti-interference performance, high stability and corrosion resistance is used as the sensor. Mach-Zehnder with high sensitivity is used for interference counting, and embedded technology is introduced. The extracted spectral characteristics of optical fiber signals are used for pattern recognition through a mathematical modeling method. In the hardware design, embedded technology is introduced. Multi-channel AD collects data, and the processed optical fiber data are communicated with the master computer through the high-speed network port to realize the data transmission without breakpoints, and then reduce the development cost. Fast Fourier transform (FFT) is used as the research method of mathematical modeling in optical fiber signal processing and is improved. The new algorithm can analyze the vibration signal from the frequency domain. In the test, one group of intrusion vibration signals and one group of ordinary vibration signals are taken as the test objects. The results show that the improved mathematical modeling method will not have the situation of false intrusion alarm, nor will it have the situation of common false alarm. Using this algorithm in the vibration monitoring system of optical fiber sensors can accurately identify intrusion signals to distinguish the intrusion vibration situation from the ordinary vibration situation.

Optical fiber intrusion signal unmixing by nonorthogonal principal skewness analysis

The optical fiber intrusion signal detection technology adopts the distributed optical fiber as sensor to monitor and identify perimeter intrusion signals. Due to the diversity of perimeter intrusion, the optical fiber intrusion signals are composed of various types of pure signals. Therefore, direct identification on mixed signals will cause the system performance degradation. A more effective method is to first unmix the mixed signal to obtain each pure signal component, and then perform signal identification. In this paper, nonorthogonal principal skewness analysis (NPSA) based unmixing algorithm is proposed. By introducing supersymmetric tensors, the mixed fiber signal unmixing problem is transformed into the skewness analysis problem, and then the non-orthogonal solution is further solved which significantly improves the accuracy of solving pure signal components. The effectiveness of the proposed algorithm is verified by actual data experiments.

Application of Smart Fiber Optic Sensor Technology in Feature Extraction, Recognition, and Detection

To improve the signal recognition effect of the security system, this paper studies the security system based on intelligent Fiber Optic Sensor (FOS) technology. Firstly, the research background of intelligent FOS is introduced, and its current situation in feature extraction, recognition, and detection is introduced. Secondly, the double Mach–Zehnder (M-Z) Optical Fiber- (OF-) based interferometer model is implemented, and the Wavelet Analysis (WA) theory is introduced to analyze the characteristic threshold and Frequency (F) characteristics of intrusion signal. Finally, the distributed intelligent FOS-based perimeter security system is constructed, and an empirical study is conducted to verify its performance. The results show that the intruder knocking-induced signal F, intruder climbing-induced signal F, noiseless environment-induced signal F, and rainy environment-induced signal F are 0–250 kHz, 0–25 kHz, 0–1.5 kHz, and 0–3.5 kHz, respectively; in all the four cases, excellent results have been obtained after wavelet threshold denoising. Meanwhile, the received signal is decomposed into seven layers through multiscale WA theory. The signal feature classification is based on WA and takes variance as the representation, and the clear classification results are obtained; when the False Alarm Rate (FAR) = 1%, the detection probability of the proposed system is about 99%, while the detection probability of the traditional system is about 3%. The reference arm and sensing arm of the distributed OF-based perimeter security system can be laid in the same optical cable. Therefore, the designed wavelet threshold filtering method is feasible, and the detection probability of the designed WA-based system is higher than that of the traditional security system. The research content provides a reference for the development of intelligent FOS technology in the field of security.

Dual-model hybrid pattern recognition method based on a fiber optic line-based sensor with a large amount of data

A dual-model hybrid pattern recognition based on a fiber optic line-based sensor with a large amount of data is proposed. The vibration signals are converted to gray-level images to reduce the memory requirement. The ResNet18 model for classification is used. To reduce the false positive rate, the over-zero rate and short-time energy are extracted from the intrusion signal, and a support vector machine (SVM) is used. Finally, a discriminator is constructed to determine the types of events by combining the two models trained on the validation dataset. The results demonstrate the excellent average recognition accuracy of this method, which achieves the 97.1% for six events.

Fiber Intrusion Signal Classification Based on Fourier Decomposition and Permutation Entropy Noise Reduction Method

Fiber Intrusion Signal Classification Based on Fourier Decomposition and Permutation Entropy Noise Reduction Method

Fiber Intrusion Signal Classification Based on Gradient Boosting Decision Tree Algorithm

Fiber Intrusion Signal Classification Based on Gradient Boosting Decision Tree Algorithm