Perimeter Security System Research Articles

Identification of Multiple Events Based on Two-Dimensional Images and Isotropic Network in Optical Fiber Perimeter Security System

Identification of Multiple Events Based on Two-Dimensional Images and Isotropic Network in Optical Fiber Perimeter Security System

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.

Intelligent intrusion detection for optical fiber perimeter security system based on an improved high efficiency feature extraction technique

The automated analysis of optical fiber vibration sensing data has been highly demanded in engineering applications. Therefore, intrusion analysis, which aims at detecting, recognizing, and classifying intrusions, holds great importance for optical fiber vibration sensing. In this work, an intelligent intrusion detection scheme employing an improved high-efficiency feature extraction technique and utilizing a dual Mach–Zehnder interferometer (DMZI)-based optical fiber perimeter security system is proposed. So, the DMZI-based perimeter security system in practical settings can be successfully established. Specifically, time-frequency feature vectors with nine features are firstly constructed using a maximal overlap discrete wavelet transformation approach and a zero crossing rate method. Then, the feature vectors are classified into corresponding categories using a radial basis function neural network. The effectiveness of the proposed scheme has been validated using six types of human intrusions, such as knocking, climbing, waggling, cutting, crashing and kicking the fence. The results show that the given intrusions can be accurately and rapidly recognized by the proposed scheme. The average recognition rate of 95.0% is achieved, and the average processing time for each sample data is only 0.033 s, which is significantly lower than the sampling interval (0.3 s) in our experiment. It is believed that the proposed scheme holds promising potential in the field of optical fiber perimeter security systems.

Authentication, access, and monitoring system for critical areas with the use of artificial intelligence integrated into perimeter security in a data center.

Perimeter security in data centers helps protect systems and the data they store by preventing unauthorized access and protecting critical resources from potential threats. According to the report of the information security company SonicWall, in 2021, there was a 66% increase in the number of ransomware attacks. In addition, the message from the same company indicates that the total number of cyber threats detected in 2021 increased by 24% compared to 2019. Among these attacks, the infrastructure of data centers was compromised; for this reason, organizations include elements Physical such as security cameras, movement detection systems, authentication systems, etc., as an additional measure that contributes to perimeter security. This work proposes using artificial intelligence in the perimeter security of data centers. It allows the automation and optimization of security processes, which translates into greater efficiency and reliability in the operations that prevent intrusions through authentication, permit verification, and monitoring critical areas. It is crucial to ensure that AI-based perimeter security systems are designed to protect and respect user privacy. In addition, it is essential to regularly monitor the effectiveness and integrity of these systems to ensure that they function correctly and meet security standards.

Airport Perimeter Security System Readiness Analysis (Case Study At Budiarto Curug-Tangerang Airport)

This study aims to determine how the perimeter conditions and solutions to the obstacles that exist in the Budiarto Airport Management Unit (UPBU). The study used a descriptive method and data collection used was the method of interviewing, observing and documenting. The data analysis technique used is data reduction, data display, and conclusing drawing. This study used several respondents as a sample, one airport security officer and four village/sub-district officials in four villages surrounding the Budiarto Airport Management Unit (UPBU). This research shows that the perimeter security system at Budiarto Airport is still not ready and tight, because avsec (aviation security) is only located or standing guard at the gate only, the results of interviews with the surrounding village chief also showed that there are still many residents who are still approaching the airport security area because they still do not understand the meaning of warning signs on the perimeter.

Intrusion Monitoring Based on High Dimensional Random Matrix by Using Ultra-Weak Fiber Bragg Grating Array

In order to ensure that a perimeter security system can work effectively, a convenient and effective event detection algorithm has an important engineering significance. Given the above background, in this paper, we propose a high reliability intrusion event recognition method and vibration sensing system, based on ultra-weak fiber Bragg grating array, by using high dimensional random matrix. We obtain a high sensitivity optical interference signal by constructing a patch-matched optical interference system, then compose the demodulated interference signal into a high-dimensional random matrix. The statistical characteristics of the matrix for the Marcenko-Pastur (M-P) law and ring law are used to confirm the presence of intrusion events efficiently, which can reflect the limit spectrum distribution of the high-dimensional random matrix; meanwhile, the abnormal state quantity and moment are obtained. Further, the average spectral radius value is used to judge the fault cause. Field experimental results show that the proposed method can effectively obtain the correct monitoring data for the sensor array. By comparing the monitoring results of normal operation and crusher operation, we can detect the intrusion event in 4.5 s, and the accuracy rate can reach more than 90%, which verifies that the proposed high-dimensional random matrix analysis method can work properly, proving a practical engineering application prospect.

Distributed Fiber-optic Perimeter Security System of Various Objects

The issues of development of a distributed fiber-optic security system for restricted access objects and other objects of state significance from unauthorized access are considered. The analysis of existing systems developed by foreign scientists is given. A scheme of a passive perimeter security system is proposed for consideration, in which an optical fiber is used as the main element. The principle of operation of a distributed type fiber-optic security system is based on a method for controlling the amount of additional losses during mechanical action on an optical fiber. A laboratory stand was constructed for conducting field experiments and testing the design of the security system. The obtained research results make it possible to further create fiber-optic security systems of a distributed type with a lower cost compared to foreign analogues due to a fundamentally new method of data processing and the use of multimode optical fibers. This will also increase the length of the fiber-optic security system and increase their reliability. The proposed distributed fiber-optic security system uses a completely different principle associated with pixel analysis of the change in the light spot.

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.

Fiber-optic security alarm system of distributed type

This article discusses the development of a distributed fiber-optic security system for restricted access objects and other objects of state significance against unauthorized access. The analysis of existing systems developed by foreign scientists is given. It is proposed to consider the scheme of a passive perimeter security system, which uses optical fiber as the main element. The principle of operation of a distributed fiber-optic security system is based on the method of controlling the amount of additional losses during mechanical action on the optical fiber for conducting full-scale experiments to test the design of the security system. The results of the study suggest the prospect of reducing the cost of the fiber-optic security system due to new hardware and software methods for processing data received from sensors, this will also increase the length of the fiber-optic security system and increase their reliability indicators.

Image Edge Detection Methods in Perimeter Security Systems Using Distributed Fiber Optical Sensing.

This paper aims to evaluate detection algorithms for perimeter security systems based on phase-sensitive optical time-domain reflectometry (-OTDR). Our own designed and developed sensor system was used for the measurement. The main application of the system is in the area the perimeter fencing intrusion detection. The system is unique thanks to the developed motherboard, which contains a field-programmable gate array (FPGA) that takes care of signal processing. This allows the entire system to be integrated into a 1U rack chassis. A polygon containing two different fence types and also cable laid underground in a plastic tube was used for testing. Edge detection algorithms using the Sobel and Prewitt operators are considered for post-processing. The comparison is made based on the signal-to-noise ratio (SNR) values calculated for each event. Results of algorithms based on edge detection methods are compared with the conventional differential method commonly used in -OTDR systems.

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.

Perimeter Security System Based on Ultra-Weak Fiber Bragg Grating Array

Perimeter Security System Based on Ultra-Weak Fiber Bragg Grating Array

Online Monitoring for Non-Stationary Operation via a Collaborative Neural Network

Online monitoring has become a challenging issue because of the changing knowledge in non-stationary operation. It requires an appropriate monitoring algorithm to timely self-adjust based on a finite number of online data. This study proposes a collaborative neural network (CoNN) for detecting physical intrusions in perimeter security monitoring through real-time adaptation. The proposed CoNN method consists of two half-shared neural networks for online learning with finite measurements through preserving previous knowledge and absorbing the new knowledge. Specifically, the shared feature layers can learn latent domain-invariant representations through weight sharing and adapt CoNN to prior knowledge changes. On this basis, the classification layers use concept-exclusive parameters to model the changes in posterior knowledge. Furthermore, the objective of CoNN is formulated as a linearly constrained non-convex minimization problem with coupled functions, and an alternating direction method is used to solve it with theoretically guaranteed convergence. The feasibility and effectiveness of the proposed method are validated through a real-field experiment of a perimeter security system.

Optimizing Perimeter Surveillance Drones to enhance the security system of unmanned aerial vehicles

This work is to optimize perimeter surveillance and explore the distribution of ground bases for unmanned aerial vehicles along the Jordanian border and optimize the set of technologies for each aerial vehicle. This model is part of ongoing research on perimeter security systems based on unmanned aerial vehicles. The suggested models give an initial insight about selecting technologies carried by unmanned aerial vehicles based on their priority; it runs for a small scale system that can be expanded, the initial results show the need for at least four ground bases along the length of the border, and a selected set of various technologies for each vehicle.

Secured Perimeter with Electromagnetic Detection and Tracking with Drone Embedded and Static Cameras

Perimeter detection systems detect intruders penetrating protected areas, but modern solutions require the combination of smart detectors, information networks and controlling software to reduce false alarms and extend detection range. The current solutions available to secure a perimeter (infrared and motion sensors, fiber optics, cameras, radar, among others) have several problems, such as sensitivity to weather conditions or the high failure alarm rate that forces the need for human supervision. The system exposed in this paper overcomes these problems by combining a perimeter security system based on CEMF (control of electromagnetic fields) sensing technology, a set of video cameras that remain powered off except when an event has been detected. An autonomous drone is also informed where the event has been initially detected. Then, it flies through computer vision to follow the intruder for as long as they remain within the perimeter. This paper covers a detailed view of how all three components cooperate in harmony to protect a perimeter effectively, without having to worry about false alarms, blinding due to weather conditions, clearance areas, or privacy issues. The system also provides extra information of where the intruder is or has been, at all times, no matter whether they have become mixed up with more people or not during the attack.

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%.

High-performance railway perimeter security system based on the inline time-division multiplexing fiber Fabry-Perot interferometric sensor array

This paper proposes a high-performance railway perimeter security system based on the inline time-division multiplexing fiber Fabry-Perot interferometric (TDM-FFPI) sensor array. A rectangular-pulse binary phase modulation (RPBPM) method is used for phase demodulation and a field programmable gate array (FPGA)-based acquisition system is adopted for receiving and processing the signals with the advantages of low complexity and high real-time capability in this system. Besides, a three-polarizer polarization diversity receiver (tr-PDR) is used to overcome the polarization-induced fading elimination. For the demodulated phase signals, a real-time and accurate two-level pattern recognition method based on the support vector machines (SVM) is employed for the recognition of different events (non-intrusion events including environment, rain and wind, and four intrusion events including shaking, cutting, climbing and knocking). The feasibility of the present system is verified experimentally. The average recognition accuracy is up to about 94.5% with the integrating time less than 0.2 s, which satisfies the high accuracy and high real-time requirements.

Life cycle assessment of repurposed waste electric and electronic equipment in comparison with original equipment

Reuse is considered as a priority alternative for the management of Waste from Electrical and Electronic Equipment (WEEE). The reason is that it is thought that reuse always has a lower environmental impact. However, few studies have evaluated in detail the environmental impacts of reuse, and even fewer have analysed cases of reuse for a purpose other than the original one. In this study, life cycle assessment (LCA) following the ISO 14040 standard, was employed to assess the environmental impacts of two preparing for reuse processes of desktop computer considered as WEEE, whose results were repurposed products with industrial application: a programmable logic controller (PLC) and a perimeter security system (PSA). These scenarios were compared with other equivalents in which the products come from virgin raw materials. The results showed a worse environmental performance of repurposed PLC than one original, due mainly to differences in distribution and use stages. The greater weight, the shorter lifespan and mainly the higher operating power were responsible for the greater negative effects of the reuse scenario. However, repurposed PSA has a lower negative impact than original equipment in all environmental categories analysed. This was mainly due to its lower operating power and also not having demanding technical characteristics. Therefore, in this case, the usage profile, the composition and the lifespan can be considered as the main factors that determine the environmental advantage of repurposed products. The main conclusion of this work is that the environmental viability of the reuse of WEEE depends on the existing commercial alternatives for the application of the new product obtained; being one of the main factors the power consumption and the lifespan. This constitutes an important aspect to take into account when developing regulations, strategies and policies to prevent the implementation of WEEE management systems with environmental impacts greater than other alternatives. Further, the specific information about environmental performance of repurposing can contribute to the development of new processes of preparing for reuse. In this way, the commercialization of new products from these processes is favoured, which contributes to improving the environmental management of WEEE and the development of the circular economy.

MBSE Testbed for Rapid, Cost-Effective Prototyping and Evaluation of System Modeling Approaches

Model-based systems engineering (MBSE) has made significant strides in the last decade and is now beginning to increase coverage of the system life cycle and in the process generating many more digital artifacts. The MBSE community today recognizes the need for a flexible framework to efficiently organize, access, and manage MBSE artifacts; create and use digital twins for verification and validation; facilitate comparative evaluation of system models and algorithms; and assess system performance. This paper presents progress to date in developing a MBSE experimentation testbed that addresses these requirements. The current testbed comprises several components, including a scenario builder, a smart dashboard, a repository of system models and scenarios, connectors, optimization and learning algorithms, and simulation engines, all connected to a private cloud. The testbed has been successfully employed in developing an aircraft perimeter security system and an adaptive planning and decision-making system for autonomous vehicles. The testbed supports experimentation with simulated and physical sensors and with digital twins for verifying system behavior. A simulation-driven smart dashboard is used to visualize and conduct comparative evaluation of autonomous and human-in-the-loop control concepts and architectures. Key findings and lessons learned are presented along with a discussion of future directions.

Intrusion behavior classification method applied in a perimeter security monitoring system.

A distributed optic fiber perimeter security system is proved to be an effective strategy for the security monitoring of some vital targets, such as power plants, power substations and telecommunication base stations. However, this method can hardly distinguish different categories of the intrusion behavior and is easily mis-triggered by different kinds of environmental interference. To distinguish different intrusion patterns and different interference events effectively, a vibration pattern recognition algorithm is proposed and demonstrated based on the merged Sagnac interferometer structure. The method consists of two parts: the pre-processing algorithm and the multi-layer perceptron neural networks (MLP-NNs). The pre-processing algorithm is applied to retrieve and extract the vibration signal from the captured source signal, and the MLP-NN is used to realize pattern recognition from each type of input. Typically, a high-dimensional vector group which contains hundreds of orders of vibration signal's power frequency is obtained to cover as many signalized features as possible. Moreover, results of the experiment deployed on a 10 kilometer long perimeter fence in the transformer substation show that the proposed classification-based model achieves 97.6% classification accuracy in the test. Through multiple comparison tests, the proposed model gives a solid performance in the subsequent integrated evaluation to classify each intrusion pattern.