Perimeter Security Research Articles

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.

Trustworthiness Evaluation for Permissioned Blockchain-Enabled Applications

As permissioned blockchain becomes a common foundation of blockchain-based circumstances for current organizations, related stakeholders need a means to assess the trustworthiness of the applications involved within. It is extremely important to consider the potential impact brought by the Blockchain technology in terms of security and privacy. Therefore, this study proposes a rigorous security risk management framework for permissioned blockchain-enabled applications. The framework divides itself into different implementation domains, i.e., organization security, application security, consensus mechanism security, node management and network security, host security and perimeter security, and simultaneously provides guidelines to control the security risks of permissioned blockchain applications with respect to these security domains. In addition, a case study, including a security testing and risk evaluation on each stack of a specific organization, is demonstrated as an implementation instruction of our proposed risk management framework. According to the best of our knowledge, this study is one of the pioneer researches that provide a means to evaluate the security risks of permissioned blockchain applications from a holistic point of view. If users can trust the applications that adopted this framework, this study can contribute to the adoption of permissioned blockchain-enabled technologies. Furthermore, application providers can use the framework to perform gap analysis on their existing systems and controls and understand the risks of their applications.

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.

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

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

Mixed Intrusion Events Recognition Based on Group Convolutional Neural Networks in DAS System

The mixed intrusion events recognition is still a challenging problem in optical fiber perimeter security with distributed acoustic sensing (DAS), because the vibration signals will be mixed when multiple events occur at the same time and in close proximity. In order to identify mixed events consisting of two single events, we propose a recognition scheme based on deep group neural networks algorithm. The 100 groups convolutional neural networks (100G-Net) model is designed to make the best of vibration information of samples for feature extraction and classification. The experiments show that not only average training recognition accuracy of the proposed algorithm can reach 99.6%, but also the generalization ability of the proposed algorithm is better than typical CNN models.

Использование беспилотных летательных аппаратов при доставке запрещенных предметов на территорию исправительных учреждений и возможные способы борьбы с ними

Prohibited items and substances have been smuggled into correctional institutions since their inception and will be smuggled as long as these institutions continue to function. Technological progress sets ever new challenges for restricted access facilities, including the institutions of the RF Federal Penitentiary Service. In recent years, penitentiary institutions have recorded dozens of attempts to deliver prohibited items (drugs, mobile devices, alcohol, etc.) by unmanned aerial vehicles to a closed area. Currently, the Federal Penitentiary Service of Russia is developing and implementing equipment that will allow detecting and blocking drones, quadrocopters and other unmanned aerial vehicles near correctional facilities and pre-trial detention centers. Protection against the quadcopter's unwanted penetration into the restricted access area is to take control over it and land it in a designated place. When an unmanned aerial vehicle (UAV) approaches the territory of the correctional facility, the detection system is triggered, it determines the UAV's unique identification number and then proceeds to seize control over it. At any time of the day and regardless of weather and other conditions, excluding the influence of the human factor, the protection system is able to independently detect the UAV and land it in a designated place. The system is able to intercept it not only when it is trying to cross the perimeter, but also when it is approaching a restricted access facility at a distance of several hundred meters. The fight against small-sized quadrocopters has long been massive; there are dozens of ready-made solutions for protecting any objects, including correctional facilities. One of these solutions may be a perimeter security radar complex: the control of airspace over a facility and its adjacent territory with the help of radar stations is a traditional way of detecting air targets.

DeepWare: Imaging Performance Counters with Deep Learning to Detect Ransomware

In the year passed, rarely a month passes without a ransomware incident published in a newspaper or social media. In addition to the rise in the frequency of ransomware attacks, emerging attacks are very effective as they utilize sophisticated techniques to bypass existing organizational security perimeter. Toward this issue, this paper presents "DeepWare," a simple yet effective concept of imaging hardware performance counters with deep learning to detect ransomware, to identify ransomware efficiently and effectively. By imaging the HPC values and restructuring the conventional CNN model, DeepWare can address HPCs nondeterminism issue by extracting the event-specific and event-wise behavioral features, which allows it to distinguish the ransomware activity from the benign one effectively. The experiment results across ransomware families show that the proposed DeepWare is effective at detecting different classes of ransomware with the highest detection accuracy, which is 30% and 76% improvement over the two baseline models. It achieves an astounding recall score and nearly zero false-positive rates using just a 100 ms snapshot of HPC data. This timeliness of DeepWare is critical on the ground that organizations and individuals have the opportunity to take countermeasures in the first stage of the attack.

Disturbance location and pattern recognition of a distributed optical fiber sensor based on dual-Michelson interferometers.

A distributed fiber optic sensor based on dual-Michelson interferometers for disturbance localization and pattern recognition is proposed. The system obtains the phase difference of each of the two interferometers using a passive demodulating algorithm based on a 3×3 coupler. Two correlation signals with disturbance position information are obtained by delaying and subtracting the phase difference signals through which the disturbance location can be obtained. This method has the same frequency response over the whole sensing path, and there is no localization blind spot. The pattern recognition method of this sensing system is to obtain the spectral signal by fast Fourier transform of the demodulated interferometer phase information and input it as a feature vector into a one-dimensional convolutional neural network to verify the correct rate of pattern recognition for four behaviors: stepping, shearing, sweeping, and shaking. The total transmission distance of the system can reach 100 km; the location errors are within ±35m, and the correct rate of four pattern recognitions is higher than 97%. The sensing system has good polarization stability, which can ensure the stability of long-term operation and has a broad application prospect in long-distance perimeter security.

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.

Autonomous Vehicle: Security by Design

Security of (semi)-autonomous vehicles is a growing concern, first, due to the increased exposure of the functionality to potential attackers; second, due to the reliance of functionalities on diverse (semi)-autonomous systems; third, due to the interaction of a single-vehicle with myriads of other smart systems in urban traffic infrastructure. Beyond these technical issues, we argue that the security-by-design principle for smart and complex autonomous systems, such as an Autonomous Vehicle (AV) is poorly understood and rarely practiced. Unlike traditional IT systems, where the risk mitigation techniques and adversarial models are well studied and developed with security design principles such as security perimeter and defense-in-depth, the lack of such a framework for connected autonomous systems is plaguing the design and implementation of a secure AV. We attempt to identify the core issues of securing an AV. This is done methodically by developing a security-by-design framework for AV from the first principles. Subsequently, the technical challenges for AV security are identified.

An Asymmetrical Dual Sagnac Distributed Fiber Sensor for High Precision Localization Based on Time Delay Estimation

In this paper, the Sagnac interferometer with a one-way optical fiber ring and a novel dual Sagnac distributed fiber vibration sensor with an asymmetrical structure for disturbance localization is proposed. The two Sagnac interferometers in this system are asymmetrical and equal in the lengths of optical paths. Using time delay estimation based on cross-correlation, the disturbance localization can be calculated from the two demodulated phase signals. The localization precision is ±10 m with a 50 km sensing fiber and the overall localization error is 0.02%. The time error problem possibly existing in the fiber sensing systems based on time delay estimation is firstly studied. Due to the design of equal optical path lengths, the time error problem does not exist in this system, which is a possible reason for high localization precision. The system has application potential in long-distance perimeter security and many other fields.

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.

“Disoriented Superpower”: U.S. Foreign Policy in the Post-Bipolar World

This article seeks to fi ll the gap between well theorized upon constants of the U.S. strategic thinking and the divergent approaches of various administrations to practical actions on the international arena. Grounded in the concept developed by Christopher Hammer, the analysis presents the U.S. foreign policy as the product of a never-ending internal debate about the most optimal ways of promoting values, dealing with international institutions and prioritizing threats within a security perimeter. Taking stock of the choices made by consecutive presidents in each of the three areas the authors study the roots of foreign policy inertia of the sole superpower that resulted in unforced mistakes undermining its own political cohesion as well as hindering the emergence of lasting international architecture to replace the cold-war order. Thus, the article argues that the key feature of the last thirty years – the absence of an enemy as an organizing principle for the U.S. foreign policy – prompted the American public to pay less attention to global events which became the area of opportunistic decisions. Overall, the authors conclude that the mistakes were made precisely when the domestic debates in the U.S. about international relations were absent and note that the current divisions in the American society as an impetus for self-scrutiny and the emergence of China as a focal point for the American elites may result in a more coherent foreign policy and make the U.S. a more responsible international actor.

Pulse Coding in Distributed Optical Fiber Vibration Sensor: A Review

Distributed optical fiber vibration sensor (DOFVS) is suitable for remote monitoring of vibration signals generated by external perturbations. In order to improve the sensing performance, various pulse coding techniques are applied in DOFVS, which have attracted an increasing amount of research attention. In this paper, recent advances of pulse coding methods used in DOFVS are reviewed, from pulse compression, including linear frequency modulation (LFM) and nonlinear frequency modulation (NLFM), pulse coding modulation based on the linear combination and correlation operation, such as Simplex code, Golay code and random code, as well as pulse sequence modulation (PSM), such as phase-shift pulses, differential frequency pulses, etc. Characteristics, working principles and the latest research results of pulse coding methods are discussed. Furthermore, some application progresses of pulse coding for performance enhancement in DOFVS are also introduced. By combining these technologies, the sensing performance could be improved in aspects of sensing distance, spatial resolution (SR), and frequency response, which show considerable potential applications in perimeter security, oil and gas monitoring, pipeline leakage, and structural health monitoring.

Using CME and FCME Algorithms for the Task of Detecting Human Steps

Currently, global trends in the development of perimeter security technology lead to the general miniaturization of devices and systems and the increase of their autonomy. The primary trend in developing these systems is the maximum processing and classification of signals by built-in tools. The article presents the study results on the possibility of using blind algorithms to calculate the level of operation of the threshold detector CME and FCME in detecting human steps in seismic perimeter security detectors using low-power computing modules. The algorithms were tested on actual data recorded during the experiments. As a result of applying algorithms for the seismic signal envelope, the human steps of the probability of false positives for the CME and FCME algorithms were 23% and 10%, respectively. Neutralizing the signal trend allowed obtaining values of false alarms of 16% for the CME algorithm and 7% for the FCME algorithm, and normalization of the signal amplitude within one analysis interval allowed obtaining the probability of false alarms at 0% for both algorithms. The obtained results give complete information when choosing the type of algorithm depending on the input data. In detecting seismic data by an autonomous sensor, it is most appropriate to use the SME algorithm with pre-normalization of the signal amplitude due to less computational complexity.

Principle and Application State of Fully Distributed Fiber Optic Vibration Detection Technology Based on Φ-OTDR: A Review

Phase-sensitive optical time-domain reflectometry Φ-OTDR) is a powerful sensing device that enables fully distributed optical fiber vibration detection with fast response and high sensitivity. Based on the introduction of its principles, Rayleigh backscatter, sensing mechanism, the characteristics of the Φ-OTDR system are analyzed in detail. Some gaps in the Φ-OTDR technology are outlined, and its development trends are highlighted. The application of its current technology is illustrated in different fields such as the perimeter security monitoring system, submarine cable surveillance, and so on. In the experiments analysis, it was pointed out that the device is very sensitive, which is subject to a high rate of false alarms; it also has unresolved types of disturbances. For these purposes, this paper first summarizes the development status of the Φ-OTDR-based fully distributed optical fiber sensing device. Then, it analyzes and proposes the use of a Printed Circuit Board (PCB) winding flyback converter with shielding, to solve the problem of sensitivity. The shielding method has been proven to significantly reduce high frequency noise up to 30 MHz. This will enable relevant industries and future researchers to know the current state of the Φ-OTDR device.

Analysis results of experimental operation of the protection complex of long perimeters VM 8018

In recent years, technical means of perimeter security are becoming more widespread, which is due to their increasing efficiency of protection from unauthorized access by intruders to protected facilities. The high competition of manufacturers of such means has led to the emergence of a variety of technical solutions for detection means (hereinafter referred to as DT) operating on different physical principles and designed to solve specific problems. The main purpose of the DT is to ensure the detection of the facts of intruders' penetration through the guarded obstacle. Considering that the methods of intruders' penetration are varied (destruction of fences, climbing over the fence, digging under the fence, etc.), the creation of an effective security system using only one type of DT is a problematic task. At the same time, the autonomous use of many types of DT leads to the fact that there will be a large number of technical means in the duty room, which increase not only the time and financial costs for their maintenance, but also increase the requirements for the competence of the duty personnel. Research Institute of Electronic Computers has developed the complex VM 8018, designed to automate the process of protecting extended perimeters of objects for various purposes, including sections of the state border, perimeters and premises of outposts, extendedperimeters of industrial and military facilities, as well as controlling executive devices (electric drives for gates and gates, lighting etc.), video surveillance.

Cyberphysical Security Through Resiliency: A Systems-Centric Approach

Cyber-physical systems (CPS) are often defended in the same manner as information technology (IT) systems -- by using perimeter security. Multiple factors make such defenses insufficient for CPS. Resiliency shows potential in overcoming these shortfalls. Techniques for achieving resilience exist; however, methods and theory for evaluating resilience in CPS are lacking. We argue that such methods and theory should assist stakeholders in deciding where and how to apply design patterns for resilience. Such a problem potentially involves tradeoffs between different objectives and criteria, and such decisions need to be driven by traceable, defensible, repeatable engineering evidence. Multi-criteria resiliency problems require a system-oriented approach that evaluates systems in the presence of threats as well as potential design solutions once vulnerabilities have been identified. We present a systems-oriented view of cyber-physical security, termed Mission Aware, that is based on a holistic understanding of mission goals, system dynamics, and risk.