Intrusion behavior classification method applied in a perimeter security monitoring system

Abstract

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.

A guardian system for the elderly living alone based on Zigbee wireless ad hoc network

“…Due to the large number of sensor types, the experiment is tested for wind sensors, and the input voltage value is used as the analog signal and the output value is obtained after processing. The output results were compared with the literature [7] method literature [8] method and literature [9] method, and the test results are shown in Figure 4 and Figure 5, respectively Analyzing the above figure, it can be concluded that the sensor acquisition performance in the monitoring system in this paper is good, the error between the output value and the input value of the two sensors is small, and the average value range is from 0.02 V; The other three methods don't work well. Stable, sometimes with large errors.…”

“…Generally, when the CPU usage is higher than 90%, the risk of system crash is greater. Therefore, it can be seen that when the number of concurrent users in the literature [7] reaches 150, the CPU utilization rate has exceeded 90%, while when the number of users in the fuzzy hierarchy method reaches 200, the CPU utilization rate cannot meet the normal operation requirements of the system, and the method in this paper can meet the needs of more users at the same time, and when the number of users reaches 200, the information packet loss rate is 75.4%. This shows that the three-tier system distribution strategy can improve the resource utilization efficiency and provide more space for the system operation.…”

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

“…Therefore, the protection of the perimeter of a computer system is considered one of the most relevant aspects in the field of information security. Perimeter security aims to protect the system perimeter against intruders and is regarded as an organization's first line of defense (Mi et al, 2021 ). The importance of perimeter security is that any breach or vulnerability in this area can lead to unauthorized intrusions, putting data integrity and business continuity at risk (Lyu et al, 2021b ).…”

Strain FBG-Based Sensor for Detecting Fence Intruders Using Machine Learning and Adaptive Thresholding

“…The interferometry-based sensing systems, such as the dual Mach-Zehnder interferometer (DMZI) and Sagnac interferometer, were utilized for event detection in fenced perimeter security systems [7][8][9][10][11]. In Ref.…”

“…In Ref. [11], the proposed intruder pattern recognition algorithm utilized the Sagnac interferometer structure and consisted of pre-processing and pattern recognition using multi-layer perceptron neural networks (MLP-NNs). The power spectrum of the vibration signal was used in the pre-processing step to extract relevant features, and a selected frequency band of 0 Hz to 2000 Hz was used to construct a frequency sequence.…”