Ultra Surf Flow Detection Based on Statistical Classification

2015 ◽  
Vol 713-715 ◽  
pp. 495-499
Author(s):  
Xun Yi Ren ◽  
Han Qing Hu
Keyword(s):  
False Alarm ◽  
False Alarm Rate ◽  
Experimental Results ◽  
Statistical Classification ◽  
Flow Detection

Unbounded browsing software is an application worked in Internet client. It uses a custom encryption protocol to break the traditional network filtering. In this paper, we can detect this application through classifying the Ultra Surf T mode (TCP packets) packets and using SPID. The experimental results show that we can effectively reduce the false alarm rate and detect the application accurately by classifying the Ultra Surf T mode (TCP packets) packets and using SPID.

scholarly journals An Anti-FOD Method Based on CA-CM-CFAR for MMW Radar in Complex Clutter Background

Sensors ◽  
2020 ◽  
Vol 20 (6) ◽  
pp. 1635 ◽  
Author(s):  
Xiaoqi Yang ◽  
Kai Huo ◽  
Jianwei Su ◽  
Xinyu Zhang ◽  
Weidong Jiang
Keyword(s):  
False Alarm ◽  
False Alarm Rate ◽  
Millimeter Wave ◽  
Radar System ◽  
Experimental Results ◽  
Foreign Object ◽  
Constant False Alarm Rate ◽  
Test Statistic ◽  
Homogeneous Background ◽  
Distributed Test

Traditional constant false alarm rate (CFAR) methods have shown their potential for foreign object debris (FOD) indication. However, the performance of these methods would deteriorate under the complex clutter background in airport scenes. This paper presents a threshold-improved approach based on the cell-averaging clutter-map (CA-CM-) CFAR and tests it on a millimeter-wave (MMW) radar system. Clutter cases are first classified with variability indexes (VIs). In homogeneous background, the threshold is calculated by the student-t-distributed test statistic; under the discontinuous clutter conditions, the threshold is modified according to current VI conditions, in order to address the performance decrease caused by extended clutter edges. Experimental results verify that the chosen targets can be indicated by the t-distributed threshold in homogeneous background. Moreover, effective detection of the obscured targets could also be achieved with significant detectability improvement at extended clutter edges.

Color Features Description and Extraction of Flame Image Segmented Region of Early Fire in Large Space

2013 ◽  
Vol 706-708 ◽  
pp. 1862-1865 ◽  
Author(s):  
Ai Min Hu
Keyword(s):  
False Alarm ◽  
False Alarm Rate ◽  
Human Life ◽  
Recognition System ◽  
Experimental Results ◽  
Large Space ◽  
Feature Descriptors ◽  
Color Features ◽  
Flame Region ◽  
Fire Recognition

It is very important to detect the early fire in large space to protect human life and property security. In this paper, color features description and analysis of the segmented flame image are carried out to extract a varity of quantitative feature descriptors of flame region based on the detection results of flame video image. The experimental results show that the color features can improve the effectiveness and robustness of flame recognition system. Furthermore, it can reduce the false alarm rate in the fire recognition system.

scholarly journals Object Scanning of Windows Kernel Driver Based on Pool Tag Quick Scanning

2019 ◽  
Vol 37 (5) ◽  
pp. 1044-1052
Author(s):  
Jiqiang Zhai ◽  
Yajun Xiao ◽  
Hailu Yang ◽  
Jian Wang
Keyword(s):  
False Alarm ◽  
False Alarm Rate ◽  
Experimental Results ◽  
Memory Forensics ◽  
Scanning Step ◽  
Memory Pool ◽  
Detailed Search ◽  
Windows Kernel

In the memory forensics, the Pool Tag Scanning based on the memory pool tag requires a detailed search of the physical memory when scanning the kernel driver object, which is very inefficient. The object scanning of Windows kernel driver by using the pool tag quick scanning is proposed. The method uses the quick pool tag scanning to reduce the memory range of the scan, and then scan the driver object according to the characteristics of the kernel driver object quickly, to help investigator to determine whether the driver is normal. Experimental results shows that the scanning efficiency for object scanning of kernel driver is improved greatly by using the quick pool tag scanning technology and the time spent in the scanning step is reduced while ensuring the false alarm rate is same.

Multiple faults detection and isolation approach applied to a kraft recovery boiler

TAPPI Journal ◽  
2014 ◽  
Vol 13 (1) ◽  
pp. 33-41
Author(s):  
YVON THARRAULT ◽  
MOULOUD AMAZOUZ
Keyword(s):  
Fault Detection ◽  
Early Detection ◽  
False Alarm ◽  
False Alarm Rate ◽  
Principal Component ◽  
Fault Isolation ◽  
Fault Detection And Isolation ◽  
Detection Scheme ◽  
Multiple Faults ◽  
Recovery Boiler

Recovery boilers play a key role in chemical pulp mills. Early detection of defects, such as water leaks, in a recovery boiler is critical to the prevention of explosions, which can occur when water reaches the molten smelt bed of the boiler. Early detection is difficult to achieve because of the complexity and the multitude of recovery boiler operating parameters. Multiple faults can occur in multiple components of the boiler simultaneously, and an efficient and robust fault isolation method is needed. In this paper, we present a new fault detection and isolation scheme for multiple faults. The proposed approach is based on principal component analysis (PCA), a popular fault detection technique. For fault detection, the Mahalanobis distance with an exponentially weighted moving average filter to reduce the false alarm rate is used. This filter is used to adapt the sensitivity of the fault detection scheme versus false alarm rate. For fault isolation, the reconstruction-based contribution is used. To avoid a combinatorial excess of faulty scenarios related to multiple faults, an iterative approach is used. This new method was validated using real data from a pulp and paper mill in Canada. The results demonstrate that the proposed method can effectively detect sensor faults and water leakage.

Fuzzy ART Neural Network Model for Automated Detection of Freeway Incidents

1998 ◽  
Vol 1634 (1) ◽  
pp. 56-63 ◽  
Author(s):  
Sherif S. Ishak ◽  
Haitham M. Al-Deek
Keyword(s):  
Neural Networks ◽  
False Alarm ◽  
False Alarm Rate ◽  
Back Propagation ◽  
Incident Detection ◽  
Traffic Patterns ◽  
Traffic Pattern ◽  
Detection Algorithms ◽  
Fuzzy Art ◽  
Freeway Incidents

Pattern recognition techniques such as artificial neural networks continue to offer potential solutions to many of the existing problems associated with freeway incident-detection algorithms. This study focuses on the application of Fuzzy ART neural networks to incident detection on freeways. Unlike back-propagation models, Fuzzy ART is capable of fast, stable learning of recognition categories. It is an incremental approach that has the potential for on-line implementation. Fuzzy ART is trained with traffic patterns that are represented by 30-s loop-detector data of occupancy, speed, or a combination of both. Traffic patterns observed at the incident time and location are mapped to a group of categories. Each incident category maps incidents with similar traffic pattern characteristics, which are affected by the type and severity of the incident and the prevailing traffic conditions. Detection rate and false alarm rate are used to measure the performance of the Fuzzy ART algorithm. To reduce the false alarm rate that results from occasional misclassification of traffic patterns, a persistence time period of 3 min was arbitrarily selected. The algorithm performance improves when the temporal size of traffic patterns increases from one to two 30-s periods for all traffic parameters. An interesting finding is that the speed patterns produced better results than did the occupancy patterns. However, when combined, occupancy–speed patterns produced the best results. When compared with California algorithms 7 and 8, the Fuzzy ART model produced better performance.

A Constant False Alarm Rate-(CFAR) Based Change Detection Approach to Helicopter Diagnostics

2008 ◽  
Author(s):  
Kenneth Ranney ◽  
Hiralal Khatri ◽  
Jerry Silvious ◽  
Kwok Tom ◽  
Romeo del Rosario
Keyword(s):  
Change Detection ◽  
False Alarm ◽  
False Alarm Rate ◽  
Constant False Alarm Rate ◽  
Detection Approach

scholarly journals Realizing Target Detection in SAR Images Based on Multiscale Superpixel Fusion

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1643
Author(s):  
Ming Liu ◽  
Shichao Chen ◽  
Fugang Lu ◽  
Mengdao Xing ◽  
Jingbiao Wei
Keyword(s):  
Synthetic Aperture Radar ◽  
False Alarm ◽  
False Alarm Rate ◽  
Target Detection ◽  
Synthetic Aperture ◽  
False Alarms ◽  
Sar Image ◽  
Constant False Alarm Rate ◽  
Sar Images ◽  
Complex Scenes

For target detection in complex scenes of synthetic aperture radar (SAR) images, the false alarms in the land areas are hard to eliminate, especially for the ones near the coastline. Focusing on the problem, an algorithm based on the fusion of multiscale superpixel segmentations is proposed in this paper. Firstly, the SAR images are partitioned by using different scales of superpixel segmentation. For the superpixels in each scale, the land-sea segmentation is achieved by judging their statistical properties. Then, the land-sea segmentation results obtained in each scale are combined with the result of the constant false alarm rate (CFAR) detector to eliminate the false alarms located on the land areas of the SAR image. In the end, to enhance the robustness of the proposed algorithm, the detection results obtained in different scales are fused together to realize the final target detection. Experimental results on real SAR images have verified the effectiveness of the proposed algorithm.

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