scholarly journals Research on Constant False Alarm Detection Method of Entangled State Quantum Detection System

2021 ◽  
Vol 0 (0) ◽  
pp. 0-0
Author(s):  
Keyword(s):  
False Alarm ◽  
Detection Method ◽  
Detection System ◽  
Entangled State ◽  
Quantum Detection

scholarly journals Research on method of detecting constant false alarm of entangled state quantum detection system

2022 ◽  
Vol 71 (1) ◽  
pp. 010303-010303
Author(s):  
Wei Rong-Yu ◽  
◽  
Li Jun ◽  
Zhang Da-Ming ◽  
Wang Wei-Hao
Keyword(s):  
False Alarm ◽  
Detection System ◽  
Entangled State ◽  
Quantum Detection

scholarly journals Research on hybrid intrusion detection method based on the ADASYN and ID3 algorithms

2021 ◽  
Vol 19 (2) ◽  
pp. 2030-2042
Author(s):  
Yue Li ◽  
◽  
Wusheng Xu ◽  
Wei Li ◽  
Ang Li ◽  
...  
Keyword(s):  
Intrusion Detection ◽  
Decision Tree ◽  
False Alarm ◽  
False Alarm Rate ◽  
Detection Method ◽  
Detection System ◽  
Performance Comparison ◽  
Tree Model ◽  
Network Intrusion ◽  
Id3 Algorithm

<abstract> <p>Intrusion detection system plays an important role in network security. Early detection of the potential attacks can prevent the further network intrusion from adversaries. To improve the effectiveness of the intrusion detection rate, this paper proposes a hybrid intrusion detection method that utilizes ADASYN (Adaptive Synthetic) and the decision tree based on ID3 algorithm. At first, the intrusion detection dataset is transformed by coding technology and normalized. Subsequently, the ADASYN algorithm is applied to implement oversampling on the training set, and the ID3 algorithm is employed to build a decision tree model. In addition, the model proposed by the research is evaluated by accuracy, precision, recall, and false alarm rate. Besides, a performance comparison is conducted with other models. Consequently, it is found that the combined model based on ADASYN and ID3 decision tree proposed in this research possesses higher accuracy as well as lower false alarm rate, which is more suitable for intrusion detection tasks.</p> </abstract>

Proposed Hybrid Classifier to Improve Network Intrusion Detection System using Data Mining Techniques

2020 ◽  
Vol 38 (1B) ◽  
pp. 6-14
Author(s):  
ٍٍSarah M. Shareef ◽  
Soukaena H. Hashim
Keyword(s):  
Intrusion Detection ◽  
False Alarm ◽  
Intrusion Detection System ◽  
Detection System ◽  
Multinomial Logistic Regression ◽  
Network Intrusion Detection ◽  
Limiting Factor ◽  
Network Intrusion ◽  
Network Intrusion Detection System ◽  
Normal Behavior

Network intrusion detection system (NIDS) is a software system which plays an important role to protect network system and can be used to monitor network activities to detect different kinds of attacks from normal behavior in network traffics. A false alarm is one of the most identified problems in relation to the intrusion detection system which can be a limiting factor for the performance and accuracy of the intrusion detection system. The proposed system involves mining techniques at two sequential levels, which are: at the first level Naïve Bayes algorithm is used to detect abnormal activity from normal behavior. The second level is the multinomial logistic regression algorithm of which is used to classify abnormal activity into main four attack types in addition to a normal class. To evaluate the proposed system, the KDDCUP99 dataset of the intrusion detection system was used and K-fold cross-validation was performed. The experimental results show that the performance of the proposed system is improved with less false alarm rate.

Image detection and parameterization for different components in cross-sections of radial tires

2021 ◽  
pp. 095440702110236
Author(s):  
Chen Liu ◽  
Yude Dong ◽  
Yanli Wei ◽  
Jiangtao Wang ◽  
Hongling Li
Keyword(s):  
Image Processing ◽  
Digital Image Processing ◽  
Cross Sections ◽  
Digital Image ◽  
Detection Method ◽  
Detection System ◽  
Practical Significance ◽  
Vehicle Safety ◽  
Wire Steel ◽  
Good Detection

The internal structure analysis of radial tires is of great significance to improve vehicle safety and during tire research. In order to perform the digital analysis and detection of the internal composition in radial tire cross-sections, a detection method based on digital image processing was proposed. The research was carried out as follows: (a) the distribution detection and parametric analysis of the bead wire, steel belt, and carcass in the tire section were performed by means of digital image processing, connected domain extraction, and Hough transform; (b) using the angle of location distribution and area relationship, the detection data were optimized through coordinate and quantity relationship constraints; (c) a detection system for tire cross-section components was designed using the MATLAB platform. Our experimental results showed that this method displayed a good detection performance, and important practical significance for the research and manufacture of tires.

Research on Several Key Techniques of Power Tool Rest Noise Detection System

2012 ◽  
Vol 630 ◽  
pp. 271-275
Author(s):  
Xiao Hong Lu ◽  
Yong Yan Shang ◽  
Peng Zhuo Han ◽  
Guang Jun Li ◽  
Wen Yi Wu
Keyword(s):  
Spectrum Analysis ◽  
Detection Method ◽  
Detection System ◽  
Sampled Data ◽  
Noise Detection ◽  
Machining Center ◽  
Cnc Lathe ◽  
Spectrum Transformation ◽  
Key Techniques

The scarcity and imperfection of power tool rest noise detection method have seriously limited the development of the industry of CNC lathe, lathe and milling composite machining center. A noise detection system based on LabVIEW is developed. The developed system adopts a noise sensor as noise detection component to test the noise information of the power tool rest. To enhance the anti-interference ability of this system, the sampled signals are amplified and adjusted by the signal disposal instrument. Through the spectrum transformation and spectrum analysis of the sampled noise signals, the noise causes of the power tool rest can be inquired and the concerned measurements can be taken to reduce the noise effectively. Finally, the sampled data is stored by the data saving function.

scholarly journals An Automata Based Intrusion Detection Method for Internet of Things

2017 ◽  
Vol 2017 ◽  
pp. 1-13 ◽  
Author(s):  
Yulong Fu ◽  
Zheng Yan ◽  
Jin Cao ◽  
Ousmane Koné ◽  
Xuefei Cao
Keyword(s):  
Open Access ◽  
Internet Of Things ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection Method ◽  
Detection System ◽  
Network Communication ◽  
5G Networks ◽  
Machine To Machine ◽  
Security Of Iot

Internet of Things (IoT) transforms network communication to Machine-to-Machine (M2M) basis and provides open access and new services to citizens and companies. It extends the border of Internet and will be developed as one part of the future 5G networks. However, as the resources of IoT’s front devices are constrained, many security mechanisms are hard to be implemented to protect the IoT networks. Intrusion detection system (IDS) is an efficient technique that can be used to detect the attackers when cryptography is broken, and it can be used to enforce the security of IoT networks. In this article, we analyzed the intrusion detection requirements of IoT networks and then proposed a uniform intrusion detection method for the vast heterogeneous IoT networks based on an automata model. The proposed method can detect and report the possible IoT attacks with three types: jam-attack, false-attack, and reply-attack automatically. We also design an experiment to verify the proposed IDS method and examine the attack of RADIUS application.

scholarly journals An adaptive wavelet transformation filtering algorithm for improving road anomaly detection and characterization in vehicular technology

2019 ◽  
Vol 9 (5) ◽  
pp. 3664
Author(s):  
Habeeb Bello-Salau ◽  
A. J. Onumanyi ◽  
B. O. Sadiq ◽  
H. Ohize ◽  
A. T. Salawudeen ◽  
...  
Keyword(s):  
Anomaly Detection ◽  
False Alarm ◽  
Wavelet Transformation ◽  
Multiple Scales ◽  
Detection System ◽  
Spatial Filter ◽  
Adaptive Threshold ◽  
Wavelet Coefficients ◽  
Adaptive Wavelet ◽  
Vehicular Technology

Accelerometers are widely used in modern vehicular technologies to automatically detect and characterize road anomalies such as potholes and bumps. However, measurements from an accelerometer are usually plagued by high noise levels, which typically increase the false alarm and misdetection rates of an anomaly detection system. To address this problem, we have developed in this paper an adaptive threshold estimation technique to filter accelerometer measurements effectively to improve road anomaly detection and characterization in vehicular technologies. Our algorithm decomposes the output signal of an accelerometer into multiple scales using wavelet transformation (WT). Then, it correlates the wavelet coefficients across adjacent scales and classifies them using a newly proposed adaptive threshold technique. Furthermore, our algorithm uses a spatial filter to smoothen further the correlated coefficients before using these coefficients to detect road anomalies. Our algorithm then characterizes the detected road anomalies using two unique features obtained from the filtered wavelet coefficients to differentiate potholes from bumps. The findings from several comparative tests suggest that our algorithm successfully detects and characterizes road anomalies with high levels of accuracy, precision and low false alarm rates as compared to other known methods.

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