scholarly journals Data Authentication for Wireless Sensor Networks with High Detection Efficiency Based on Reversible Watermarking

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Guangyong Gao ◽  
Zhao Feng ◽  
Tingting Han
Keyword(s):  
Wireless Sensor Networks ◽  
False Positive ◽  
Detection Efficiency ◽  
False Positive Rate ◽  
Reversible Watermarking ◽  
Wireless Sensor ◽  
Data Authentication ◽  
High Detection ◽  
High Detection Efficiency ◽  
Positive Rate

Data authentication is an important part of wireless sensor networks (WSNs). Aiming at the problems of high false positive rate and poor robustness in group verification of existing reversible watermarking schemes in WSNs, this paper proposes a scheme using reversible watermarking technology to achieve data integrity authentication with high detection efficiency (DAHDE). The core of DAHDE is dynamic grouping and double verification algorithm. Under the condition of satisfying the requirement of the group length, the synchronization point is used for dynamic grouping, and the double verification ensures that the grouping will not be confused. According to the closely related characteristics of adjacent data in WSNs, a new data item prediction method is designed based on the prediction-error expansion formula, and a flag check bit is added to the data with embedded watermarking during data transmission to ensure the stability of grouping, by which the fake synchronization point can be accurately identified. Moreover, the embedded data can be recovered accurately through the reversible algorithm of digital watermarking. Analysis and experimental results show that compared with the previously known schemes, the proposed scheme can avoid false positive rate, reduce computation cost, and own stronger grouping robustness.

A Novel Method Based on Clustering Algorithm and SVM for Anomaly Intrusion Detection of Wireless Sensor Networks

2011 ◽  
Vol 121-126 ◽  
pp. 3745-3749
Author(s):  
Zheng Hong Xiao ◽  
Zhi Gang Chen ◽  
Xiao Heng Deng
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Intrusion Detection ◽  
Clustering Algorithm ◽  
False Positive Rate ◽  
Wireless Sensor ◽  
Support Vector ◽  
High Detection Rate ◽  
Positive Rate ◽  
Anomaly Intrusion Detection

Based on the principle that the same class is adjacent, an anomaly intrusion detection method based on K-means and Support Vector Machine (SVM) is presented. In order to overcome the disadvantage that k-means algorithm requires initializing parameters, this paper proposes an improved K-means algorithm with a strategy of adjustable parameters. According to the location of wireless sensor networks (WSN), we can obtain clustering results by applying improved K-means algorithm to WSN, and then SVM algorithm is applied to different clusters for anomaly intrusion detection. Simulation results show that the proposed method can detect abnormal behaviors efficiently and has high detection rate and low false positive rate than the current typical intrusion detection schemes of WSN.

Density-Based Distributed Elliptical Anomaly Detection in Wireless Sensor Networks

2012 ◽  
Vol 249-250 ◽  
pp. 226-230 ◽  
Author(s):  
Shu Hua Ma ◽  
Jin Kuan Wang ◽  
Zhi Gang Liu ◽  
Hou Yan Jiang
Keyword(s):  
Wireless Sensor Networks ◽  
Research Laboratory ◽  
Detection Rate ◽  
False Positive Rate ◽  
Wireless Sensor ◽  
Battery Power ◽  
Positive Rate ◽  
The Usa ◽  
Important Challenge ◽  
Energy Constrained

Data measured and collected by WSNs is often unreliable and a big amount of anomaly data exist. Detecting these anomaly in energy-constrained situations is an important challenge in managing these types of networks. To detect anomalies induced by the decrease of battery power, we use HyCARCE algorithm but it has the problem of low detection rate and high false positive rate when the input space consists of a mixture of dense and sparse regions which make the anomalies form clusters. The paper presents a density-based algorithm to separate the normal cluster from all clusters. The performance of this algorithm is tested on a subset of the data gathered from a real sensor network deployed at the Intel Berkeley Research Laboratory in the USA and this density-based method has a better detection performance than HyCARCE algorithm.

Distributed Malicious Nodes Detection in Wireless Sensor Networks

2014 ◽  
Vol 519-520 ◽  
pp. 1243-1246
Author(s):  
Zhao Hui Zhang ◽  
Ming Ming Hu ◽  
Dong Li ◽  
Xiao Gang Qi
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
False Positive Rate ◽  
Abnormal Behavior ◽  
Wireless Sensor ◽  
Malicious Nodes ◽  
Request Message ◽  
Positive Rate ◽  
Simulation Results ◽  
Great Damage

Data theft and node attack in wireless sensor networks causes great damage to the networks and the attacker destroys network and obtains the data of the network by malicious nodes distributed in the network. Therefore, it is necessary to detect these malicious nodes and to eliminate their influence. We propose a distributed malicious nodes detection protocol which called BMND based on Bayesian voting, every node determine its suspected malicious nodes by its request message and abnormal behavior. Also, we determine the malicious nodes by Bayesian voting, so that the network can protect itself from such malicious nodes influence. The simulation results show that our algorithm has good performance in both the detection rate and false positive rate.

Accuracy of cardiac auscultation in detection of neonatal congenital heart disease by general paediatricians

2019 ◽  
Vol 29 (5) ◽  
pp. 679-683
Author(s):  
Qu-ming Zhao ◽  
Conway Niu ◽  
Fang Liu ◽  
Lin Wu ◽  
Xiao-jing Ma ◽  
...  
Keyword(s):  
False Positive ◽  
False Positive Rate ◽  
Cardiac Auscultation ◽  
Detection Rates ◽  
Maternity Hospitals ◽  
High Detection ◽  
Positive Rate ◽  
Timely Fashion ◽  
A Prospective Study

AbstractBackground:Challenges remain in the judgement of pathological murmurs in newborns at maternity hospitals, and there are still many simple major CHD patients in developing countries who are not diagnosed in a timely fashion. This study aimed to evaluate the accuracy of cardiac auscultation on neonatal CHD by general paediatricians.Methods:We conducted a prospective study at three hospitals. All asymptomatic newborns underwent auscultation, pulse oximetry monitoring, and echocardiography. Major CHD was classified and confirmed through follow-up. We evaluated the accuracy of various degrees of murmurs for detecting major CHD to determine the most appropriate standards and time of auscultation.Results:A total of 6750 newborns were included. The median age of auscultation was 43 hours. Cardiac murmurs were identified in 6.6% of newborns. For all CHD, 44.4% had varying degrees of murmurs. A murmur of grade ≥2 used as a reference standard for major CHD had a sensitivity of 89.58%. The false positive rate of murmurs of grade ≥2 for detecting major CHD was significantly negatively related to auscultation time, with 84.4% of false positives requiring follow-up for non-major CHD cardiac issues. Auscultation after 27 hours of life could reduce the false positive rate of major CHD from 2.7 to 0.9%.Conclusions:With appropriate training, maternity hospital’s paediatricians can detect major CHD with high detection rates with an acceptable false positive rate.

scholarly journals A Node Confident based IDS to Avoid Packet Drop Attacks for Wireless Sensor Network

2021 ◽  
Vol 13 (6) ◽  
pp. 41-56
Author(s):  
Kareti Madhava Rao ◽  
◽  
S Ramakrishna
Keyword(s):  
False Positive ◽  
False Positive Rate ◽  
Wireless Sensor ◽  
Reputation Systems ◽  
Data Packets ◽  
Traffic Pattern ◽  
Drop Attack ◽  
Centralized Management ◽  
Positive Rate ◽  
Packet Drop

Because of the great characteristics of Wireless Sensor Networks like easier to use and less cost of deployment, they have attracted the researchers to conduct the investigations and received the importance in various civilian and military applications. A number of security attacks have been involved due to the lack of centralized management in these networks. The packet drop attack is one of the attacks and it has a compromised node which drops the malicious packets. In WSNs, different techniques have been implemented to identify the packet drop attack but none of them provides the feasibility to stop or isolate their occurrence in the future. In recent times, the reputation systems provide the way to identify the trustworthy nodes for data forwarding. But the lack of data classification in the reputation systems affects the false positive rate. In this paper, a novel CONFIDENT SCORE based BAYESIAN FILTER NODE MONITORING AGENT (CFS-BFNMA) mechanism is introduced to identify & avoid the packet drop nodes and also to monitor the node behaviours to improve the false positive rate. The final CFS of a node is estimated based on the node past and threshold CFS values. The node monitoring agents (BFNMA) constantly monitors the forwarding behaviour of the nodes and assigns CFS based on the successful forwards. The NMA saves the copy of the data packets in their buffers before forwarding to the neighbour nodes to compare them. Also, this BFNMA analyses the traffic pattern of every round of transmission to improve the false positive rate. By comparing with other conventional security algorithms, the proposed mechanism has been improved the network security & false positive rate drastically based on the simulation results.

Contour Extraction of Flame for Fire Detection

2011 ◽  
Vol 383-390 ◽  
pp. 1106-1110
Author(s):  
Yun Yang Yan ◽  
Shang Bing Gao ◽  
Hong Yan Wang ◽  
Zhi Bo Guo
Keyword(s):  
False Positive ◽  
Detection Rate ◽  
False Positive Rate ◽  
Fire Detection ◽  
Video Sequences ◽  
Contour Extraction ◽  
High Detection Rate ◽  
Contour Feature ◽  
High Detection ◽  
Positive Rate

Fire detection based on sequences of images is more suitable for the need in big room or badly environment. Color and contour are both the important features of a flame image. The method to extract the contour feature of a flame image is developed based on threshold of flame area. The edges of the burning flames jitter continuously, but their contour are similar each other. The method to detect flames in video sequences is proposed here based on flame’s dynamic contour. Many experiments show that the system is able to work well and get high detection rate with a low false positive rate.

Improving diagnosis of acute appendicitis with atypical findings by Tc-99m HMPAO leukocyte scan

2002 ◽  
Vol 41 (01) ◽  
pp. 37-41 ◽  
Author(s):  
S. Shung-Shung ◽  
S. Yu-Chien ◽  
Y. Mei-Due ◽  
W. Hwei-Chung ◽  
A. Kao
Keyword(s):  
Acute Appendicitis ◽  
False Positive ◽  
False Positive Rate ◽  
Accurate Method ◽  
Clinical Findings ◽  
Pathological Findings ◽  
Lower Quadrant ◽  
Predictive Values ◽  
Positive Rate

Summary Aim: Even with careful observation, the overall false-positive rate of laparotomy remains 10-15% when acute appendicitis was suspected. Therefore, the clinical efficacy of Tc-99m HMPAO labeled leukocyte (TC-WBC) scan for the diagnosis of acute appendicitis in patients presenting with atypical clinical findings is assessed. Patients and Methods: Eighty patients presenting with acute abdominal pain and possible acute appendicitis but atypical findings were included in this study. After intravenous injection of TC-WBC, serial anterior abdominal/pelvic images at 30, 60, 120 and 240 min with 800k counts were obtained with a gamma camera. Any abnormal localization of radioactivity in the right lower quadrant of the abdomen, equal to or greater than bone marrow activity, was considered as a positive scan. Results: 36 out of 49 patients showing positive TC-WBC scans received appendectomy. They all proved to have positive pathological findings. Five positive TC-WBC were not related to acute appendicitis, because of other pathological lesions. Eight patients were not operated and clinical follow-up after one month revealed no acute abdominal condition. Three of 31 patients with negative TC-WBC scans received appendectomy. They also presented positive pathological findings. The remaining 28 patients did not receive operations and revealed no evidence of appendicitis after at least one month of follow-up. The overall sensitivity, specificity, accuracy, positive and negative predictive values for TC-WBC scan to diagnose acute appendicitis were 92, 78, 86, 82, and 90%, respectively. Conclusion: TC-WBC scan provides a rapid and highly accurate method for the diagnosis of acute appendicitis in patients with equivocal clinical examination. It proved useful in reducing the false-positive rate of laparotomy and shortens the time necessary for clinical observation.

Predicting Fetal Chromosome Anomalies in the First Trimester Using Pregnancy Associated Plasma Protein-A: A Comparison of Statistical Methods

1993 ◽  
Vol 32 (02) ◽  
pp. 175-179 ◽  
Author(s):  
B. Brambati ◽  
T. Chard ◽  
J. G. Grudzinskas ◽  
M. C. M. Macintosh
Keyword(s):  
Logistic Regression ◽  
General Population ◽  
Likelihood Ratio ◽  
False Positive ◽  
False Positive Rate ◽  
Ratio Method ◽  
Detection Rates ◽  
Gaussian Distributions ◽  
Positive Rate ◽  
Likelihood Ratio Method

Abstract:The analysis of the clinical efficiency of a biochemical parameter in the prediction of chromosome anomalies is described, using a database of 475 cases including 30 abnormalities. A comparison was made of two different approaches to the statistical analysis: the use of Gaussian frequency distributions and likelihood ratios, and logistic regression. Both methods computed that for a 5% false-positive rate approximately 60% of anomalies are detected on the basis of maternal age and serum PAPP-A. The logistic regression analysis is appropriate where the outcome variable (chromosome anomaly) is binary and the detection rates refer to the original data only. The likelihood ratio method is used to predict the outcome in the general population. The latter method depends on the data or some transformation of the data fitting a known frequency distribution (Gaussian in this case). The precision of the predicted detection rates is limited by the small sample of abnormals (30 cases). Varying the means and standard deviations (to the limits of their 95% confidence intervals) of the fitted log Gaussian distributions resulted in a detection rate varying between 42% and 79% for a 5% false-positive rate. Thus, although the likelihood ratio method is potentially the better method in determining the usefulness of a test in the general population, larger numbers of abnormal cases are required to stabilise the means and standard deviations of the fitted log Gaussian distributions.

Identification of and Correction for Publication Bias: Comment

2019 ◽  
Author(s):  
Amanda Kvarven ◽  
Eirik Strømland ◽  
Magnus Johannesson
Keyword(s):  
Publication Bias ◽  
False Positive ◽  
Large Scale ◽  
Meta Analysis ◽  
False Positive Rate ◽  
Effect Sizes ◽  
Replication Studies ◽  
Moderate Reduction ◽  
Positive Rate ◽  
Meta Analyses

Andrews & Kasy (2019) propose an approach for adjusting effect sizes in meta-analysis for publication bias. We use the Andrews-Kasy estimator to adjust the result of 15 meta-analyses and compare the adjusted results to 15 large-scale multiple labs replication studies estimating the same effects. The pre-registered replications provide precisely estimated effect sizes, which do not suffer from publication bias. The Andrews-Kasy approach leads to a moderate reduction of the inflated effect sizes in the meta-analyses. However, the approach still overestimates effect sizes by a factor of about two or more and has an estimated false positive rate of between 57% and 100%.

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