The Trade-Off Between the False-Positive Ratio and the Attack Cost of Slow HTTP DoS

2020 ◽  
pp. 225-237
Author(s):  
Tetsuya Hirakawa ◽  
Toyoo Takata
Keyword(s):  
False Positive ◽  
Trade Off ◽  
Positive Ratio ◽  
False Positive Ratio

scholarly journals Quality Assessment of Tire Shearography Images via Ensemble Hybrid Faster Region-Based ConvNets

Electronics ◽  
2019 ◽  
Vol 9 (1) ◽  
pp. 45 ◽  
Author(s):  
Chuan-Yu Chang ◽  
Kathiravan Srinivasan ◽  
Wei-Chun Wang ◽  
Ganapathy Pattukandan Ganapathy ◽  
Durai Raj Vincent ◽  
...  
Keyword(s):  
Neural Networks ◽  
Quality Assessment ◽  
Convolutional Neural Networks ◽  
False Positive ◽  
High Performance ◽  
Learning Approaches ◽  
Positive Ratio ◽  
False Positive Ratio ◽  
Proposed Model ◽  
Human Eyes

In recent times, the application of enabling technologies such as digital shearography combined with deep learning approaches in the smart quality assessment of tires, which leads to intelligent tire manufacturing practices with automated defects detection. Digital shearography is a prominent approach that can be employed for identifying the defects in tires, usually not visible to human eyes. In this research, the bubble defects in tire shearography images are detected using a unique ensemble hybrid amalgamation of the convolutional neural networks/ConvNets with high-performance Faster Region-based convolutional neural networks. It can be noticed that the routine of region-proposal generation along with object detection is accomplished using the ConvNets. Primarily, the sliding window based ConvNets are utilized in the proposed model for dividing the input shearography images into regions, in order to identify the bubble defects. Subsequently, this is followed by implementing the Faster Region-based ConvNets for identifying the bubble defects in the tire shearography images and further, it also helps to minimize the false-positive ratio (sometimes referred to as the false alarm ratio). Moreover, it is evident from the experimental results that the proposed hybrid model offers a cent percent detection of bubble defects in the tire shearography images. Also, it can be witnessed that the false-positive ratio gets minimized to 18 percent.

Implementation of Improved Hash and Mapping Modified Low Power Parallel Bloom Filter Design

2013 ◽  
Vol 7 (4) ◽  
pp. 11-21
Author(s):  
K. Saravanan ◽  
A. Senthilkumar
Keyword(s):  
Low Power ◽  
False Positive ◽  
Filter Design ◽  
Parallel Architecture ◽  
Bloom Filter ◽  
Bloom Filters ◽  
Positive Ratio ◽  
False Positive Ratio ◽  
Field Programmable ◽  
Mapping Scheme

In this article, the authors present an investigation on bloom filters and introduce a new improved variant, which uses a secure modified hash function and suggested improved mapping scheme with an efficient parallel architecture. This novel architecture provides efficient, relatively fast membership querying and compact information representation with negligible false positive. This is relatively a low power and secure design with very less false positive ratio when compared with the traditional bloom filters. The design has been evaluated and tested using Xilinx 65 nm Virtex-5 field-programmable gate array as the target technology. The performance matrices are false positive ratio, power, speed and compactness.

Limits of the ‘Mini-Mental State’ as a screening test for dementia and delirium among hospital patients

1982 ◽  
Vol 12 (2) ◽  
pp. 397-408 ◽  
Author(s):  
James C. Anthony ◽  
Linda LeResche ◽  
Unaiza Niaz ◽  
Michael R. Von Korff ◽  
Marshal F. Folstein
Keyword(s):  
Mental State ◽  
False Positive ◽  
Mini Mental State Examination ◽  
False Negative ◽  
General Medical Ward ◽  
Positive Ratio ◽  
False Positive Ratio ◽  
Hospital Patients ◽  
Screen Instrument ◽  
State Examination

SynopsisWith a psychiatrist's standardized clinical diagnosis as the criterion, the ‘Mini-Mental State’ Examination (MMSE) was 87% sensitive and 82% specific in detecting dementia and delirium among hospital patients on a general medical ward. The false positive ratio was 39% and the false negative ratio was 5 %. All false positives had less than 9 years of education; many were 60 years of age or older. Performance on specific MMSE items was related to education or age. These findings confirm the MMSE's value as a screen instrument for dementia and delirium when later, more intensive diagnostic enquiry is possible; they reinforce earlier suggestions that the MMSE alone cannot yield a diagnosis for these conditions.

A Hybrid Approach for Accurate BT Traffic Identification

2010 ◽  
Vol 108-111 ◽  
pp. 279-284
Author(s):  
Ru Hui Zhang ◽  
Ye Du ◽  
Xing Wang ◽  
Zhong Lan Yuan
Keyword(s):  
False Positive ◽  
Hybrid Approach ◽  
Transfer Mechanism ◽  
Preliminary Evaluation ◽  
Traffic Identification ◽  
Positive Ratio ◽  
False Positive Ratio ◽  
Information Method ◽  
Flow Information ◽  
Encrypted Traffic

In this paper, a hybrid approach for identifying the traffic running over BitTorrent (BT) protocol is proposed. Besides the conventional port-based and signature-based methods, another two BT-oriented methods dealing with the peer-information and flow-information of BT traffic are also adopted. The peer-information method makes use of the unencrypted peer-transfer mechanism of BT protocol, and the flow-information method focuses on identifying the encrypted traffic, which evades the above three methods, with low false-positive ratio. The preliminary evaluation shows that our hybrid approach is effective and comprehensive for BT traffic identification.

scholarly journals A Novel Prefix Cache with Two-Level Bloom Filters in IP Address Lookup

2020 ◽  
Vol 10 (20) ◽  
pp. 7198
Author(s):  
Junghwan Kim ◽  
Myeong-Cheol Ko ◽  
Moon Sun Shin ◽  
Jinsoo Kim
Keyword(s):  
False Positive ◽  
Bloom Filter ◽  
Bloom Filters ◽  
Experimental Result ◽  
Ip Address ◽  
Positive Ratio ◽  
Ip Addresses ◽  
False Positive Ratio ◽  
Ip Address Lookup ◽  
Filter Size

Prefix caching is one of the notable techniques in enhancing the IP address lookup performance which is crucial in packet forwarding. A cached prefix can match a range of IP addresses, so prefix caching leads to a higher cache hit ratio than IP address caching. However, prefix caching has an issue to be resolved. When a prefix is matched in a cache, the prefix cannot be the result without assuring that there is no longer descendant prefix of the matching prefix which is not cached yet. This is due to the aspect of the IP address lookup seeking to find the longest matching prefix. Some prefix expansion techniques avoid the problem, but the expanded prefixes occupy more entries as well as cover a smaller range of IP addresses. This paper proposes a novel prefix caching scheme in which the original prefix can be cached without expansion. In this scheme, for each prefix, a Bloom filter is constructed to be used for testing if there is any matchable descendant. The false positive ratio of a Bloom filter generally grows as the number of elements contained in the filter increases. We devise an elaborate two-level Bloom filter scheme which adjusts the filter size at each level, to reduce the false positive ratio, according to the number of contained elements. The experimental result shows that the proposed scheme achieves a very low cache miss ratio without increasing the number of prefixes. In addition, most of the filter assertions are negative, which means the proposed prefix cache effectively hits the matching prefix using the filter.

scholarly journals Calibrating Thresholds based on Trade-Offs Between Detection Accuracy and FPR for Copy Move Forgery Detection

2019 ◽  
Vol 8 (2) ◽  
pp. 3658-3663
Keyword(s):  
False Positive ◽  
Feature Vector ◽  
Roc Curves ◽  
Optimal Threshold ◽  
Detection Accuracy ◽  
Forgery Detection ◽  
Positive Ratio ◽  
False Positive Ratio ◽  
Harris Corner ◽  
Corner Points

In this paper, prominent keypoint based features are compared in order to analyze their reliability and efficiency against forgery detection. Four features specifically SURF, KAZE, Harris corner points and BRISK features are used individually on a set of images. The method includes four phases: Image pre-processing, keypoint detection, feature vector description and feature vector matching. In feature matching, MaxRatio has been chosen as a varying parameter for calculating values of false positives and false negatives for each feature. MaxRatio defines the ratio for rejecting ambiguous matches of feature descriptors in the images. The optimal threshold value for MaxRatio is calibrated with the help of trade-off between detection accuracy and false positive ratio. The changes in false negative ratio and false positive ratio are picturized in order to find out optimal threshold for detection accuracy. ROC curves are also plotted for each feature at different values of MaxRatio and area under the ROC curves are calculated. The experiments are performed on two benchmark datasets, namely CASIA version 2.0 and MICC-F600. It has been perceived from experimental outcomes that KAZE features gave best values for all the performance metrics namely accuracy, precision, area under the ROC curve and F1-score with little compromise in time complexity, whereas Harris corner points gave the worst results as compared to rest of the features. Further, in order to improve the execution time, the computation of non-linear scale space process in KAZE can be simplified and GPU programming for real-time performance may also be used.

scholarly journals Retrospective analysis of false positive ratio of our patients with lung cancer at positron emission tomography-CT screen

2020 ◽  
Vol 22 (1) ◽  
pp. 23
Author(s):  
Pınar Mutlu ◽  
HasanOguz Kapicibasi ◽  
şahınurAycan Alkan ◽  
NihalArzu Mirici ◽  
Buse Yuksel ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Positron Emission Tomography ◽  
Retrospective Analysis ◽  
False Positive ◽  
Emission Tomography ◽  
Positive Ratio ◽  
Positron Emission ◽  
False Positive Ratio

scholarly journals Deep Consensus, a deep learning-based approach for particle pruning in cryo-electron microscopy

IUCrJ ◽  
2018 ◽  
Vol 5 (6) ◽  
pp. 854-865 ◽  
Author(s):  
Ruben Sanchez-Garcia ◽  
Joan Segura ◽  
David Maluenda ◽  
Jose Maria Carazo ◽  
Carlos Oscar S. Sorzano
Keyword(s):  
Electron Microscopy ◽  
Deep Learning ◽  
Single Particle ◽  
False Positive ◽  
Data Set ◽  
Positive Ratio ◽  
Whole Process ◽  
Cryo Electron Microscopy ◽  
False Positive Ratio ◽  
User Intervention

Single-particle cryo-electron microscopy (cryo-EM) has recently become a mainstream technique for the structural determination of macromolecules. Typical cryo-EM workflows collect hundreds of thousands of single-particle projections from thousands of micrographs using particle-picking algorithms. However, the number of false positives selected by these algorithms is large, so that a number of different `cleaning steps' are necessary to decrease the false-positive ratio. Most commonly employed techniques for the pruning of false-positive particles are time-consuming and require user intervention. In order to overcome these limitations, a deep learning-based algorithm named Deep Consensus is presented in this work. Deep Consensus works by computing a smart consensus over the output of different particle-picking algorithms, resulting in a set of particles with a lower false-positive ratio than the initial set obtained by the pickers. Deep Consensus is based on a deep convolutional neural network that is trained on a semi-automatically generated data set. The performance of Deep Consensus has been assessed on two well known experimental data sets, virtually eliminating user intervention for pruning, and enhances the reproducibility and objectivity of the whole process while achieving precision and recall figures above 90%.

Epileptic Seizure Detection System Based on Multi-Domain Feature and Spike Feature of EEG

2019 ◽  
Vol 16 (04) ◽  
pp. 1950016 ◽  
Author(s):  
Duanpo Wu ◽  
Zimeng Wang ◽  
Hong Huang ◽  
Guangsheng Wang ◽  
Junbiao Liu ◽  
...  
Keyword(s):  
Detection System ◽  
Epileptic Seizures ◽  
Seizure Detection ◽  
Spike Rate ◽  
Linear Discriminant ◽  
Positive Ratio ◽  
False Positive Ratio ◽  
Eeg Data ◽  
The Family

Epilepsy is caused by sudden abnormal discharges of neurons in the brain. This paper constructs an automatic seizure detection system, which combines the predicting result of multi-domain feature with the predicting result of spike rate feature to detect the occurrence of epileptic seizures. After segmenting EEG data into 5[Formula: see text]s with 80% overlap epochs, the paper extracts time domain features, frequency domain features and hurst exponents (HE) from each epoch and these features are reduced by linear discriminant analysis (LDA) to be input parameters of the random forest (RF) classifier, which provides classification of the EEG epochs concerning the existence of seizures. In parallel, the paper extracts spikes from EEG data with morphological filter and calculates the spike rate to determine whether there is seizure. Then the results obtained by these two methods are merged as the final detection result. The paper shows that the accuracy (AC), sensitivity (SE), specificity (SP) and the false positive ratio based on event (FPRE) obtained by hybrid method are 98.94%, 76.60%, 98.99% and 2.43 times/h, respectively. Finally, the paper applies the seizure detection method to do seizure warning and recording to help the family member to take care of the patients and the doctor to adjust the antiepileptic drugs (AEDs).

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