scholarly journals Storage-Efficient 16-Bit Hybrid IP Traceback with Single Packet

2014 ◽  
Vol 2014 ◽  
pp. 1-11 ◽  
Author(s):  
Ming Hour Yang
Keyword(s):  
User Interface ◽  
False Positive ◽  
High Accuracy ◽  
Small Degree ◽  
False Positives ◽  
Storage Requirement ◽  
Ip Traceback ◽  
Packet Marking

Since adversaries may spoof their source IPs in the attacks, traceback schemes have been proposed to identify the attack source. However, some of these schemes’ storage requirements increase with packet numbers. Some even have false positives because they use an IP header’s fragment offset for marking. Thus, we propose a 16-bit single packet hybrid IP traceback scheme that combines packet marking and packet logging with high accuracy and low storage requirement. The size of our log tables can be bounded by route numbers. We also set a threshold to determine whether an upstream interface number is stored in a log table or in a marking field, so as to balance the logging frequency and our computational loads. Because we store user interface information on small-degree routers, compared with current single packet traceback schemes, ours can have the lowest storage requirements. Besides, our traceback achieves zero false positive/negative rates and guarantees reassembly of fragmented packets at the destination.

scholarly journals Hybrid Single-Packet IP Traceback with Low Storage and High Accuracy

2014 ◽  
Vol 2014 ◽  
pp. 1-12 ◽  
Author(s):  
Ming Hour Yang
Keyword(s):  
False Positive ◽  
False Negative ◽  
High Accuracy ◽  
Storage Requirement ◽  
Ip Traceback ◽  
Ip Addresses ◽  
Packet Marking ◽  
High Storage

Traceback schemes have been proposed to trace the sources of attacks that usually hide by spoofing their IP addresses. Among these methods, schemes using packet logging can achieve single-packet traceback. But packet logging demands high storage on routers and therefore makes IP traceback impractical. For lower storage requirement, packet logging and packet marking are fused to make hybrid single-packet IP traceback. Despite such attempts, their storage still increases with packet numbers. That is why RIHT bounds its storage with path numbers to guarantee low storage. RIHT uses IP header’s ID and offset fields to mark packets, so it inevitably suffers from fragment and drop issues for its packet reassembly. Although the 16-bit hybrid IP traceback schemes, for example, MORE, can mitigate the fragment problem, their storage requirement grows up with packet numbers. To solve the storage and fragment problems in one shot, we propose a single-packet IP traceback scheme that only uses packets’ ID field for marking. Our major contributions are as follows: (1) our fragmented packets with tracing marks can be reassembled; (2) our storage is not affected by packet numbers; (3) it is the first hybrid single-packet IP traceback scheme to achieve zero false positive and zero false negative rates.

scholarly journals Train Fast While Reducing False Positives: Improving Animal Classification Performance Using Convolutional Neural Networks

Geomatics ◽  
2021 ◽  
Vol 1 (1) ◽  
pp. 34-49
Author(s):  
Mael Moreni ◽  
Jerome Theau ◽  
Samuel Foucher
Keyword(s):  
False Positive ◽  
Classification Performance ◽  
Large Datasets ◽  
False Positives ◽  
Fine Tuning ◽  
Training Time ◽  
In The Wild ◽  
Test Sets ◽  
High Level ◽  
Time Decrease

The combination of unmanned aerial vehicles (UAV) with deep learning models has the capacity to replace manned aircrafts for wildlife surveys. However, the scarcity of animals in the wild often leads to highly unbalanced, large datasets for which even a good detection method can return a large amount of false detections. Our objectives in this paper were to design a training method that would reduce training time, decrease the number of false positives and alleviate the fine-tuning effort of an image classifier in a context of animal surveys. We acquired two highly unbalanced datasets of deer images with a UAV and trained a Resnet-18 classifier using hard-negative mining and a series of recent techniques. Our method achieved sub-decimal false positive rates on two test sets (1 false positive per 19,162 and 213,312 negatives respectively), while training on small but relevant fractions of the data. The resulting training times were therefore significantly shorter than they would have been using the whole datasets. This high level of efficiency was achieved with little tuning effort and using simple techniques. We believe this parsimonious approach to dealing with highly unbalanced, large datasets could be particularly useful to projects with either limited resources or extremely large datasets.

Evaluation of Positive T- and B-Cell Gene Rearrangement Studies Among Patients Without a Definitive Diagnosis by Other Assays

2019 ◽  
Vol 152 (Supplement_1) ◽  
pp. S35-S36
Author(s):  
Hadrian Mendoza ◽  
Christopher Tormey ◽  
Alexa Siddon
Keyword(s):  
T Cell ◽  
False Positive ◽  
Gene Rearrangement ◽  
Hematologic Malignancy ◽  
False Negative ◽  
False Positives ◽  
False Negatives ◽  
True Negative ◽  
Flow Cytometric ◽  
Pathology Reports

Abstract In the evaluation of bone marrow (BM) and peripheral blood (PB) for hematologic malignancy, positive immunoglobulin heavy chain (IG) or T-cell receptor (TCR) gene rearrangement results may be detected despite unrevealing results from morphologic, flow cytometric, immunohistochemical (IHC), and/or cytogenetic studies. The significance of positive rearrangement studies in the context of otherwise normal ancillary findings is unknown, and as such, we hypothesized that gene rearrangement studies may be predictive of an emerging B- or T-cell clone in the absence of other abnormal laboratory tests. Data from all patients who underwent IG or TCR gene rearrangement testing at the authors’ affiliated VA hospital between January 1, 2013, and July 6, 2018, were extracted from the electronic medical record. Date of testing; specimen source; and morphologic, flow cytometric, IHC, and cytogenetic characterization of the tissue source were recorded from pathology reports. Gene rearrangement results were categorized as true positive, false positive, false negative, or true negative. Lastly, patient records were reviewed for subsequent diagnosis of hematologic malignancy in patients with positive gene rearrangement results with negative ancillary testing. A total of 136 patients, who had 203 gene rearrangement studies (50 PB and 153 BM), were analyzed. In TCR studies, there were 2 false positives and 1 false negative in 47 PB assays, as well as 7 false positives and 1 false negative in 54 BM assays. Regarding IG studies, 3 false positives and 12 false negatives in 99 BM studies were identified. Sensitivity and specificity, respectively, were calculated for PB TCR studies (94% and 93%), BM IG studies (71% and 95%), and BM TCR studies (92% and 83%). Analysis of PB IG gene rearrangement studies was not performed due to the small number of tests (3; all true negative). None of the 12 patients with false-positive IG/TCR gene rearrangement studies later developed a lymphoproliferative disorder, although 2 patients were later diagnosed with acute myeloid leukemia. Of the 14 false negatives, 10 (71%) were related to a diagnosis of plasma cell neoplasms. Results from the present study suggest that positive IG/TCR gene rearrangement studies are not predictive of lymphoproliferative disorders in the context of otherwise negative BM or PB findings. As such, when faced with equivocal pathology reports, clinicians can be practically advised that isolated positive IG/TCR gene rearrangement results may not indicate the need for closer surveillance.

scholarly journals The KELT Follow-up Network and Transit False-positive Catalog: Pre-vetted False Positives for TESS

2018 ◽  
Vol 156 (5) ◽  
pp. 234 ◽  
Author(s):  
Karen A. Collins ◽  
Kevin I. Collins ◽  
Joshua Pepper ◽  
Jonathan Labadie-Bartz ◽  
Keivan G. Stassun ◽  
...  
Keyword(s):  
False Positive ◽  
False Positives

The Study of the Ontology and Context Verification Based Intrusion Detection Model

2014 ◽  
Vol 644-650 ◽  
pp. 3338-3341 ◽  
Author(s):  
Guang Feng Guo
Keyword(s):  
Intrusion Detection ◽  
Knowledge Base ◽  
False Positive ◽  
Intrusion Detection System ◽  
Detection System ◽  
False Positive Rate ◽  
False Positives ◽  
The Real ◽  
Detection Model ◽  
Positive Rate

During the 30-year development of the Intrusion Detection System, the problems such as the high false-positive rate have always plagued the users. Therefore, the ontology and context verification based intrusion detection model (OCVIDM) was put forward to connect the description of attack’s signatures and context effectively. The OCVIDM established the knowledge base of the intrusion detection ontology that was regarded as the center of efficient filtering platform of the false alerts to realize the automatic validation of the alarm and self-acting judgment of the real attacks, so as to achieve the goal of filtering the non-relevant positives alerts and reduce false positives.

Follow-up Photometry in Another Band Helps to Reduce Kepler’s False-positive Rates

2021 ◽  
Vol 162 (6) ◽  
pp. 258
Author(s):  
Mu-Tian Wang ◽  
Hui-Gen Liu ◽  
Jiapeng Zhu ◽  
Ji-Lin Zhou
Keyword(s):  
False Positive ◽  
Binary Systems ◽  
False Positives ◽  
Eclipsing Binaries ◽  
Identification Rate ◽  
Transiting Planets ◽  
Secondary Star ◽  
Band Depth ◽  
Depth Feature

Abstract The Kepler mission’s single-band photometry suffers from astrophysical false positives, most commonly of background eclipsing binaries (BEBs) and companion transiting planets (CTPs). Multicolor photometry can reveal the color-dependent depth feature of false positives and thus exclude them. In this work, we aim to estimate the fraction of false positives that cannot be classified by Kepler alone but can be identified from their color-dependent depth feature if a reference band (z, K s , and Transiting Exoplanet Survey Satellite (TESS)) is adopted in follow-up observation. We construct physics-based blend models to simulate multiband signals of false positives. Nearly 65%–95% of the BEBs and more than 80% of the CTPs that host a Jupiter-sized planet will show detectable depth variations if the reference band can achieve a Kepler-like precision. The K s band is most effective in eliminating BEBs exhibiting features of any depth, while the z and TESS bands are better for identifying giant candidates, and their identification rates are more sensitive to photometric precision. Given the radius distribution of planets transiting the secondary star in binary systems, we derive a formalism to calculate the overall identification rate for CTPs. By comparing the likelihood distribution of the double-band depth ratio for BEB and planet models, we calculate the false-positive probability (FPP) for typical Kepler candidates. Additionally, we show that the FPP calculation helps distinguish the planet candidate’s host star in an unresolved binary system. The framework of the analysis in this paper can be easily adapted to predict the multicolor photometric yield for other transit surveys, especially TESS.

scholarly journals Automated detection of cribriform growth patterns in prostate histology images

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Pierre Ambrosini ◽  
Eva Hollemans ◽  
Charlotte F. Kweldam ◽  
Geert J. L. H. van Leenders ◽  
Sjoerd Stallinga ◽  
...  
Keyword(s):  
Deep Learning ◽  
False Positive ◽  
Clinical Decision Making ◽  
False Negative ◽  
Area Under The Curve ◽  
High Sensitivity ◽  
False Positives ◽  
Growth Patterns ◽  
Learning Method ◽  
Method Performance

Abstract Cribriform growth patterns in prostate carcinoma are associated with poor prognosis. We aimed to introduce a deep learning method to detect such patterns automatically. To do so, convolutional neural network was trained to detect cribriform growth patterns on 128 prostate needle biopsies. Ensemble learning taking into account other tumor growth patterns during training was used to cope with heterogeneous and limited tumor tissue occurrences. ROC and FROC analyses were applied to assess network performance regarding detection of biopsies harboring cribriform growth pattern. The ROC analysis yielded a mean area under the curve up to 0.81. FROC analysis demonstrated a sensitivity of 0.9 for regions larger than $${0.0150}\,\hbox {mm}^{2}$$ 0.0150 mm 2 with on average 7.5 false positives. To benchmark method performance for intra-observer annotation variability, false positive and negative detections were re-evaluated by the pathologists. Pathologists considered 9% of the false positive regions as cribriform, and 11% as possibly cribriform; 44% of the false negative regions were not annotated as cribriform. As a final experiment, the network was also applied on a dataset of 60 biopsy regions annotated by 23 pathologists. With the cut-off reaching highest sensitivity, all images annotated as cribriform by at least 7/23 of the pathologists, were all detected as cribriform by the network and 9/60 of the images were detected as cribriform whereas no pathologist labelled them as such. In conclusion, the proposed deep learning method has high sensitivity for detecting cribriform growth patterns at the expense of a limited number of false positives. It can detect cribriform regions that are labelled as such by at least a minority of pathologists. Therefore, it could assist clinical decision making by suggesting suspicious regions.

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