Statistical analysis of false positives and false negatives from real traffic with intrusion detection/prevention systems

2012 ◽  
Vol 50 (3) ◽  
pp. 146-154 ◽  
Author(s):  
Cheng-Yuan Ho ◽  
Yuan-Cheng Lai ◽  
I-Wei Chen ◽  
Fu-Yu Wang ◽  
Wei-Hsuan Tai
Keyword(s):  
Statistical Analysis ◽  
Intrusion Detection ◽  
False Positives ◽  
False Negatives ◽  
Real Traffic

scholarly journals False Positives and Negatives from Real Traffic with Intrusion Detection/Prevention Systems

2012 ◽  
pp. 87-90 ◽  
Author(s):  
Cheng-Yuan Ho ◽  
Ying-Dar Lin ◽  
Yuan-Cheng Lai ◽  
I-Wei Chen ◽  
Fu-Yu Wang ◽  
...  
Keyword(s):  
Intrusion Detection ◽  
False Positives ◽  
Real Traffic ◽  
False Positives And Negatives

Reducing False Negatives in Intelligent Intrusion Detection Decision Response System

2011 ◽  
Vol 128-129 ◽  
pp. 676-681 ◽  
Author(s):  
Hong Mei Kai ◽  
Xiao Jie Liu ◽  
Ya Fei Liu ◽  
Lin Zhou
Keyword(s):  
Intrusion Detection ◽  
Detection System ◽  
Fuzzy Theory ◽  
False Positives ◽  
False Alarms ◽  
False Negatives ◽  
Essential Information ◽  
Malicious Activity ◽  
Response System ◽  
Number Of False Alarms

As soon as the Intrusion Detection System (IDS) detects any suspicious or malicious activity, it will generate alarms. Unfortunately, the triggered alarms usually are accompanied with huge number of false alarms (false-positives and false-negatives) which is the key performance parameters of the IDS. The risk of false-negatives is higher than false-positives. In our previous paper, we proposed a novel intelligent intrusion detection, decision, response system (I2D2RS) with fuzzy theory, which use the two essential information times and time, of the failed login to decide automatically the attacker like an experienced system/security administrator. Though the system can reduce the false alarms perfectly, the capability of processing simultaneous multi-point attack is relatively weak, and then false-negatives will be occurred. In this paper, we employ a preprocessing module to collect the failed login information before data processing. The proposed approach changes the processing procedure from serial to parallel processing, thus eliminates the false-negatives. The efficiency of these improvements was confirmed with the experiments.

scholarly journals The Eight Steps of Data Analysis: A Graphical Framework to Promote Sound Statistical Analysis

2020 ◽  
Vol 15 (4) ◽  
pp. 1054-1075
Author(s):  
Dustin Fife
Keyword(s):  
Data Analysis ◽  
Statistical Analysis ◽  
Outlier Detection ◽  
Multiple Comparisons ◽  
False Positives ◽  
Small Sample ◽  
False Negatives ◽  
Analysis Strategy ◽  
General Statistical ◽  
Small Sample Sizes

Data analysis is a risky endeavor, particularly among people who are unaware of its dangers. According to some researchers, “statistical conclusions validity” threatens all research subjected to the dark arts of statistical magic. Although traditional statistics classes may advise against certain practices (e.g., multiple comparisons, small sample sizes, violating normality), they may fail to cover others (e.g., outlier detection and violating linearity). More common, perhaps, is that researchers may fail to remember them. In this article, rather than rehashing old warnings and diatribes against this practice or that, I instead advocate a general statistical-analysis strategy. This graphic-based eight-step strategy promises to resolve the majority of statistical traps researchers may fall into—without having to remember large lists of problematic statistical practices. These steps will assist in preventing both false positives and false negatives and yield critical insights about the data that would have otherwise been missed. I conclude with an applied example that shows how the eight steps reveal interesting insights that would not be detected with standard statistical practices.

Sky Segmentation for Enhanced Depth Reconstruction and Bokeh Rendering with Efficient Architectures

2020 ◽  
Vol 2020 (14) ◽  
pp. 378-1-378-7
Author(s):  
Tyler Nuanes ◽  
Matt Elsey ◽  
Radek Grzeszczuk ◽  
John Paul Shen
Keyword(s):  
Real Time ◽  
Mobile Device ◽  
Computational Cost ◽  
False Positives ◽  
Compact Model ◽  
High Quality ◽  
False Negatives ◽  
Trade Off ◽  
Depth Reconstruction ◽  
Binary Classifiers

We present a high-quality sky segmentation model for depth refinement and investigate residual architecture performance to inform optimally shrinking the network. We describe a model that runs in near real-time on mobile device, present a new, highquality dataset, and detail a unique weighing to trade off false positives and false negatives in binary classifiers. We show how the optimizations improve bokeh rendering by correcting stereo depth misprediction in sky regions. We detail techniques used to preserve edges, reject false positives, and ensure generalization to the diversity of sky scenes. Finally, we present a compact model and compare performance of four popular residual architectures (ShuffleNet, MobileNetV2, Resnet-101, and Resnet-34-like) at constant computational cost.

Automatic Extraction of Acronyms from Text

2020 ◽  
Author(s):  
Stuart Yeates
Keyword(s):  
Digital Library ◽  
False Positives ◽  
Automatic Extraction ◽  
False Negatives ◽  
Library Research ◽  
Communications Theory ◽  
Textual Content

A brief introduction to acronyms is given and motivation for extracting them in a digital library environment is discussed. A technique for extracting acronyms is given with an analysis of the results. The technique is found to have a low number of false negatives and a high number of false positives. Introduction Digital library research seeks to build tools to enable access of content, while making as few as possible assumptions about the content, since assumptions limit the range of applicability of the tools. Generally, the broader the assumptions the more widely applicable the tools. For example, keyword based indexing [5] is based on communications theory and applies to all natural human textual languages (allowances for differences in character sets and similar localisation issues not withstanding) . The algorithm described in this paper makes much stronger assumptions about the content. It assumes textual content that contains acronyms, an assumption which is known to hold for...

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.

Sign in / Sign up

Export Citation Format

Share Document