scholarly journals Door Detection Based on Geometrical Features and Harris Corner

2021 ◽  
Vol 12 (3) ◽  
pp. 3250-3257
Author(s):  
Md. Akber Hossain Et.al
Keyword(s):  
Detection Method ◽  
False Positive Rate ◽  
Experimental Results ◽  
Computational Time ◽  
Visually Impaired People ◽  
Lower False Positive Rate ◽  
Harris Corner ◽  
Positive Rate ◽  
Edge Detection Method ◽  
Prewitt Edge Detection

Door is a very significant element as it enables a person to enter a house or room. Though identifying doorway is an easy task for a regular person, for robots or visually impaired people it is a challenging task. To overcome this challenge, we have proposed a door detection method. Our proposed method is based on Prewitt edge detection method and Harris corner detector. Here, we are using a number of predefined rules to detect the doorframe correctly.  To establish the robustness of our proposed method, we have formed a substantial dataset of scene images that are captured in various unfamiliar environments. Our experimental results validate that our proposed method is robust against changes in viewpoint, shapes, occlusions, illumination, colors, sizes, orientations, and textures of the door. The experimental results show that our proposed method reaches 87.45% accuracy as well as achieves lower false positive rate and lower computational time.

scholarly journals PRATD: A Phased Remote Access Trojan Detection Method with Double-Sided Features

Electronics ◽  
2020 ◽  
Vol 9 (11) ◽  
pp. 1894
Author(s):  
Chun Guo ◽  
Zihua Song ◽  
Yuan Ping ◽  
Guowei Shen ◽  
Yuhei Cui ◽  
...  
Keyword(s):  
False Positive ◽  
Detection Method ◽  
False Positive Rate ◽  
True Positive Rate ◽  
Remote Access ◽  
Detection Methods ◽  
Security Threats ◽  
True Positive ◽  
Trojan Detection ◽  
Positive Rate

Remote Access Trojan (RAT) is one of the most terrible security threats that organizations face today. At present, two major RAT detection methods are host-based and network-based detection methods. To complement one another’s strengths, this article proposes a phased RATs detection method by combining double-side features (PRATD). In PRATD, both host-side and network-side features are combined to build detection models, which is conducive to distinguishing the RATs from benign programs because that the RATs not only generate traffic on the network but also leave traces on the host at run time. Besides, PRATD trains two different detection models for the two runtime states of RATs for improving the True Positive Rate (TPR). The experiments on the network and host records collected from five kinds of benign programs and 20 famous RATs show that PRATD can effectively detect RATs, it can achieve a TPR as high as 93.609% with a False Positive Rate (FPR) as low as 0.407% for the known RATs, a TPR 81.928% and FPR 0.185% for the unknown RATs, which suggests it is a competitive candidate for RAT detection.

scholarly journals RoughDroid: Operative Scheme for Functional Android Malware Detection

2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Khaled Riad ◽  
Lishan Ke
Keyword(s):  
False Positive Rate ◽  
Experimental Results ◽  
Android Application ◽  
Android Malware ◽  
Feature Sets ◽  
Android Malware Detection ◽  
Analysis Technique ◽  
Positive Rate ◽  
Pass Through ◽  
Google Play

There are thousands of malicious applications that invade Google Play Store every day and seem to be legal applications. These malicious applications have the ability to link the malware referred to as Dresscode created for network hacking as well as scrolling information. Since Android smartphones are indispensable, there should be an efficient and also unusual protection. Therefore, Android smartphones usually continue to be safeguarded from novel malware. In this paper, we propose RoughDroid, a floppy analysis technique that can discover Android malware applications directly on the smartphone. RoughDroid is based on seven feature sets (FS1,FS2,…,FS7) from the XML manifest file of an Android application, plus three feature sets (FS8,FS9, and FS10) from the Dex file. Those feature sets pass through the Rough Set algorithm to elastically classify the Android application as either benign or malicious. The experimental results mainly consider 20 most common malware families, plus three new malware families (Grabos, TrojanDropper.Agent.BKY, and AsiaHitGroup) that invade Google Play Store at 2017. According to the experimental results, RoughDroid has 95.6% detection performance for the malware families at 1% false-positive rate. Finally, RoughDroid is a lightweight approach for straightly examining downloaded applications on the smartphone.

scholarly journals Symmetric-Key Cryptographic Routine Detection in Anti-Reverse Engineered Binaries Using Hardware Tracing

Electronics ◽  
2020 ◽  
Vol 9 (6) ◽  
pp. 957 ◽  
Author(s):  
Juhyun Park ◽  
Yongsu Park
Keyword(s):  
False Positive ◽  
False Positive Rate ◽  
Experimental Results ◽  
Software Implementation ◽  
Detection Scheme ◽  
Prototype Implementation ◽  
Cryptographic Algorithms ◽  
Positive Rate ◽  
Symmetric Key

Software uses cryptography to provide confidentiality in communication and to provide authentication. Additionally, cryptographic algorithms can be used to protect software against cracking core algorithms in software implementation. Recently, malware and ransomware have begun to use encryption to protect their codes from analysis. As for the detection of cryptographic algorithms, previous works have had demerits in analyzing anti-reverse engineered binaries that can detect differences in analysis environments and normal execution. Here, we present a new symmetric-key cryptographic routine detection scheme using hardware tracing. In our experiments, patterns were successfully generated and detected for nine symmetric-key cryptographic algorithms. Additionally, the experimental results show that the false positive rate of our scheme is extremely low and the prototype implementation successfully bypasses anti-reversing techniques. Our work can be used to detect symmetric-key cryptographic routines in malware/ransomware with anti-reversing techniques.

scholarly journals Implementation of maternal blood cell-free DNA testing in early screening for aneuploidies

2019 ◽  
Vol 31 (3) ◽  
pp. 283-288
Author(s):  
María M. Gil ◽  
Kypros H. Nicolaides
Keyword(s):  
False Positive Rate ◽  
Maternal Blood ◽  
Screening Methods ◽  
Routine Practice ◽  
Clinical Implementation ◽  
Early Screening ◽  
Test Models ◽  
Lower False Positive Rate ◽  
Implementation Studies ◽  
Positive Rate

AbstractSeveral externally blinded validation and implementation studies in the last 9 years have shown that it is now possible, through analysis of cell-free (cf) DNA in maternal blood, to effectively detect a high proportion of fetuses affected by trisomies 21, 18, and 13 at a much lower false-positive rate (FPR) than all other existing screening methods. This article is aimed at reviewing technical and clinical considerations for implementing cfDNA testing in routine practice, including methods of analysis, performance of the test, models for clinical implementation, and interpretation of results.

scholarly journals Gene set enrichment for reproducible science: comparison of CERNO and eight other algorithms

2019 ◽  
Vol 35 (24) ◽  
pp. 5146-5154 ◽  
Author(s):  
Joanna Zyla ◽  
Michal Marczyk ◽  
Teresa Domaszewska ◽  
Stefan H E Kaufmann ◽  
Joanna Polanska ◽  
...  
Keyword(s):  
False Positive Rate ◽  
R Package ◽  
Supplementary Information ◽  
Computational Time ◽  
P Value ◽  
Gene Set ◽  
Related Data ◽  
Novel Approach ◽  
Positive Rate ◽  
Real World Datasets

Abstract Motivation Analysis of gene set (GS) enrichment is an essential part of functional omics studies. Here, we complement the established evaluation metrics of GS enrichment algorithms with a novel approach to assess the practical reproducibility of scientific results obtained from GS enrichment tests when applied to related data from different studies. Results We evaluated eight established and one novel algorithm for reproducibility, sensitivity, prioritization, false positive rate and computational time. In addition to eight established algorithms, we also included Coincident Extreme Ranks in Numerical Observations (CERNO), a flexible and fast algorithm based on modified Fisher P-value integration. Using real-world datasets, we demonstrate that CERNO is robust to ranking metrics, as well as sample and GS size. CERNO had the highest reproducibility while remaining sensitive, specific and fast. In the overall ranking Pathway Analysis with Down-weighting of Overlapping Genes, CERNO and over-representation analysis performed best, while CERNO and GeneSetTest scored high in terms of reproducibility. Availability and implementation tmod package implementing the CERNO algorithm is available from CRAN (cran.r-project.org/web/packages/tmod/index.html) and an online implementation can be found at http://tmod.online/. The datasets analyzed in this study are widely available in the KEGGdzPathwaysGEO, KEGGandMetacoreDzPathwaysGEO R package and GEO repository. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals A novel Ensemble of Hybrid Intrusion Detection System for Detecting Internet of Things Attacks

Electronics ◽  
2019 ◽  
Vol 8 (11) ◽  
pp. 1210 ◽  
Author(s):  
Khraisat ◽  
Gondal ◽  
Vamplew ◽  
Kamruzzaman ◽  
Alazab
Keyword(s):  
Internet Of Things ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection System ◽  
False Positive Rate ◽  
Support Vector ◽  
Detection Accuracy ◽  
Lower False Positive Rate ◽  
Positive Rate ◽  
Iot Devices

The Internet of Things (IoT) has been rapidly evolving towards making a greater impact on everyday life to large industrial systems. Unfortunately, this has attracted the attention of cybercriminals who made IoT a target of malicious activities, opening the door to a possible attack to the end nodes. Due to the large number and diverse types of IoT devices, it is a challenging task to protect the IoT infrastructure using a traditional intrusion detection system. To protect IoT devices, a novel ensemble Hybrid Intrusion Detection System (HIDS) is proposed by combining a C5 classifier and One Class Support Vector Machine classifier. HIDS combines the advantages of Signature Intrusion Detection System (SIDS) and Anomaly-based Intrusion Detection System (AIDS). The aim of this framework is to detect both the well-known intrusions and zero-day attacks with high detection accuracy and low false-alarm rates. The proposed HIDS is evaluated using the Bot-IoT dataset, which includes legitimate IoT network traffic and several types of attacks. Experiments show that the proposed hybrid IDS provide higher detection rate and lower false positive rate compared to the SIDS and AIDS techniques.

A Novel Hybrid LE and SVM with CV in Intrusion Detection

2014 ◽  
Vol 644-650 ◽  
pp. 2572-2576
Author(s):  
Qing Liu ◽  
Yun Kai Zhang ◽  
Qing Ru Li
Keyword(s):  
Intrusion Detection ◽  
False Positive Rate ◽  
Likelihood Estimation ◽  
Support Vector ◽  
Training Time ◽  
Detection Algorithms ◽  
Lower False Positive Rate ◽  
Proposed Model ◽  
Positive Rate ◽  
Rbf Kernel

A support vector machine (SVM) model combined Laplacian Eigenmaps (LE) with Cross Validation (CV) is proposed for intrusion detection. In the proposed model, a classifier is adopted to estimate whether an action is an attack or not. Maximum Likelihood Estimation (MLE) is used to estimate the intrinsic dimensions, and LE is used as a preprocessor of SVM to reduce the dimensions of feature vectors then training time is shortened. In order to improve the performance of SVM, CV is used to optimize the parameters of SVM in RBF kernel function. Compared with other detection algorithms, the experimental results show that the proposed model has the advantages: shorter training time, higher accuracy rate and lower false positive rate.

scholarly journals Detection of de novo copy number deletions from targeted sequencing of trios

2018 ◽  
Author(s):  
Jack M. Fu ◽  
Elizabeth J. Leslie ◽  
Alan F. Scott ◽  
Jeffrey C. Murray ◽  
Mary L. Marazita ◽  
...  
Keyword(s):  
Open Source Software ◽  
Copy Number ◽  
Minimum Distance ◽  
Formal Method ◽  
De Novo ◽  
False Positive Rate ◽  
Targeted Sequencing ◽  
Lower False Positive Rate ◽  
Positive Rate ◽  
Sequencing Platforms

AbstractDe novo copy number deletions have been implicated in many diseases, but there is no formal method to date however that identifies de novo deletions in parent-offspring trios from capture-based sequencing platforms. We developed Minimum Distance for Targeted Sequencing (MDTS) to fill this void. MDTS has similar sensitivity (recall), but a much lower false positive rate compared to less specific CNV callers, resulting in a much higher positive predictive value (precision). MDTS also exhibited much better scalability, and is available as open source software at github.com/JMF47/MDTS.

scholarly journals Outlier Detection Method for Flash Flood Disaster Monitoring Data based on Information Entropy

2021 ◽  
Vol 2138 (1) ◽  
pp. 012013
Author(s):  
Yongzhi Chen ◽  
Ziao Xu ◽  
Chaoqun Niu
Keyword(s):  
Outlier Detection ◽  
Information Entropy ◽  
Detection Method ◽  
Flash Flood ◽  
False Positive Rate ◽  
Flood Disaster ◽  
Detection Methods ◽  
Positive Rate ◽  
Disaster Monitoring ◽  
Local Outlier

Abstract In the research of flash flood disaster monitoring and early warning, the Internet of Things is widely used in real-time information collection. There are abnormal situations such as noise, repetition and errors in a large amount of data collected by sensors, which will lead to false alarm, lower prediction accuracy and other problems. Aiming at the characteristic that outliers flow of sensors will cause obvious fluctuation of information entropy, this paper proposes a local outlier detection method based on information entropy and optimized by sliding window and LOF (Local Outlier Factor). This method can be used to improve the data quality, thus improving the accuracy of disaster prediction. The method is applied to data stream processing of water sensor, and the experimental results show that the method can accurately detect outliers. Compared with the existing detection methods that only use data distance to determine, the test positive rate is improved and the false positive rate is reduced.

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