scholarly journals Detecting Multielement Algorithmically Generated Domain Names Based on Adaptive Embedding Model

2021 ◽  
Vol 2021 ◽  
pp. 1-20
Author(s):  
Luhui Yang ◽  
Guangjie Liu ◽  
Weiwei Liu ◽  
Huiwen Bai ◽  
Jiangtao Zhai ◽  
...  
Keyword(s):  
Detection Accuracy ◽  
Domain Name ◽  
Domain Names ◽  
Detection Algorithms ◽  
Proposed Model ◽  
Adaptive Embedding ◽  
Series Of Experiments ◽  
Dynamic Loss ◽  
Comparison Algorithms ◽  
Dynamic Domain

With the development of detection algorithms on malicious dynamic domain names, domain generation algorithms have developed to be more stealthy. The use of multiple elements for generating domains will lead to higher detection difficulty. To effectively improve the detection accuracy of algorithmically generated domain names based on multiple elements, a domain name syntax model is proposed, which analyzes the multiple elements in domain names and their syntactic relationship, and an adaptive embedding method is proposed to achieve effective element parsing of domain names. A parallel convolutional model based on the feature selection module combined with an improved dynamic loss function based on curriculum learning is proposed, which can achieve effective detection on multielement malicious domain names. A series of experiments are designed and the proposed model is compared with five previous algorithms. The experimental results denote that the detection accuracy of the proposed model for multiple-element malicious domain names is significantly higher than that of the comparison algorithms and also has good adaptability to other types of malicious domain names.

scholarly journals Detection of Micro-Defects on Irregular Reflective Surfaces Based on Improved Faster R-CNN

Sensors ◽  
2019 ◽  
Vol 19 (22) ◽  
pp. 5000 ◽  
Author(s):  
Zhuangzhuang Zhou ◽  
Qinghua Lu ◽  
Zhifeng Wang ◽  
Haojie Huang
Keyword(s):  
Specular Reflection ◽  
Receptive Fields ◽  
Ground Truth ◽  
Detection Performance ◽  
Support Vector ◽  
Detection Accuracy ◽  
Irregular Surfaces ◽  
Detection Algorithms ◽  
Proposed Model ◽  
Improved Model

The detection of defects on irregular surfaces with specular reflection characteristics is an important part of the production process of sanitary equipment. Currently, defect detection algorithms for most irregular surfaces rely on the handcrafted extraction of shallow features, and the ability to recognize these defects is limited. To improve the detection accuracy of micro-defects on irregular surfaces in an industrial environment, we propose an improved Faster R-CNN model. Considering the variety of defect shapes and sizes, we selected the K-Means algorithm to generate the aspect ratio of the anchor box according to the size of the ground truth, and the feature matrices are fused with different receptive fields to improve the detection performance of the model. The experimental results show that the recognition accuracy of the improved model is 94.6% on a collected ceramic dataset. Compared with SVM (Support Vector Machine) and other deep learning-based models, the proposed model has better detection performance and robustness to illumination, which proves the practicability and effectiveness of the proposed method.

scholarly journals Improved Detection Criteria for Detecting Drug-Drug Interaction Signals Using the Proportional Reporting Ratio

2020 ◽  
Vol 14 (1) ◽  
pp. 4
Author(s):  
Yoshihiro Noguchi ◽  
Keisuke Aoyama ◽  
Satoaki Kubo ◽  
Tomoya Tachi ◽  
Hitomi Teramachi
Keyword(s):  
Drug Interaction ◽  
Confidence Interval ◽  
Drug Interactions ◽  
Signal Detection ◽  
Proportional Reporting Ratio ◽  
Detection Accuracy ◽  
Detection Algorithms ◽  
Proposed Model ◽  
Signal Score ◽  
Drug Drug Interaction

There is a current demand for “safety signal” screening, not only for single drugs but also for drug-drug interactions. The detection of drug-drug interaction signals using the proportional reporting ratio (PRR) has been reported, such as through using the combination risk ratio (CRR). However, the CRR does not consider the overlap between the lower limit of the 95% confidence interval of the PRR of concomitant-use drugs and the upper limit of the 95% confidence interval of the PRR of single drugs. In this study, we proposed the concomitant signal score (CSS), with the improved detection criteria, to overcome the issues associated with the CRR. “Hypothetical” true data were generated through a combination of signals detected using three detection algorithms. The signal detection accuracy of the analytical model under investigation was verified using machine learning indicators. The CSS presented improved signal detection when the number of reports was ≥3, with respect to the following metrics: accuracy (CRR: 0.752 → CSS: 0.817), Youden’s index (CRR: 0.555 → CSS: 0.661), and F-measure (CRR: 0.780 → CSS: 0.820). The proposed model significantly improved the accuracy of signal detection for drug-drug interactions using the PRR.

scholarly journals Multi-Scale Feature Pyramid Network: A Heavily Occluded Pedestrian Detection Network Based on ResNet

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1820
Author(s):  
Xiaotao Shao ◽  
Qing Wang ◽  
Wei Yang ◽  
Yun Chen ◽  
Yi Xie ◽  
...  
Keyword(s):  
Semantic Information ◽  
Detection System ◽  
Pedestrian Detection ◽  
Detection Accuracy ◽  
The Public ◽  
Scale Feature ◽  
Detection Algorithms ◽  
Multi Scale ◽  
Art Works ◽  
Feature Pyramid

The existing pedestrian detection algorithms cannot effectively extract features of heavily occluded targets which results in lower detection accuracy. To solve the heavy occlusion in crowds, we propose a multi-scale feature pyramid network based on ResNet (MFPN) to enhance the features of occluded targets and improve the detection accuracy. MFPN includes two modules, namely double feature pyramid network (FPN) integrated with ResNet (DFR) and repulsion loss of minimum (RLM). We propose the double FPN which improves the architecture to further enhance the semantic information and contours of occluded pedestrians, and provide a new way for feature extraction of occluded targets. The features extracted by our network can be more separated and clearer, especially those heavily occluded pedestrians. Repulsion loss is introduced to improve the loss function which can keep predicted boxes away from the ground truths of the unrelated targets. Experiments carried out on the public CrowdHuman dataset, we obtain 90.96% AP which yields the best performance, 5.16% AP gains compared to the FPN-ResNet50 baseline. Compared with the state-of-the-art works, the performance of the pedestrian detection system has been boosted with our method.

An Opcode-Based Malware Detection Model Using Supervised Learning Algorithms

2021 ◽  
Vol 15 (4) ◽  
pp. 18-30
Author(s):  
Om Prakash Samantray ◽  
Satya Narayan Tripathy
Keyword(s):  
Supervised Learning ◽  
Learning Algorithms ◽  
Malware Detection ◽  
Support Vector ◽  
Detection Accuracy ◽  
Detection Techniques ◽  
Detection Model ◽  
Proposed Model ◽  
Tree Classifier ◽  
Supervised Learning Algorithms

There are several malware detection techniques available that are based on a signature-based approach. This approach can detect known malware very effectively but sometimes may fail to detect unknown or zero-day attacks. In this article, the authors have proposed a malware detection model that uses operation codes of malicious and benign executables as the feature. The proposed model uses opcode extract and count (OPEC) algorithm to prepare the opcode feature vector for the experiment. Most relevant features are selected using extra tree classifier feature selection technique and then passed through several supervised learning algorithms like support vector machine, naive bayes, decision tree, random forest, logistic regression, and k-nearest neighbour to build classification models for malware detection. The proposed model has achieved a detection accuracy of 98.7%, which makes this model better than many of the similar works discussed in the literature.

scholarly journals Hot Anchors: A Heuristic Anchors Sampling Method in RCNN-Based Object Detection

Sensors ◽  
2018 ◽  
Vol 18 (10) ◽  
pp. 3415 ◽  
Author(s):  
Jinpeng Zhang ◽  
Jinming Zhang ◽  
Shan Yu
Keyword(s):  
Object Detection ◽  
Sampling Method ◽  
Ground Truth ◽  
Detection Accuracy ◽  
Two Stage ◽  
Baseline Model ◽  
Detection Algorithms ◽  
Imbalance Problem ◽  
Image Object Detection ◽  
Image Object

In the image object detection task, a huge number of candidate boxes are generated to match with a relatively very small amount of ground-truth boxes, and through this method the learning samples can be created. But in fact the vast majority of the candidate boxes do not contain valid object instances and should be recognized and rejected during the training and evaluation of the network. This leads to extra high computation burden and a serious imbalance problem between object and none-object samples, thereby impeding the algorithm’s performance. Here we propose a new heuristic sampling method to generate candidate boxes for two-stage detection algorithms. It is generally applicable to the current two-stage detection algorithms to improve their detection performance. Experiments on COCO dataset showed that, relative to the baseline model, this new method could significantly increase the detection accuracy and efficiency.

scholarly journals Deep Learning Assisted Covid-19 Detection using full CT-scans

2020 ◽  
Author(s):  
varan singhrohila ◽  
Nitin Gupta ◽  
Amit Kaul ◽  
Deepak Sharma
Keyword(s):  
Deep Learning ◽  
Ct Scans ◽  
Detection Accuracy ◽  
Residual Network ◽  
Excellent Performance ◽  
Examination Procedure ◽  
Medical Institutions ◽  
Proposed Model ◽  
Learning Technique ◽  
Intensive Approach

<div>The ongoing pandemic of COVID-19 has shown</div><div>the limitations of our current medical institutions. There</div><div>is a need for research in the field of automated diagnosis</div><div>for speeding up the process while maintaining accuracy</div><div>and reducing computational requirements. In this work, an</div><div>automatic diagnosis of COVID-19 infection from CT scans</div><div>of the patients using Deep Learning technique is proposed.</div><div>The proposed model, ReCOV-101 uses full chest CT scans to</div><div>detect varying degrees of COVID-19 infection, and requires</div><div>less computational power. Moreover, in order to improve</div><div>the detection accuracy the CT-scans were preprocessed by</div><div>employing segmentation and interpolation. The proposed</div><div>scheme is based on the residual network, taking advantage</div><div>of skip connection, allowing the model to go deeper.</div><div>Moreover, the model was trained on a single enterpriselevel</div><div>GPU such that it can easily be provided on the edge of</div><div>the network, reducing communication with the cloud often</div><div>required for processing the data. The objective of this work</div><div>is to demonstrate a less hardware-intensive approach for COVID-19 detection with excellent performance that can</div><div>be combined with medical equipment and help ease the</div><div>examination procedure. Moreover, with the proposed model</div><div>an accuracy of 94.9% was achieved.</div>

An anisotropic heat diffusion model for enhancing the extraction of underground rail track fasteners under extremely low and uneven illumination conditions

2018 ◽  
Vol 233 (4) ◽  
pp. 410-419 ◽  
Author(s):  
Chengbo Ai ◽  
Shi Qiu ◽  
Guiyang Xu
Keyword(s):  
State Of The Art ◽  
Heat Diffusion ◽  
Successful Implementation ◽  
Artificial Lighting ◽  
Rail Track ◽  
Detection Algorithms ◽  
Proposed Model ◽  
Critical Components ◽  
Local Illumination ◽  
Adaptation Model

During the past two decades, subway systems have become one of the most dominant infrastructural developments in China at an unprecedented pace and scale. More than 60 metro lines in 25 cities have been completed, transporting more than 70 million passengers daily. Operating the subway systems safely and efficiently is a continuously pressing demand from both the management companies and the public. Although many automated or semi-automated methods for extracting critical components of the rail track systems, e.g. rail, fastener, sleeper, etc., have significantly improved the productivity of routine inspection, the unique challenges posed by the subway systems have hindered these existing methods from successful implementation because of the extremely low illumination in the underground environment, whereas additional artificial lighting often poses extremely uneven illumination. In this study, a generalized local illumination adaptation model using an anisotropic heat equation is proposed to dynamically adjust the acquired rail track images with extremely low and uneven illumination conditions. An integration flow is then proposed to seamlessly incorporate the proposed model into the state-of-the-art automated fastener detection algorithms. The results show that the proposed local illumination adaptation model can significantly improve the performance of the tested state-of-the-art fastener detection algorithms when they are applied to the images collected in the environment with extremely low and uneven illumination conditions, e.g. subway systems.

Heuristic ensemble for unsupervised detection of multiple types of concept drift in data stream classification

2021 ◽  
pp. 1-14
Author(s):  
Hanqing Hu ◽  
Mehmed Kantardzic
Keyword(s):  
Data Stream ◽  
Concept Drift ◽  
False Alarms ◽  
Detection Accuracy ◽  
Real World Data ◽  
Traditional Concept ◽  
Stream Classification ◽  
Data Stream Classification ◽  
Detection Algorithms ◽  
Concept Drift Detection

Real-world data stream classification often deals with multiple types of concept drift, categorized by change characteristics such as speed, distribution, and severity. When labels are unavailable, traditional concept drift detection algorithms, used in stream classification frameworks, are often focused on only one type of concept drift. To overcome the limitations of traditional detection algorithms, this study proposed a Heuristic Ensemble Framework for Drift Detection (HEFDD). HEFDD aims to detect all types of concept drift by employing an ensemble of selected concept drift detection algorithms, each capable of detecting at least one type of concept drift. Experimental results show HEFDD provides significant improvement based on the z-score test when comparing detection accuracy with state-of-the-art individual algorithms. At the same time, HEFDD is able to reduce false alarms generated by individual concept drift detection algorithms.

Improving Recommendation Accuracy and Diversity via Multiple Social Factors and Social Circles

2019 ◽  
pp. 132-154
Author(s):  
Yong Feng ◽  
Heng Li ◽  
Zhuo Chen ◽  
Baohua Qiang
Keyword(s):  
Social Factors ◽  
Online Social Networks ◽  
Online Information ◽  
Information Discovery ◽  
Proposed Model ◽  
Probabilistic Matrix Factorization ◽  
Series Of Experiments ◽  
Networks Analysis ◽  
Recommendation Accuracy ◽  
Improved Performance

Recommender systems have been widely employed to suggest personalized online information to simplify users' information discovery process. With the popularity of online social networks, analysis and mining of social factors and social circles have been utilized to support more effective recommendations, but have not been fully investigated. In this chapter, the authors propose a novel recommendation model with the consideration of more comprehensive social factors and topics. To further enhance recommendation accuracy, four social factors are simultaneously injected into the recommendation model based on probabilistic matrix factorization. Meanwhile, the authors explore several new methods to measure these social factors. Moreover, they infer explicit and implicit social circles to enhance the performance of recommendation diversity. Finally, the authors conduct a series of experiments on publicly available data. Experimental results show the proposed model achieves significantly improved performance over the existing models in which social information have not been fully considered.

Structure and Organization of the Domain Name System

2015 ◽  
Author(s):  
Torsten Bettinger
Keyword(s):  
Host Computer ◽  
The Internet ◽  
Domain Name System ◽  
Domain Name ◽  
Global Regulation ◽  
Ip Address ◽  
Domain Names ◽  
Data Packets ◽  
System P ◽  
Administrative Tasks

Although the Internet has no cross-organizational, financial, or operational management responsible for the entire Internet, certain administrative tasks are coordinated centrally. Among the most important organizational tasks that require global regulation is the management of Internet Protocol (IP) addresses and their corresponding domain names. The IP address consists of an existing 32 bit (IP4) or 128 bit (IP6) sequence of digits and is the actual physical network address by which routing on the Internet takes place and which will ensure that the data packets reach the correct host computer.

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