Blog Backlinks Malicious Domain Name Detection via Supervised Learning

2021 ◽  
Vol 17 (3) ◽  
pp. 1-17
Author(s):  
Abdulrahman A. Alshdadi ◽  
Ahmed S. Alghamdi ◽  
Ali Daud ◽  
Saqib Hussain
Keyword(s):  
Machine Learning ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Svm Classifier ◽  
Financial Loss ◽  
Domain Name ◽  
Hybrid Features ◽  
Learning Techniques ◽  
Web Spam ◽  
Social Media Platforms

Web spam is the unwanted request on websites, low-quality backlinks, emails, and reviews which is generated by an automated program. It is the big threat for website owners; because of it, they can lose their top keywords ranking from search engines, which will result in huge financial loss to the business. Over the years, researchers have tried to identify malicious domains based on specific features. However, lighthouse plugin, Ahrefs tool, and social media platforms features are ignored. In this paper, the authors are focused on detection of the spam domain name from a mixture of legit and spam domain name dataset. The dataset is taken from Google webmaster tools. Machine learning models are applied on individual, distributed, and hybrid features, which significantly improved the performance of existing malicious domain machine learning techniques. Better accuracy is achieved for support vector machine (SVM) classifier, as compared to Naïve Bayes, C4.5, AdaBoost, LogitBoost.

scholarly journals Identification of bipolar disorder using a combination of multimodality magnetic resonance imaging and machine learning techniques

2020 ◽  
Author(s):  
Hao Li ◽  
Liqian Cui ◽  
Liping Cao ◽  
Yizhi Zhang ◽  
Yueheng Liu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Bipolar Disorder ◽  
Functional Mri ◽  
Added Value ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Svm Classifier ◽  
Accurate Identification ◽  
Learning Techniques ◽  
Svm Model

Abstract Background: Bipolar disorder (BPD) is a common mood disorder that is often goes misdiagnosed or undiagnosed. Recently, machine learning techniques have been combined with neuroimaging methods to aid in the diagnosis of BPD. However, most studies have focused on the construction of classifiers based on single-modality MRI. Hence, in this study, we aimed to construct a support vector machine (SVM) model using a combination of structural and functional MRI, which could be used to accurately identify patients with BPD.Methods: In total, 44 patients with BPD and 36 healthy controls were enrolled in the study. Clinical evaluation and MRI scans were performed for each subject. Next, image pre-processing, VBM and ReHo analyses were performed. The ReHo values of each subject in the clusters showing significant differences were extracted. Further, LASSO approach was recruited to screen features. Based on selected features, the SVM model was established, and discriminant analysis was performed.Results: After using the two-sample t-test with multiple comparisons, a total of 8 clusters were extracted from the data (VBM = 6; ReHo = 2). Next, we used both VBM and ReHo data to construct the new SVM classifier, which could effectively identify patients with BPD at an accuracy of 87.5% (95%CI: 72.5-95.3%), sensitivity of 86.4% (95%CI: 64.0-96.4%), and specificity of 88.9% (95%CI: 63.9-98.0%) in the test data (p=0.0022). Conclusions: A combination of structural and functional MRI can be of added value in the construction of SVM classifiers to aid in the accurate identification of BPD in the clinic.

scholarly journals A Novel Approach for Detecting DGA-Based Botnets in DNS Queries Using Machine Learning Techniques

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Ali Soleymani ◽  
Fatemeh Arabgol
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Text Mining ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Detection Accuracy ◽  
Domain Name ◽  
Botnet Detection ◽  
Learning Techniques

In today’s security landscape, advanced threats are becoming increasingly difficult to detect as the pattern of attacks expands. Classical approaches that rely heavily on static matching, such as blacklisting or regular expression patterns, may be limited in flexibility or uncertainty in detecting malicious data in system data. This is where machine learning techniques can show their value and provide new insights and higher detection rates. The behavior of botnets that use domain-flux techniques to hide command and control channels was investigated in this research. The machine learning algorithm and text mining used to analyze the network DNS protocol and identify botnets were also described. For this purpose, extracted and labeled domain name datasets containing healthy and infected DGA botnet data were used. Data preprocessing techniques based on a text-mining approach were applied to explore domain name strings with n-gram analysis and PCA. Its performance is improved by extracting statistical features by principal component analysis. The performance of the proposed model has been evaluated using different classifiers of machine learning algorithms such as decision tree, support vector machine, random forest, and logistic regression. Experimental results show that the random forest algorithm can be used effectively in botnet detection and has the best botnet detection accuracy.

Prediction of Severity of Non Proliferated Diabetic Retinopathy Using Machine Learning Techniques

2020 ◽  
Vol 17 (9) ◽  
pp. 4219-4222
Author(s):  
ManjulaSri Rayudu ◽  
Srujana Pendam ◽  
Srilaxmi Dasari
Keyword(s):  
Machine Learning ◽  
Diabetic Retinopathy ◽  
Vision Loss ◽  
Diversity Index ◽  
Research Work ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Svm Classifier ◽  
Linear Discriminant ◽  
Learning Techniques

All the patients of Type1 and more than 60% of Type2 Diabetes suffer from Diabetic Retinopathy (DR). Diabetic retinopathy causes damage to retina of eye and slowly leads to complete vision loss. The longer the patients are suffering from diabetes the probability of presence of DR is more. Hence diabetic retinopathy is to be identified in early stage to avoid blindness. The objective of this research work is to predict the severity of diabetic retinopathy (Non Proliferated) using machine learning techniques. Proliferated diabetic retinopathy (later stage) is characterized by neovasculature in the retinal veins and is the final stage. Non proliferated DR (earlier stage) is identified by any of the abnormalities out of microaneurysms, Hard exudates and hemorrhages. Then Machine learning techniques are employed to identify the class of DR. The following Classification and regression techniques are employed for categorizing the DR: Gini Diversity Index method, Linear discriminant analysis, Ensemble method with bagged and boosted trees, K-Nearest Neighbor, and Support Vector Machine classification methods. 89 images from DRIVE database (DiaRet DB1) are classified using the machine learning techniques cited above. It is observed the maximum accuracy is achieved as 88.8% with Linear SVM classifier.

scholarly journals A Comparison of Machine Learning Techniques for Sentiment Analysis

2021 ◽  
Vol 12 (3) ◽  
pp. 1738-1744
Author(s):  
Shahzad Qaiser Et.al
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Sentiment Analysis ◽  
Modern Method ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
The People ◽  
Learning Techniques ◽  
Social Media Platforms ◽  
Single Dataset

The availability of the data has increased tremendously due to the excess usage of social media platforms like Twitter and Facebook. Due to the abundant availability of data, scientists, businesses, educationalists and other people working under different roles have started using Sentiment Analysis (SA) to get in-depth knowledge about the sentiments of the people regarding any topic of interest. There are many techniques to implement SA, and one of them is Machine Learning (ML). This study is focused on the comparison of ancient ML methods such as Naïve Bayes (NB), Decision Tree (DT), Support Vector Machine (SVM), and a modern method, i.e., Deep Learning (DL). The ML techniques are applied to a single dataset to compare their performance in terms of accuracy to understand how they perform against each other. The study found that DL performed the best with 96.41% accuracy followed by NB and SVM with 87.18% and 82.05% respectively. DT performed the poorest with 68.21% accuracy.

scholarly journals Identification of bipolar disorder using a combination of multimodality magnetic resonance imaging and machine learning techniques

2020 ◽  
Author(s):  
Hao Li ◽  
Liqian Cui ◽  
Liping Cao ◽  
Yizhi Zhang ◽  
Yueheng Liu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Bipolar Disorder ◽  
Functional Mri ◽  
Added Value ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Svm Classifier ◽  
Accurate Identification ◽  
Learning Techniques ◽  
Svm Model

Abstract Background: Bipolar disorder (BPD) is a common mood disorder that is often goes misdiagnosed or undiagnosed. Recently, machine learning techniques have been combined with neuroimaging methods to aid in the diagnosis of BPD. However, most studies have focused on the construction of classifiers based on single-modality MRI. Hence, in this study, we aimed to construct a support vector machine (SVM) model using a combination of structural and functional MRI, which could be used to accurately identify patients with BPD.Methods: In total, 44 patients with BPD and 36 healthy controls were enrolled in the study. Clinical evaluation and MRI scans were performed for each subject. Next, image pre-processing, VBM and ReHo analyses were performed. The ReHo values of each subject in the clusters showing significant differences were extracted. Further, LASSO approach was recruited to screen features. Based on selected features, the SVM model was established, and discriminant analysis was performed.Results: After using the two-sample t-test with multiple comparisons, a total of 8 clusters were extracted from the data (VBM = 6; ReHo = 2). Next, we used both VBM and ReHo data to construct the new SVM classifier, which could effectively identify patients with BPD at an accuracy of 87.5% (95%CI: 72.5-95.3%), sensitivity of 86.4% (95%CI: 64.0-96.4%), and specificity of 88.9% (95%CI: 63.9-98.0%) in the test data (p=0.0022). Limitations: The sample size was small, and we were unable to eliminate the potential effects of medications. Conclusions: A combination of structural and functional MRI can be of added value in the construction of SVM classifiers to aid in the accurate identification of BPD in the clinic.

scholarly journals Identification of bipolar disorder using a combination of multimodality magnetic resonance imaging and machine learning techniques

2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Hao Li ◽  
Liqian Cui ◽  
Liping Cao ◽  
Yizhi Zhang ◽  
Yueheng Liu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Bipolar Disorder ◽  
Functional Mri ◽  
Added Value ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Svm Classifier ◽  
Accurate Identification ◽  
Learning Techniques ◽  
Svm Model

Abstract Background Bipolar disorder (BPD) is a common mood disorder that is often goes misdiagnosed or undiagnosed. Recently, machine learning techniques have been combined with neuroimaging methods to aid in the diagnosis of BPD. However, most studies have focused on the construction of classifiers based on single-modality MRI. Hence, in this study, we aimed to construct a support vector machine (SVM) model using a combination of structural and functional MRI, which could be used to accurately identify patients with BPD. Methods In total, 44 patients with BPD and 36 healthy controls were enrolled in the study. Clinical evaluation and MRI scans were performed for each subject. Next, image pre-processing, VBM and ReHo analyses were performed. The ReHo values of each subject in the clusters showing significant differences were extracted. Further, LASSO approach was recruited to screen features. Based on selected features, the SVM model was established, and discriminant analysis was performed. Results After using the two-sample t-test with multiple comparisons, a total of 8 clusters were extracted from the data (VBM = 6; ReHo = 2). Next, we used both VBM and ReHo data to construct the new SVM classifier, which could effectively identify patients with BPD at an accuracy of 87.5% (95%CI: 72.5–95.3%), sensitivity of 86.4% (95%CI: 64.0–96.4%), and specificity of 88.9% (95%CI: 63.9–98.0%) in the test data (p = 0.0022). Conclusions A combination of structural and functional MRI can be of added value in the construction of SVM classifiers to aid in the accurate identification of BPD in the clinic.

scholarly journals Electronic Credit Card Fraud Detection System by Collaboration of Machine Learning Models

2019 ◽  
Vol 8 (12S) ◽  
pp. 92-94 ◽  
Keyword(s):  
Machine Learning ◽  
Credit Card ◽  
Detection System ◽  
Industrial Sector ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Identification System ◽  
Financial Loss ◽  
Learning Techniques ◽  
Free Data

In the financial industrial sector the lightning growth and participation of internet-based transactional events give rise to malicious activities like a fraud that result in financial loss. The malicious activities have no continuous pattern their pattern, behavior, working always keep on changing with the increasing growth in technology. Every time a new technology comes in the market the hoaxer study about that technology and implement malicious activity through the learned technology and internet-based activities. The hoaxer analyzes the behavior patterns of consumers to execute the plan of fraud to cause loss to the consumer. So to overcome this problem of fraud, hoax, cheat in the financial sector a fraud identification system is needed to identify the cheating, fraud and alike activities in internet-based money transactions by employing machine learning techniques. This presented paper focuses on fraud activities that cannot be detected manually by carrying out research and examine the results of logistic regression, decision tree and support vector machine. A dataset of electronic payment card is taken from European electronic cardholders, the machine learning techniques are applied on the unstructured and process-free data.

scholarly journals Identification of bipolar disorder using a combination of multimodality magnetic resonance imaging and machine learning techniques

2020 ◽  
Author(s):  
Hao Li ◽  
Liqian Cui ◽  
Liping Cao ◽  
Yizhi Zhang ◽  
Yueheng Liu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Bipolar Disorder ◽  
Functional Mri ◽  
Added Value ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Svm Classifier ◽  
Accurate Identification ◽  
Learning Techniques ◽  
Svm Model

Abstract Background: Bipolar disorder (BPD) is a common mood disorder that is often goes misdiagnosed or undiagnosed for years. Recently, machine learning techniques have been combined with neuroimaging methods to aid in the diagnosis of BPD. However, most studies have focused on the construction of classifiers based on single-modality MRI. Hence, in this study, we aimed to construct a support vector machine (SVM) model using a combination of structural and functional MRI, which could be used to accurately identify patients with BPD.Methods: In total, 44 patients with BPD and 36 healthy controls were enrolled in the study. Clinical evaluation and MRI scans were performed for each subject. Next, image pre-processing, voxel-based morphometry (VBM), and ReHo analyses were performed. The ReHo values of each subject in the clusters showing significant differences were extracted. Further, LASSO approach was recruited to screen features. Based on selected features, the SVM model was established, and discriminant analysis was performed.Results: After using the two-sample t-test with multiple comparisons, a total of 8 clusters were extracted from the data (VBM = 6; ReHo = 2). Next, we used both VBM and ReHo data to construct the new SVM classifier, which could effectively identify patients with BPD at an accuracy of 87.5%, sensitivity of 86.4%, and specificity of 88.9% in the test data (p=0.0022).Conclusions: A combination of structural and functional MRI can be of added value in the construction of SVM classifiers to aid in the accurate identification of BPD in the clinic.

scholarly journals Machine Learning Applications based on SVM Classification A Review

2021 ◽  
Vol 1 (2) ◽  
pp. 81-90
Author(s):  
Dakhaz Mustafa Abdullah ◽  
Adnan Mohsin Abdulazeez
Keyword(s):  
Machine Learning ◽  
Face Recognition ◽  
Detection System ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Svm Classifier ◽  
Data Sets ◽  
Learning Techniques ◽  
Svm Algorithm ◽  
Machine Learning Applications

Extending technologies and data development culminated in the need for quicker and more reliable processing of massive data sets. Machine Learning techniques are used excessively. This paper, therefore, attempts to deal with data processing, using a support vector machine (SVM) algorithm in different fields since it is a reliable, efficient classification method in the area of machine learning. Accordingly, many works have been explored in this paper to cover the use of SVM classifier. Classification based on SVM has been used in many fields like face recognition, diseases diagnostics, text recognition, sentiment analysis, plant disease identification and intrusion detection system for network security application. Based on this study, it can be concluded that SVM classifier has obtained high accuracy results in most of the applications, specifically, for face recognition and diseases identification applications.

scholarly journals Identification of bipolar disorder using a combination of multimodality magnetic resonance imaging and machine learning techniques

2020 ◽  
Author(s):  
Hao Li ◽  
Liqian Cui ◽  
Liping Cao ◽  
Yizhi Zhang ◽  
Yueheng Liu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Bipolar Disorder ◽  
Functional Mri ◽  
Added Value ◽  
Classification Model ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Svm Classifier ◽  
Learning Techniques ◽  
Svm Model

Abstract Background Bipolar disorder (BPD) is a common mood disorder that is often goes misdiagnosed or undiagnosed. Recently, machine learning techniques have been combined with neuroimaging methods to aid in the diagnosis of BPD. However, most studies have focused on the construction of classifiers based on single-modality MRI. Hence, in this study, we aimed to construct a support vector machine (SVM) model using a combination of structural and functional MRI, which could be used to accurately identify patients with BPD. Methods In total, 44 patients with BPD and 36 healthy controls were enrolled in the study. Clinical evaluation and MRI scans were performed for each subject. Next, image pre-processing, voxel-based morphometry (VBM), and ReHo analyses were performed. The grey matter volumes or ReHo values of the clusters showed significant differences as discriminant features in the SVM classification model. Based on extracted features, the SVM model was established, and discriminant analysis was performed. Results After using the two-sample t-test with multiple comparisons, 12 clusters with significant differences were extracted from the data. Next, we used both VBM and ReHo data to construct the new SVM classifier, which could effectively identify patients with BPD at an accuracy of 90% in the test data (p=0.0014). Limitations The sample size was small, and we were unable to eliminate the potential effects of medications. Conclusions A combination of structural and functional MRI can be of added value in the construction of SVM classifiers to aid in the accurate identification of BPD in the clinic.

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