A Comparative Evaluation of Ensemble Classifiers for Malicious Webpage Detection

Subaşı, Abdülhamit; Balfaqih, Mohammed; Balfagih, Zain; Alfawwaz, Khaled

doi:10.1016/j.procs.2021.10.082

Cited by 19 publications

(27 citation statements)

References 13 publications

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“…The decision tree (DT), which is a machine learning algorithm, is performed for regression or classification [ 40 , 41 ]. Ross Quinlan created the program C4.5 over 20 years ago [ 42 ]. If any decision tree is implemented as a classification algorithm, it will possess a hierarchical structure.…”

Section: Methodsmentioning

confidence: 99%

“…The k-Nearest Neighbors (kNN) algorithm is a supervised learning algorithm, and kNN is utilized for classification problems [ 45 ]. The state of nearest is calculated using a distance metric such as a Euclidean distance [ 42 ]. Notably, the essential component of the nearest neighbor algorithm assigns an input sample vector

, which is not a known classification, to its nearest neighbor class [ 46 , 47 ].…”

Section: Methodsmentioning

confidence: 99%

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A Deep Learning Approach for Detecting Stroke from Brain CT Images Using OzNet

Özaltın

Coskun²,

Yeniay

et al. 2022

Bioengineering

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A brain stroke is a life-threatening medical disorder caused by the inadequate blood supply to the brain. After the stroke, the damaged area of the brain will not operate normally. As a result, early detection is crucial for more effective therapy. Computed tomography (CT) images supply a rapid diagnosis of brain stroke. However, while doctors are analyzing each brain CT image, time is running fast. This circumstance may lead to result in a delay in treatment and making errors. Therefore, we targeted the utilization of an efficient artificial intelligence algorithm in stroke detection. In this paper, we designed hybrid algorithms that include a new convolution neural networks (CNN) architecture called OzNet and various machine learning algorithms for binary classification of real brain stroke CT images. When we classified the dataset with OzNet, we acquired successful performance. However, for this target, we combined it with a minimum Redundancy Maximum Relevance (mRMR) method and Decision Tree (DT), k-Nearest Neighbors (kNN), Linear Discriminant Analysis (LDA), Naïve Bayes (NB), and Support Vector Machines (SVM). In addition, 4096 significant features were obtained from the fully connected layer of OzNet, and we reduced the dimension of features from 4096 to 250 using the mRMR method. Finally, we utilized these machine learning algorithms to classify important features. As a result, OzNet-mRMR-NB was an excellent hybrid algorithm and achieved an accuracy of 98.42% and AUC of 0.99 to detect stroke from brain CT images.

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Section: Methodsmentioning

confidence: 99%

, which is not a known classification, to its nearest neighbor class [ 46 , 47 ].…”

Section: Methodsmentioning

confidence: 99%

A Deep Learning Approach for Detecting Stroke from Brain CT Images Using OzNet

Özaltın

Coskun²,

Yeniay

et al. 2022

Bioengineering

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“…For many decades, malicious URL detection has been a major concern for cybersecurity specialists [8][9][10][11][12][13][14]. Several solutions have been proposed to detect malicious URLs and protect users from being victims of an attack.…”

Section: Related Workmentioning

confidence: 99%

“…The problem with detecting malicious websites has been around since early 2004 [8][9][10][11][12][13][14][15][16][17][18]. Many solutions have been proposed to accurately detect these websites.…”

Section: Introductionmentioning

confidence: 99%

“…Many solutions have been proposed to accurately detect these websites. These solutions can be divided into three categories by their source of investigation: URL-based [8][9][10][11][12][13][14][15][16], web content-based [19][20][21], and script-based [17,18]. URL-based detection is the most investigated approach followed by content-based detection, while little research has been investigated on script-based detection.…”

Section: Introductionmentioning

confidence: 99%

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Cyber Threat Intelligence-Based Malicious URL Detection Model Using Ensemble Learning

Ghaleb

Alsaedi

Saeed

et al. 2022

Sensors

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Web applications have become ubiquitous for many business sectors due to their platform independence and low operation cost. Billions of users are visiting these applications to accomplish their daily tasks. However, many of these applications are either vulnerable to web defacement attacks or created and managed by hackers such as fraudulent and phishing websites. Detecting malicious websites is essential to prevent the spreading of malware and protect end-users from being victims. However, most existing solutions rely on extracting features from the website’s content which can be harmful to the detection machines themselves and subject to obfuscations. Detecting malicious Uniform Resource Locators (URLs) is safer and more efficient than content analysis. However, the detection of malicious URLs is still not well addressed due to insufficient features and inaccurate classification. This study aims at improving the detection accuracy of malicious URL detection by designing and developing a cyber threat intelligence-based malicious URL detection model using two-stage ensemble learning. The cyber threat intelligence-based features are extracted from web searches to improve detection accuracy. Cybersecurity analysts and users reports around the globe can provide important information regarding malicious websites. Therefore, cyber threat intelligence-based (CTI) features extracted from Google searches and Whois websites are used to improve detection performance. The study also proposed a two-stage ensemble learning model that combines the random forest (RF) algorithm for preclassification with multilayer perceptron (MLP) for final decision making. The trained MLP classifier has replaced the majority voting scheme of the three trained random forest classifiers for decision making. The probabilistic output of the weak classifiers of the random forest was aggregated and used as input for the MLP classifier for adequate classification. Results show that the extracted CTI-based features with the two-stage classification outperform other studies’ detection models. The proposed CTI-based detection model achieved a 7.8% accuracy improvement and 6.7% reduction in false-positive rates compared with the traditional URL-based model.

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Classification of brain hemorrhage computed tomography images using OzNet hybrid algorithm

Özaltın

Coskun²,

Yeniay

et al. 2022

Int J Imaging Syst Tech

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Classification of brain hemorrhage computed tomography (CT) images provides a better diagnostic implementation for emergency patients. Attentively, each brain CT image must be examined by doctors. This situation is time-consuming, exhausting, and sometimes leads to making errors. Hence, we aim to find the best algorithm owing to a requirement for automatic classification of CT images to detect brain hemorrhage. In this study, we developed OzNet hybrid algorithm, which is a novel convolution neural networks (CNN) algorithm. Although OzNet achieves high classification performance, we combine it with Neighborhood Component Analysis (NCA) and many classifiers: Artificial neural networks (ANN), Adaboost, Bagging, Decision Tree, K-Nearest Neighbor (K-NN), Linear Discriminant Analysis (LDA), Naïve Bayes and Support Vector Machines (SVM). In addition, Oznet is utilized for feature extraction, where 4096 features are extracted from the fully connected layer. These features are reduced to have significant and informative features with minimum loss by NCA. Eventually, we use these classifiers to classify these significant features. Finally, experimental results display that OzNet-NCA-ANN excellent classifier model and achieves 100% accuracy with created Dataset 2 from Brain Hemorrhage CT images.

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A Comparative Evaluation of Ensemble Classifiers for Malicious Webpage Detection

Cited by 19 publications

References 13 publications

A Deep Learning Approach for Detecting Stroke from Brain CT Images Using OzNet

A Deep Learning Approach for Detecting Stroke from Brain CT Images Using OzNet

Cyber Threat Intelligence-Based Malicious URL Detection Model Using Ensemble Learning

Classification of brain hemorrhage computed tomography images using OzNet hybrid algorithm

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