Detecting malicious URLs using binary classification through adaboost algorithm

Khan, Firoz; Ahamed, Jinesh; Kadry, Seifedine; Ramasamy, Lakshmana Kumar

doi:10.11591/ijece.v10i1.pp997-1005

Cited by 30 publications

(17 citation statements)

References 14 publications

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“…Similar to these studies, there are many other studies such as in ( Guz et al, 2010 , Jabri et al, 2018 , Khan et al, 2020 ). All these studies aim to show the power of AdaBoost algorithm, especially in binary classification problems.…”

Section: Related Worksupporting

confidence: 73%

An empowered AdaBoost algorithm implementation: A COVID-19 dataset study

Sevinç

2022

Computers & Industrial Engineering

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The Covid-19 outbreak, which emerged in 2020, became the top priority of the world. The fight against this disease, which has caused millions of people’s deaths, is still ongoing, and it is expected that these studies will continue for years. In this study, we propose an improved learning model to predict the severity of the patients by exploiting a combination of machine learning techniques. The proposed model uses an adaptive boost algorithm with a decision tree estimator and a new parameter tuning process. The learning ratio of the new model is promising after many repeated experiments are performed by using different parameters to reduce the effect of selecting random parameters. The proposed algorithm is compared with other recent state-of-the-art algorithms on UCI data sets and a recent Covid-19 dataset. It is observed that competitive accuracy results are obtained, and we hope that this study unveils more usage of advanced machine learning approaches.

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Section: Related Worksupporting

confidence: 73%

An empowered AdaBoost algorithm implementation: A COVID-19 dataset study

Sevinç

2022

Computers & Industrial Engineering

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“…AdaBoost was used as classifier method to detect malicious URLs. They showed that AdaBoost algorithm gave more accuracy than other algorithms [24]. c) Gradient boosting Gradient boosting was a model that used decision trees to train several trees together, in which each decision tree learned from previous tree errors, resulting in greater accuracy in prediction.…”

mentioning

confidence: 99%

Oversampling technique in student performance classification from engineering course

Rachburee

Punlumjeak

2021

IJECE

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<span>The first year of an engineering student was important to take proper academic planning. All subjects in the first year were essential for an engineering basis. Student performance prediction helped academics improve their performance better. Students checked performance by themselves. If they were aware that their performance are low, then they could make some improvement for their better performance. This research focused on combining the oversampling minority class data with various kinds of classifier models. Oversampling techniques were SMOTE, Borderline-SMOTE, SVMSMOTE, and ADASYN and four classifiers were applied using MLP, gradient boosting, AdaBoost and random forest in this research. The results represented that Borderline-SMOTE gave the best result for minority class prediction with several classifiers.</span>

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“…2D or 3D convolutional layers are typically utilized with the pictures. However, a 1D convolutional layer has been employed to work with text, which has shown to be highly successful, mainly when dealing with time-series or sequence data [ 39 , 40 ]. CNN reduces the requirement for manual features extraction because the network learns the features immediately.…”

Section: Methodsmentioning

confidence: 99%

An Assessment of Lexical, Network, and Content-Based Features for Detecting Malicious URLs Using Machine Learning and Deep Learning Models

Aljabri

Alhaidari

Mohammad

et al. 2022

Computational Intelligence and Neuroscience

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The World Wide Web services are essential in our daily lives and are available to communities through Uniform Resource Locator (URL). Attackers utilize such means of communication and create malicious URLs to conduct fraudulent activities and deceive others by creating deceptive and misleading websites and domains. Such threats open the doors for many critical attacks such as spams, spyware, phishing, and malware. Therefore, detecting malicious URL is crucially important to prevent the occurrence of many cybercriminal activities. In this study, we examined a set of machine learning (ML) and deep learning (DL) models to detect malicious websites using a dataset comprising 66,506 records of URLs. We engineered three different types of features including lexical-based, network-based and content-based features. To extract the most discriminative features in the dataset, we applied several features selection algorithms, namely, correlation analysis, Analysis of Variance (ANOVA), and chi-square. Finally, we conducted a comparative performance evaluation for several ML and DL models considering set of criteria commonly used to evaluate such models. Results depicted that Naïve Bayes (NB) was the best model for detecting malicious URLs using the applied data with an accuracy of 96%. This research has made contribution to the field by conducting significant features engineering and analysis to identify the best features for malicious URLs predictions, compare different models and achieve a high accuracy using a large new URL dataset.

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Detecting malicious URLs using binary classification through adaboost algorithm

Cited by 30 publications

References 14 publications

An empowered AdaBoost algorithm implementation: A COVID-19 dataset study

An empowered AdaBoost algorithm implementation: A COVID-19 dataset study

Oversampling technique in student performance classification from engineering course

An Assessment of Lexical, Network, and Content-Based Features for Detecting Malicious URLs Using Machine Learning and Deep Learning Models

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