A malicious URLs detection system using optimization and machine learning classifiers

Lee, Ong Vienna; Heryanto, Ahmad; Razak, Mohd Faizal Ab; Raffei, Anis Farihan Mat; Phon, Danakorn Nincarean Eh; Kasim, Shahreen; Sutikno, Tole

doi:10.11591/ijeecs.v17.i3.pp1210-1214

Cited by 15 publications

(9 citation statements)

References 20 publications

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“…true-positives). The result showed that 31detected cases were erroneously labeled and agreed with [75]- [77] as false-positive; Also, 776 wrongly detected threats (i.e. false-negative) and 283-correctly recognized malicious instances labeled as true-negative.…”

Section: B Discussion Of Findingsmentioning

confidence: 99%

DeLClustE: Protecting Users from Credit-Card Fraud Transaction via the Deep-Learning Cluster Ensemble

Aghware,

Yoro,

Ejeh

et al. 2023

IJACSA

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Fraud is the unlawful acquisition of valuable assets gained via intended misrepresentation. It is a crime committed by either an internal/external user, and associated with acts of theft, embezzlement, and larceny. The proliferation of credit cards to aid financial inclusiveness has its usefulness alongside it attracting malicious attacks for gains. Attempts to classify fraudulent credit card transactions have yielded formal taxonomies as these attacks seek to evade detection. We propose a deep learning ensemble via a profile hidden Markov model with a deep neural network, which is poised to effectively classify credit-card fraud with a high degree of accuracy, reduce errors, and timely fashion. The result shows the ensemble effectively classified benign transactions with a precision of 97 percent. Thus, we posit a new scheme that is more logical, intuitive, reusable, exhaustive, and robust in classifying such fraudulent transactions based on the attack source, cause(s), and attack time gap.

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Section: B Discussion Of Findingsmentioning

confidence: 99%

DeLClustE: Protecting Users from Credit-Card Fraud Transaction via the Deep-Learning Cluster Ensemble

Aghware,

Yoro,

Ejeh

et al. 2023

IJACSA

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show abstract

“…However, ground truth is not always available for training [71]- [74]. FS consists of two modes/classes, namely the filter and the wrapper [75], [76].…”

Section: Feature Selection (Fs)mentioning

confidence: 99%

Enhancing the Random Forest Model via Synthetic Minority Oversampling Technique for Credit-Card Fraud Detection

Aghware,

Ojugo,

Adigwe

et al. 2024

J. Comput. Theor. Appl.

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Fraudsters increasingly exploit unauthorized credit card information for financial gain, targeting un-suspecting users, especially as financial institutions expand their services to semi-urban and rural areas. This, in turn, has continued to ripple across society, causing huge financial losses and lowering user trust implications for all cardholders. Thus, banks cum financial institutions are today poised to implement fraud detection schemes. Five algorithms were trained with and without the application of the Synthetic Minority Over-sampling Technique (SMOTE) to assess their performance. These algorithms included Random Forest (RF), K-Nearest Neighbors (KNN), Naïve Bayes (NB), Support Vector Machines (SVM), and Logistic Regression (LR). The methodology was implemented and tested through an API using Flask and Streamlit in Python. Before applying SMOTE, the RF classifier outperformed the others with an accuracy of 0.9802, while the accuracies for LR, KNN, NB, and SVM were 0.9219, 0.9435, 0.9508, and 0.9008, respectively. Conversely, after the application of SMOTE, RF achieved a prediction accuracy of 0.9919, whereas LR, KNN, NB, and SVM attained accuracies of 0.9805, 0.9210, 0.9125, and 0.8145, respectively. These results highlight the effectiveness of combining RF with SMOTE to enhance prediction accuracy in credit card fraud detection.

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“…Ozcan et al [89] proposed hybrid DL models that were combinations of deep NN (DNN)-LSTM and DNN-Bidirectional LSTM (BiLSTM). They used two datasets from Ebbu2017 [90] and PhishTank [14], along with a dataset from PhishStorm [91]. They have extracted the network-based and lexical features.…”

Section: ) Lexical and Network-based Features Studiesmentioning

confidence: 99%

Detecting Malicious URLs Using Machine Learning Techniques: Review and Research Directions

et al. 2022

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In recent years, the digital world has advanced significantly, particularly on the Internet, which is critical given that many of our activities are now conducted online. As a result of attackers' inventive techniques, the risk of a cyberattack is rising rapidly. One of the most critical attacks is the malicious URL intended to extract unsolicited information by mainly tricking inexperienced end users, resulting in compromising the user's system and causing losses of billions of dollars each year. As a result, securing websites is becoming more critical. In this paper, we provide an extensive literature review highlighting the main techniques used to detect malicious URLs that are based on machine learning models, taking into consideration the limitations in the literature, detection technologies, feature types, and the datasets used. Moreover, due to the lack of studies related to malicious Arabic website detection, we highlight the directions of studies in this context. Finally, as a result of the analysis that we conducted on the selected studies, we present challenges that might degrade the quality of malicious URL detectors, along with possible solutions.

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A malicious URLs detection system using optimization and machine learning classifiers

Cited by 15 publications

References 20 publications

DeLClustE: Protecting Users from Credit-Card Fraud Transaction via the Deep-Learning Cluster Ensemble

DeLClustE: Protecting Users from Credit-Card Fraud Transaction via the Deep-Learning Cluster Ensemble

Enhancing the Random Forest Model via Synthetic Minority Oversampling Technique for Credit-Card Fraud Detection

Detecting Malicious URLs Using Machine Learning Techniques: Review and Research Directions

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