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.

Mar 6, 2024 · This paper offers an overview of the most relevant techniques for the accurate detection of fraudulent URLs, from the most widely used machine learning and deep learning algorithms, to the application, as a proof of concept, of classification models based on quantum machine learning.

Jul 17, 2022 · In this article, we have demonstrated a machine learning approach to detect Malicious URLs. We have created 22 lexical features from raw URLs and trained three machine learning models XG Boost, Light GBM, and Random forest.

In this paper, we propose a malicious URL detection method using machine learning techniques based on our proposed URL behaviors and attributes. Moreover, bigdata technology is also exploited to improve the capability of detection malicious URLs based on abnormal behaviors.

It leverages data preprocessing and vectorization with TfidVectorizer and applies a machine learning model to classify URLs as malicious or benign. Data Preprocessing: Tokenization, splitting, and removal of repeated patterns in URLs. Feature Extraction: Use of TF-IDF Vectorizer for text-based feature extraction.

Nov 12, 2020 · This chapter proposes using host-based and lexical features of the associated URLs to better improve the performance of classifiers for detecting malicious web sites.

In this project, supervised Machine Learning (ML) is employed to identify and detect malicious URLs. The ISCX-URL-2016 dataset from the Canadian Institute for Cyber Security is employed for evaluation purposes. This dataset contains 79 features with four classes of URLs, namely spam, malware, phishing, and benign.

We developed a machine learning model to detect malicious URLs by combining lexical, host-based, and content-based features, overcoming the limitations of traditional blacklisting methods.

Feb 12, 2022 · A malicious URL is a link that is created with the intent of promoting scams and frauds. Clicking on such a link can download a multitude of malware that will compromise your machine or...

This research project compares the accuracies of varioius machine algorithms and deep learning frameworks in detecting and classifying malicious URLs using lexcial features.