Malicious Url Detection And Classification Analysis Using Machine In order to efficiently identify malicious urls, this paper suggests a sophisticated framework that combines machine learning (ml), deep learning (dl), and natural language processing (nlp) techniques. 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.
Pdf Detection Of Malicious Url Using Machine Learning A Review The developed model for malicious url detection exhibits impressive results, but improvement is needed, particularly in reducing the prediction time of 30 seconds for real time detection. 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 developed model for malicious url detection exhibits impressive results, but improvement is needed, particularly in reducing the prediction time of 30 seconds for real time detection. One of the most common methods criminals use to conduct their online crimes is via malicious urls (uniform resource locator), which constitutes about 60% of most cyber attacks.
Pdf Malicious Url Detection And Classification Analysis Using Machine The developed model for malicious url detection exhibits impressive results, but improvement is needed, particularly in reducing the prediction time of 30 seconds for real time detection. One of the most common methods criminals use to conduct their online crimes is via malicious urls (uniform resource locator), which constitutes about 60% of most cyber attacks. This paper successfully demonstrates the practical application of machine learning in the detection of malicious urls by leveraging lexical features such as url length, presence of special characters, embedded ip addresses, and suspicious keywords. This study presents a comprehensive comparative analysis of machine learning, deep learning, and optimization based hybrid methods for malicious url detection on the malicious phish dataset. This study explores an effective technique of detecting malicious url detection with machine learnings with explainability. in particular, three advanced ml models are applied on one real parameters url dataset, logistic regression (lr), decision trees (dt) and random forest (rf) are employed. The results confirm that combining lightweight nlp representations with imbalance aware ensemble learning provides an efficient and scalable solution for real time malicious url detection. malicious urls constitute a major cyber threat responsible for large scale phishing, credential theft, and malware dissemination across digital platforms. traditional blacklist based mechanisms offer limited.
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