[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118311-en":3,"doc-seo-118311-105":29,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":11,"language":21,"language_code":22,"site_id":23,"html_lang":22,"table_of_contents":24,"faqs":25,"seo_title":26,"seo_description":14,"update_tm":27,"read_time":28},118311,7971461740909,"Levi","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",6,"Technology","Enhanced Malicious URL Detection System - Machine Learning Algorithms","Network information insecurity is escalating in both frequency and severity, with attackers leveraging end-to-end techniques and human weaknesses such as social engineering, phishing, and pharming. Malicious URLs deceive users and can redirect them to attacker-controlled resources, unwanted sites, phishing pages, or malware downloads. Multiple studies apply machine learning and deep learning for detection. This paper proposes a machine learning-based malicious URL detection system using designed URL behaviors and attributes, augmented by big data technologies for improved detection capability, yielding significant experimental results.","Journal of Science and Technology  \nISSN: 2456-5660 Volume 8, Issue 07 (July-2023)  \n[www.jst.org.in](www.jst.org.in) DOI:[https://doi.org/10.46243/jst.2023.v8.i07.pp39-44](https://doi.org/10.46243/jst.2023.v8.i07.pp39-44)   \nENHANCED MALICIOUS URL DETECTION SYSTEM WITH  \nMACHINE LEARNING ALGORITHMS  \nMrs. Bessy 1, Archana Sharma 2, CH. Rasmitha 3, CH. Sindhu 4, D. Satvika 5  \n1 Associate Professor, Department of CSE, Malla Reddy Engineering College for Women, Hyderabad, Telangana, India.  \n2,3,4,5 UG Scholar, Department ofCSE, Malla Reddy Engineering College for Women, Hyderabad, Telangana, India.  \n[bessybijo@gmail.com](bessybijo@gmail.com)  \n[sharchana2001@gmail.com](sharchana2001@gmail.com), [rasmithachinthalapally@gmail.com](rasmithachinthalapally@gmail.com),  \n[sindhuchirra23@gmail.com](sindhuchirra23@gmail.com), [sathwikadasari01@gmail.com](sathwikadasari01@gmail.com).  \nTo Cite this Article  \nMrs. Bessy , Archana Sharma , CH. Rasmitha , CH. Sindhu , D. Satvika,“MALICIOUS URL  \n”  \nDETECTION SYSTEM WITHMACHINE LEARNING ALGORITHMS Journal of Science and Technology,  \nVol. 08, Issue 07,-July 2023, pp39-44  \nArticle Info  \nReceived: 26-06-2023 Revised: 28-06-2023 Accepted: 10-07-2023 Published: 18-07-2023  \nABSTRACT  \nCurrently, the risk of network information insecurity is increasing rapidly in number and level of danger. The methods mostly used by hackers today is to attack end-to end technology and exploit human vulnerabilities. These techniques include social engineering, phishing, pharming, etc. One of the steps in conducting these attacks is to deceive users with malicious Uniform Resource Locators (URLs) . As a results, Malicious URL detection is of great interest nowadays. There have been several scientific studies showing several methods to detect malicious URLs based on machine learning and deep learning techniques. 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. In short, the proposed detection system consists of a new set of URLs features and behaviors, a machine learning algorithm, and a big data technology. The experimental results show that the proposed URL attributes and behavior can help improve the ability to detect malicious URL significantly. This is suggested that the proposed system may be considered as anoptimized and friendly used solution for malicious URL detection.  \nINTRODUCTION  \nUniform Resource Locator (URL) is used to refer to resources on the Internet. In [1],  \nSahoo et al. presented about the characteristics and two basic components of the URL as:  \nprotocol identifier, which indicates what protocol to use, and resource name, which  \nPublished by: Longman Publishers [www.jst.org.in](www.jst.org.in)  \nspecifies the IP address or the domain name where the resource is located. Each URL has  \na specific structure and format. Attackers often try to change one or more components of  \nthe URL's structure to deceive users for spreading their malicious URL. Malicious URLs  \nare known as links that adversely affect users. These URLs will redirect users to  \nresources or pages on which attackers can execute codes on users' computers, redirect  \nusers to unwanted sites, malicious website, or another phishing site, or malware  \ndownload. Malicious URLs can also be hidden in download links that are deemed safe  \nand can spread quickly through file and message sharing in shared networks. Some attack  \ntechniques that use malicious URLs include [2, 3, 4]: Drive-by Download, Phishing and  \nSocial Engineering, and Spam.  \nAccording to statistics presented in [5], in 2019, the attacks using spreading malicious URL technique are ranked first among the 10 most common attack techniques. Especially, according to this statistic, the three main URL spreading techniques, which are malicious ","cbCaicnHKRKL1i1B","https://ap.wps.com/l/cbCaicnHKRKL1i1B","pdf",279977,1,"English","en",105,"# Introduction\n## Malicious URL detection trends\n## Signature-based malicious URL detection\n## Existing system overview","[{\"question\":\"What makes malicious URLs dangerous in the proposed context?\",\"answer\":\"Malicious URLs can redirect users to attacker-controlled pages where harmful code can execute, or to unwanted sites such as phishing pages and malware download endpoints.\"},{\"question\":\"Which detection approaches are compared in the paper?\",\"answer\":\"The paper discusses signature/rule-based detection and behavior-analysis-based detection using machine learning or deep learning to classify URLs by their attributes.\"},{\"question\":\"What components does the proposed system include?\",\"answer\":\"The system combines a new set of URL features and behaviors, a machine learning algorithm, and big data technology to enhance detection of malicious URLs.\"}]","Enhanced Malicious URL Detection System - 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