Precise URL Phishing Detection Using Neural Networks

• URL: http://arxiv.org/abs/2110.13424v1
• Date: Tue, 26 Oct 2021 05:55:53 GMT
• Title: Precise URL Phishing Detection Using Neural Networks
• Authors: Aman Rangapur, Dr Ajith Jubilson
• Abstract summary: We present you with ways to detect such malicious URLs with state of art accuracy with neural networks. Different from previous works, where web content, URL or traffic statistics are examined, we analyse only the URL text. The network is optimised and can be used even on small devices such as Ras-Pi without a change in performance.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: With the development of the Internet, ways of obtaining important data such as passwords and logins or sensitive personal data have increased. One of the ways to extract such information is page impersonation, also called phishing. Such websites do not provide service but collect sensitive details from the user. Here, we present you with ways to detect such malicious URLs with state of art accuracy with neural networks. Different from previous works, where web content, URL or traffic statistics are examined, we analyse only the URL text, making it faster and which detects zero-day attacks. The network is optimised and can be used even on small devices such as Ras-Pi without a change in performance.

Related papers

• PhishNet: A Phishing Website Detection Tool using XGBoost [1.777434178384403]
  PhisNet is a cutting-edge web application designed to detect phishing websites using advanced machine learning. It aims to help individuals and organizations identify and prevent phishing attacks through a robust AI framework.
  arXiv  Detail & Related papers  (2024-06-29T21:31:13Z)
• A Flow is a Stream of Packets: A Stream-Structured Data Approach for DDoS Detection [32.22817720403158]
  We propose a new tree-based DDoS detection approach that operates on a flow as a stream structure. Our approach matches or exceeds existing machine learning techniques' accuracy, including state-of-the-art deep learning methods.
  arXiv  Detail & Related papers  (2024-05-12T09:29:59Z)
• PhishGuard: A Convolutional Neural Network Based Model for Detecting Phishing URLs with Explainability Analysis [1.102674168371806]
  Phishing URL identification is the best way to address the problem. Various machine learning and deep learning methods have been proposed to automate the detection of phishing URLs. We propose a 1D Convolutional Neural Network (CNN) and trained the model with extensive features and a substantial amount of data.
  arXiv  Detail & Related papers  (2024-04-27T17:13:49Z)
• Cleaner Pretraining Corpus Curation with Neural Web Scraping [39.97459187762505]
  This paper presents a simple, fast, and effective Neural web Scraper (NeuScraper) to help extract primary and clean text contents from webpages. Experimental results show that NeuScraper surpasses the baseline scrapers by achieving more than a 20% improvement.
  arXiv  Detail & Related papers  (2024-02-22T16:04:03Z)
• My Brother Helps Me: Node Injection Based Adversarial Attack on Social Bot Detection [69.99192868521564]
  Social platforms such as Twitter are under siege from a multitude of fraudulent users. Due to the structure of social networks, the majority of methods are based on the graph neural network(GNN), which is susceptible to attacks. We propose a node injection-based adversarial attack method designed to deceive bot detection models.
  arXiv  Detail & Related papers  (2023-10-11T03:09:48Z)
• SqueezerFaceNet: Reducing a Small Face Recognition CNN Even More Via Filter Pruning [55.84746218227712]
  We develop SqueezerFaceNet, a light face recognition network which less than 1M parameters. We show that it can be further reduced (up to 40%) without an appreciable loss in performance.
  arXiv  Detail & Related papers  (2023-07-20T08:38:50Z)
• Phishing URL Detection: A Network-based Approach Robust to Evasion [17.786802845563745]
  We present a network-based inference method to accurately detect phishing URLs camouflaged with legitimate patterns. Our method consistently shows better detection performance throughout various experimental tests than state-of-the-art methods.
  arXiv  Detail & Related papers  (2022-09-03T16:09:05Z)
• An Adversarial Attack Analysis on Malicious Advertisement URL Detection Framework [22.259444589459513]
  Malicious advertisement URLs pose a security risk since they are the source of cyber-attacks. Existing malicious URL detection techniques are limited and to handle unseen features as well as generalize to test data. In this study, we extract a novel set of lexical and web-scrapped features and employ machine learning technique to set up system for fraudulent advertisement URLs detection.
  arXiv  Detail & Related papers  (2022-04-27T20:06:22Z)
• PhishMatch: A Layered Approach for Effective Detection of Phishing URLs [8.658596218544774]
  We present a layered anti-phishing defense, PhishMatch, which is robust, accurate, inexpensive, and client-side. A prototype plugin of PhishMatch, developed for the Chrome browser, was found to be fast and lightweight.
  arXiv  Detail & Related papers  (2021-12-04T03:21:29Z)
• Privacy Adversarial Network: Representation Learning for Mobile Data Privacy [33.75500773909694]
  A growing number of cloud-based intelligent services for mobile users require user data to be sent to the provider. Prior works either obfuscate the data, e.g. add noise and remove identity information, or send representations extracted from the data, e.g. anonymized features. This work departs from prior works in methodology: we leverage adversarial learning to a better balance between privacy and utility.
  arXiv  Detail & Related papers  (2020-06-08T09:42:04Z)
• Automating Botnet Detection with Graph Neural Networks [106.24877728212546]
  Botnets are now a major source for many network attacks, such as DDoS attacks and spam. In this paper, we consider the neural network design challenges of using modern deep learning techniques to learn policies for botnet detection automatically.
  arXiv  Detail & Related papers  (2020-03-13T15:34:33Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.