stegheck and URL detection using machine learning

Bansie V Iyengar; Kamalesh Hari M U; Gopi M; C P Jetlin

stegheck and URL detection using machine learning

Authors: Bansie V Iyengar, Kamalesh Hari M U, Gopi M, C P Jetlin

Unique Paper ID: 150836
Volume: 7
Issue: 10
PageNo: 215-219

Keywords: No Keywords Found

Abstract:
This paper presents three security concepts. The first two approaches are of the form of image and audio steganography and the third security concept that is covered is malicious URL detection. Steganography is the technique of hiding secret data in order to avoid detection, the secret data is then extracted at its destination. The use of steganography can be combined with encryption as an extra step for hiding or protecting data. Depending on the nature of the cover object, steganography can be divided into four types: Text Steganography, Image Steganography, Video Steganography, Audio Steganography. Steganography is a means of storing data in a way that it hides the existence of them. using steganography to communicate greatly reduces the risk of information leakage. steganography enhances the privacy individually, although it is not a substitute for encryption. Malicious Web sites are a cornerstone of Internet criminal activities. These Web sites contain various unwanted content such as spam-advertised products, phishing sites, dangerous "drive-by" harness that infect a visitor's system with malware. Despite a growing number of vendors offering anti-phishing solutions, phishing is a bigger problem than ever. The problem is so big, in fact, that itâ€™s hard to keep up with the latest facts and figures. The average breach costs organizations $3.92 million. This number will generally be higher in larger organizations and lower in smaller organizations. While the Manufacturing industry saw the most breaches from social attacks, employees working in Wholesale Trade are the most frequently targeted by phishing attacks, with 1 in every 22 users being targeted by a phishing email last year. In the proposed system we are using Machine-Learning techniques to classify a URL as either safe or unsafe in Real Time without even the need to download the webpage. We will be using the Logistic regression model, where the Labelled URLs will be pre-processed by eliminating extras like the 3rd level domains. From this project, we hope to build alternative security solution for the above discussed problems.

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ISSN: 2349-6002
Volume: 7
Issue: 10
PageNo: 215-219

stegheck and URL detection using machine learning

Available:https://ijirt.org/Article?manuscript=150836

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