Open AccessMachine Learning Based Solution for the Detection of Malicious JPEG Image and QR Code
Author(s) -
Prof. Asma Shaikh,
Ms. Rasika Kotavadekar,
Ms. Sanjita Sawant,
Ms. Sayali Landge
Publication year - 2021
Publication title -
international journal of advanced research in science, communication and technology
Language(s) - English
Resource type - Journals
ISSN - 2581-9429
DOI - 10.48175/ijarsct-1071
Subject(s) - computer science , malware , code (set theory) , steganography , jpeg , vulnerability (computing) , computer security , exploit , image file formats , image (mathematics) , artificial intelligence , programming language , set (abstract data type)
The biggest invention of 21st century is the social media. It is biggest platform which is using to share data, files and documents. Even it is using to share thoughts, ideas and feelings using different tools and techniques. People are hyper connected with each other and they are continuously sharing the information. For criminals, deploying malware in such scenario is very easy and propagating malware through JPEG images and QR Code is one of the best and most advanced method. Using steganography techniques, criminals embedded the malicious codes with legitimate or innocent looking images. This malicious content is just few line of codes which exploit the vulnerability of application. It give remote access of this system to the attacker which can do criminal act. In this framework, our primary purpose is to find the presence of any code or data in image. After it, the major section of this framework based upon the finding of code and its adverse effects. This framework shows the corresponding solution to the malicious code presence in JPEG images and QR code which are spreading through online social networking sites.