Cyber Attack Detection With QR code Images Using Lightweight Deep Learning Model

Cyber Attack Detection With QR code Images Using Lightweight Deep Learning Model

Registration ID: IJNRD_217376

Published ID: IJNRD2404049

DOI: http://doi.one/10.1729/Journal.38772

Sudarshan Gaikar , Ajay Bichukale , Deep Barvekar

Cybersecurity, Lightweight deep learning model, QR code, Generalization.

In today’s world information technologies are rapidly evolving because of that servers are attacked by cyber attacks. This cyber attack causes a big data loss to the organization and it becomes a big concern in such an organization. In the existing system, it only detects attacks when that attack happens once because of that it fails to detect the new attack that is going to happen. But our detection with QR code images using a lightweight deep learning model is able to detect the cyber attacks that have been identified. We have collected and curated a large dataset of QR code images, encompassing a wide range of use cases and variations. This dataset serves as the foundation for training and evaluating our lightweight deep-learning model. We propose a specially designed deep learning model optimized for QR code image analysis. This model is computationally efficient and effective at detecting anomalies and potential cyberattacks within QR codes. Our model differentiates between legitimate QR codes and potentially malicious ones. It identifies various forms of tampering, such as data manipulation, structural alterations, and hidden malware payloads. We evaluate the model’s robustness against adversarial attacks and its ability to generalize to unseen QR code variations, ensuring its effectiveness in the ever-evolving threat landscape. Our experimental results demonstrate the effectiveness of our lightweight deep model in detecting cyber attack threats within QR code images with high accuracy and low False-Positive rates. The model's efficiency and real-time capabilities make it a promising tool for enhancing cybersecurity in various domains where QR codes are utilized. In conclusion, this research paper approach to cyber attack detection through QR code image analysis leverages the power of deep learning to enhance security in digital applications.

Published Paper   E-Certificate

"Cyber Attack Detection With QR code Images Using Lightweight Deep Learning Model", IJNRD - INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT (www.IJNRD.org), ISSN:2456-4184, Vol.9, Issue 4, page no.a379-a384, April-2024, Available :https://ijnrd.org/papers/IJNRD2404049.pdf

Volume 9 Issue 4, April-2024

Pages : a379-a384

Paper Reg. ID: IJNRD_217376

Published Paper Id: IJNRD2404049

Downloads: 000121220

Research Area: Computer Engineering 

Country: Panvel, Maharashtra, India

Published Paper PDF: https://ijnrd.org/papers/IJNRD2404049.pdf

Published Paper URL: https://ijnrd.org/viewpaperforall?paper=IJNRD2404049

DOI: http://doi.one/10.1729/Journal.38772

Journal Name: INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT(IJNRD)

ISSN: 2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar | ESTD YEAR: 2016

An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.76 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator

Publisher: IJNRD (IJ Publication) Janvi Wave

This work is licensed under a Creative Commons Attribution 4.0 International License and The Open Definition

Call For Paper - Volume 10 | Issue 8 | August 2025

IJNRD is Scholarly open access journals, Peer-reviewed, and Refereed Journals, High Impact factor 8.76 (Calculate by google scholar and Semantic Scholar | AI-Powered Research Tool), Multidisciplinary, Monthly, Indexing in all major database & Metadata, Citation Generator, Digital Object Identifier(DOI) with Open-Access Publications.

INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT (IJNRD) aims to explore advances in research pertaining to applied, theoretical and experimental Technological studies. The goal is to promote scientific information interchange between researchers, developers, engineers, students, and practitioners working in and around the world. IJNRD will provide an opportunity for practitioners and educators of engineering field to exchange research evidence, models of best practice and innovative ideas.

Indexing In Google Scholar, SSRN, ResearcherID-Publons, Semantic Scholar | AI-Powered Research Tool, Microsoft Academic, Academia.edu, arXiv.org, Research Gate, CiteSeerX, ResearcherID Thomson Reuters, Mendeley : reference manager, DocStoc, ISSUU, Scribd, and many more

How to submit the paper?

Important Dates for Current issue

Paper Submission Open For: August 2025

Current Issue: Volume 10 | Issue 8

Last Date for Paper Submission: Till 31-Aug-2025

Notification of Review Result: Within 1-2 Days after Submitting paper.

Publication of Paper: Within 01-02 Days after Submititng documents.

Frequency: Monthly (12 issue Annually).

Journal Type: International Peer-reviewed, Refereed, and Open Access Journal.

Subject Category: Research Area