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INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT International Peer Reviewed & Refereed Journals, Open Access Journal ISSN Approved Journal No: 2456-4184 | Impact factor: 8.76 | ESTD Year: 2016 Scholarly open access journals, Peer-reviewed, and Refereed Journals, 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) |
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| Paper Title: | Cyber Attack Detection With QR code Images Using Lightweight Deep Learning Model |
| Authors Name: | Sudarshan Gaikar , Ajay Bichukale , Deep Barvekar |
| Download E-Certificate: | Download |
| Author Reg. ID: |
IJNRD_217376
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| Published Paper Id: | IJNRD2404049 |
| Published In: | Volume 9 Issue 4, April-2024 |
| DOI: | http://doi.one/10.1729/Journal.38772 |
| Abstract: | 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. |
| Keywords: | Cybersecurity, Lightweight deep learning model, QR code, Generalization. |
| Cite Article: | "Cyber Attack Detection With QR code Images Using Lightweight Deep Learning Model", International Journal of Novel Research and Development (www.ijnrd.org), ISSN:2456-4184, Vol.9, Issue 4, page no.a379-a384, April-2024, Available :http://www.ijnrd.org/papers/IJNRD2404049.pdf |
| Downloads: | 00072 |
| 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 |
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Published Paper ID:IJNRD2404049 Registration ID: 217376 Published In: Volume 9 Issue 4, April-2024 DOI (Digital Object Identifier): http://doi.one/10.1729/Journal.38772 Page No: a379-a384 Country: Panvel, Maharashtra, India Research Area: Computer Engineering Publisher : IJ Publication Published Paper URL : https://www.ijnrd.org/viewpaperforall?paper=IJNRD2404049 Published Paper PDF: https://www.ijnrd.org/papers/IJNRD2404049 |
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ISSN: 2456-4184
Impact Factor: 8.76 and ISSN APPROVED
Journal Starting Year (ESTD) : 2016
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