Classification of Attacks on Big Data using Deep Learning Approach

Ms. Shalu Saini , Dr. Priti Singla

Big data security, MitM attack, DoS, Brute force, Deep learning

The classification of attacks on big data using a deep learning approach is essential for safeguarding the integrity, confidentiality, and availability of large-scale data environments. MitM, DoS, and Brute Force represent significant threats to big data systems, and leveraging deep learning techniques can provide an effective means of detection and mitigation. Deep learning models can autonomously analyze massive datasets, adapt to evolving attack strategies, and identify intricate attack patterns. However, this approach is not without challenges. Deep learning for big data security requires access to diverse and labeled datasets, which can be scarce and challenging to obtain for specific attack types. Additionally, the resource-intensive nature of deep learning models may pose scalability and operational challenges for organizations with extensive big data infrastructure. Moreover, false positives and false negatives can be a concern, demanding continuous fine-tuning and adjustment to minimize misclassifications. Despite these challenges, the potential benefits of using deep learning in attack classification within big data environments are substantial, as they enable real-time threat detection and proactive security measures, ultimately reinforcing the overall security posture of these critical data systems.

Published Paper   E-Certificate

"Classification of Attacks on Big Data using Deep Learning Approach", TIJER - TIJER - INTERNATIONAL RESEARCH JOURNAL (www.TIJER.org), ISSN:2349-9249, Vol.11, Issue 2, page no.a51-a58, February-2024, Available :https://tijer.org/TIJER/papers/TIJER2402006.pdf

Volume 11 Issue 2, February-2024

Pages : a51-a58

Paper Reg. ID: TIJER_150941

Published Paper Id: TIJER2402006

Downloads: 000272

Research Area: Science and Technology

Country: -, -, India

Published Paper PDF: https://tijer.org/TIJER/papers/TIJER2402006

Published Paper URL: https://tijer.org/TIJER/viewpaperforall?paper=TIJER2402006

ISSN: 2349-9249 | IMPACT FACTOR: 8.57 Calculated By Google Scholar | ESTD YEAR: 2014

An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.57 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator

Publisher: TIJER (IJ Publication) Janvi Wave

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