Version 1
: Received: 10 January 2024 / Approved: 11 January 2024 / Online: 12 January 2024 (03:53:09 CET)
Version 2
: Received: 13 January 2024 / Approved: 15 January 2024 / Online: 15 January 2024 (08:11:36 CET)
How to cite:
Potnurwar, P.; Ainchwar, A.; Neware, R.; Bongirwar, V. Intrusion Detection System for Big Data Environment Using Deep Learning. Preprints2024, 2024010912. https://doi.org/10.20944/preprints202401.0912.v2
Potnurwar, P.; Ainchwar, A.; Neware, R.; Bongirwar, V. Intrusion Detection System for Big Data Environment Using Deep Learning. Preprints 2024, 2024010912. https://doi.org/10.20944/preprints202401.0912.v2
Potnurwar, P.; Ainchwar, A.; Neware, R.; Bongirwar, V. Intrusion Detection System for Big Data Environment Using Deep Learning. Preprints2024, 2024010912. https://doi.org/10.20944/preprints202401.0912.v2
APA Style
Potnurwar, P., Ainchwar, A., Neware, R., & Bongirwar, V. (2024). Intrusion Detection System for Big Data Environment Using Deep Learning. Preprints. https://doi.org/10.20944/preprints202401.0912.v2
Chicago/Turabian Style
Potnurwar, P., Rahul Neware and Vrushali Bongirwar. 2024 "Intrusion Detection System for Big Data Environment Using Deep Learning" Preprints. https://doi.org/10.20944/preprints202401.0912.v2
Abstract
The necessity for effective intrusion detection systems (IDS) in big data environments has grown critical due to the rising prevalence of big data systems and the rising amount of security threats. Applying conventional intrusion detection methods to the enormous and intricate data produced by big data ecosystems presents difficulties. Deep learning, has proven to be exceptionally adept at deciphering complex, large-scale data. This study proposes a Deep Learning-based Intrusion Detection System (IDS) created specifically for the Big Data environment. Traditional intrusion detection systems struggle to efficiently identify and prevent cyber threats due to the ever-increasing volume and complexity of data in current networks. We suggest an IDS that makes use of Deep Learning (CNN, LSTM, GAN, etc.), to address this problem. These cutting-edge neural network topologies give the system the ability to process and analyze massive amounts of data for precise intrusion detection. Data collection, data preprocessing, feature engineering, DL model training, intrusion detection, warning generation, and reaction are some of the crucial parts of the suggested IDS. The data collection module mines the Big Data environment for network traffic, system logs, and security event information. To make sure the data is suitable for analysis, it is preprocessed. To improve the detection abilities of the DL models, important features are extracted from the data using feature engineering approaches.
Keywords
intrusion detection system; big data; deep learning; CNN; LSTM; GAN; cybersecurity; network security
Subject
Computer Science and Mathematics, Security Systems
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Commenter: Rahul Neware
Commenter's Conflict of Interests: Author
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