A Distributed Platform for Intrusion Detection System Using Data Stream Mining in a Big Data Environment

Schuartz, Fábio César; Fonseca, Mauro; Munaretto, Anelise

doi:10.1109/csnet56116.2022.9955598

Cited by 2 publications

(1 citation statement)

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“…The increase in volume, speed, and variety of data requires a more robust, accurate intrusion detection system capable of analyzing a huge amount of data. This work [13] proposes the creation of an intrusion detection system using stream classifiers and three classification layers -with and without a reduction in the number of features of the records and three classifiers in parallel with a voting system.…”

Section: Related Workmentioning

confidence: 99%

Secure And Adaptive Data Stream Mining For New Generation Big Data

A Ravi Kishore, Dr Gururaj Murtugudde

2023

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As the era of new-generation big data applications unfolds, the need for secure and adaptive data stream mining has become increasingly paramount. Evolving databases, characterized by ever-changing data streams and dynamic data distributions, present unique challenges and opportunities. This paper addresses the crucial intersection of security and adaptability in the context of data stream mining for new-generation big data. First, we delve into the evolving landscape of big data, where real-time data streams from diverse sources drive decision-making processes. Ensuring the privacy and security of sensitive information within these data streams is a fundamental concern. We explore cryptographic techniques, anonymization methods, and access control mechanisms that safeguard data while allowing for meaningful analysis. We present novel adaptive algorithms and model update strategies that can continuously learn and adjust to changing data distributions. These approaches enable data stream mining to remain effective and accurate over time. This paper offers insights into the fusion of security and adaptability in data stream mining, providing a foundation for the development of robust and privacy-conscious solutions for the evolving landscape of new-generation big data applications.

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Section: Related Workmentioning

confidence: 99%