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Securing communicating networks in the age of big data: an advanced detection system for cyber attacks

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Abstract

Big data security is becoming increasingly important in today’s data-driven world. Big data refers to large amounts of data generated from applications like airlines, hospitals, and government organizations, including social media and banking. This data contains insightful information that can be used for data analytics, research, and decision-making. However, because the data may contain sensitive information such as personally identifiable information, trade secrets, and confidential business data, it poses significant security risks. Big data security entails protecting the data’s confidentiality, authenticity, and accessibility. MapReduce is the part of big data that can process large datasets in a distributed computing environment. Google initially developed it, which is now widely used in big data processing. MapReduce works by dividing the extensive data set into smaller chunks and distributing the processing across a cluster of computers. The map function converts the given input information into key-value pairs. The second phase is the reduced phase focused on generating the intermediate results from the map phase and combined as the final results. The reduce function condenses the key-value pairs produced by the map function into more minor key-value pairs. This paper describes an advanced detection system (ADS) to predict cyber Attacks from two publically datasets, KDD Cup 1999 and UNSW-NB15 Dataset. The performance of ADS is improved by adopting the rough set theory for the effective prediction of cyber Attacks.

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  1. Department of CSE, Sathyabama Institute of Science and Technology, Deemed to Be University, Chennai, India

    S. Uma Maheswara Rao & L. Lakshmanan

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  1. S. Uma Maheswara Rao
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  2. L. Lakshmanan
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Rao, S.U.M., Lakshmanan, L. Securing communicating networks in the age of big data: an advanced detection system for cyber attacks. Opt Quant Electron 56, 116 (2024). https://doi.org/10.1007/s11082-023-05715-7

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