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A Review of Artificial Intelligence to Enhance the Security of Big Data Systems: State-of-Art, Methodologies, Applications, and Challenges

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Abstract

Technological advancements modernize the way we live with the changes made both globally and nationwide. These technological improvements also cause adverse effects in the form of security threats. To overcome this problem, many researchers integrated both big data and artificial intelligence (AI) techniques to enhance the security of internet-connected devices. Big data normally represents the massive information collected from the web which is both structured and unstructured and big data analytics deal with the processing of this information which is often cumbersome for the traditional data processing techniques. AI technique helps machines to function similarly to humans in solving complex problems. Recent studies show that the AI technique identifies different attacks which compromise the security of the application and system in an organization. AI techniques respond to different attacks in real-time using machine learning and deep learning techniques. Machine learning is a subfield of AI which can identify the patterns present in the input data with less manual intervention. Deep Learning is a subfield of machine learning in which the algorithm used to construct an artificial neural network (ANN) is trained using a large amount of data to solve complex problems with less manual intervention. This study evaluates the security issues faced by Big data systems using AI techniques focusing on different attacks, defense strategies, and security evaluation models. The AI-based techniques for security enhancement in big data-based systems are divided into eight categories: reinforcement learning, swarm intelligence, deep learning, multi-agent, game theory, ML, and ANN. The review is systematically conducted using the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analysis) technique. The open issues present in the security domain can be used by different authors as a potential area for future research.

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Authors and Affiliations

  1. Department of Art and Architecture, Central South University, Changsha, China

    Duan Dai & Sahar Boroomand

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  1. Duan Dai
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  2. Sahar Boroomand
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Correspondence to Sahar Boroomand.

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Dai, D., Boroomand, S. A Review of Artificial Intelligence to Enhance the Security of Big Data Systems: State-of-Art, Methodologies, Applications, and Challenges. Arch Computat Methods Eng 29, 1291–1309 (2022). https://doi.org/10.1007/s11831-021-09628-0

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