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Investigation of Cybersecurity Attacks and Threats on Cloud Using Black Widow Algorithm with Recurrent Neural Network

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Abstract

The amount of personal and sensitive information collected by data collectors is rising. Those details are processed and saved on the cloud’s servers. Risks and hazards exist in the cloud infrastructure. The amount of data stored on the cloud is enormous, and some of it is secret or personal, making it vulnerable to a breach or attack. In this case, a strong security solution was required to secure the data from hackers and eavesdroppers. In the field of cloud computing, anomalies and insider assaults will deactivate service providers, resulting in the entire system failing. Insider assaults and infiltration are difficult to handle with traditional network defensive measures. The anomaly identification approach is created in this study to determine the incidence of attack, and the proposed approach uses black widow algorithm for feature selection whereby the classification is attained using recurrent neural network (RNN). The process of feature selection will eliminate the redundant features and the significant features are retrieved using meta-heuristic technique. The selected features are utilized for classification using RNN. The feature selection highly helps the process of classification and it enhances the accuracy of the classification. The classification process is simplified by the feature selection process and the training error is minimized by the RNN technique. The use of a neural network to effectively identify features improves classification accuracy. The RNN’s performance investigation and outcomes categorize real-time threats in the cloud environment with high accuracy.

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The authors declare that they have no known competing financial interests.

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Authors and Affiliations

  1. Department of Information Technology, Nehru Arts and Science College, TM Palayam, Tamil Nadu, Coimbatore, India

    S. Senthil Kumar

  2. Department of Computer Applications, Nehru Arts and Science College, TM Palayam, Coimbatore, Tamil Nadu, India

    S. Arockia Panimalar & M. Prakash

  3. Department of MCA, Sree Saraswathi Thyagaraja College, Tamil Nadu, Pollachi, Coimbatore, India

    A. Krishnakumar

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  1. S. Senthil Kumar
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  2. S. Arockia Panimalar
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  3. A. Krishnakumar
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Correspondence to S. Senthil Kumar.

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This article is part of the topical collection “Predictive Artificial Intelligence for Cyber Security and Privacy” guest edited by Hardik A. Gohel, S. Margret Anouncia and Anthoniraj Amalanathan.

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Senthil Kumar, S., Arockia Panimalar, S., Krishnakumar, A. et al. Investigation of Cybersecurity Attacks and Threats on Cloud Using Black Widow Algorithm with Recurrent Neural Network. SN COMPUT. SCI. 3, 451 (2022). https://doi.org/10.1007/s42979-022-01304-9

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