Title: Privacy-preserving with data optimisation in social networks using ensemble algorithm and K-neural network

Authors: P.S. Arun Kaarthi; S. Sathiyabama

Addresses: Department of Computer Science, Periyar University, Salem, Tamilnadu, India ' Department of Computer Science, Thiruvalluvar Government Arts College, Rasipuram, Tamilnadu, India

Abstract: A huge collection of data is being produced for each second due to advance technology development and its innovation in the social media. Monitoring the system and network, securing lines and servers are coming to end by using various mechanism. The data accuracy has been increased by the K-nearest neighbour (KNN) classification. A deep learning technique which is a neural network is used for the detection of attacks being done by hackers or fraudulent users. The proposed model uses a programming language called Python which has packages of Scikit-learn, Tensorflow and Seaborn. We have also developed and found the accuracy rate gets increased by the deep learning model and so the attacks made on the social networks have been avoided as much as feasible.

Keywords: social networking; data mining; intrusion detection system; IDS; data optimisation; deep learning techniques; clustering-based IDS; ensemble algorithm; KNN classification.

DOI: 10.1504/IJESMS.2023.133952

International Journal of Engineering Systems Modelling and Simulation, 2023 Vol.14 No.4, pp.214 - 224

Received: 12 Oct 2021
Accepted: 28 Jan 2022
Published online: 06 Oct 2023 *

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