Optimistic Ensemble Federated Learning Based on Spread Spectral Support Vector Feature Selection with Multi Perceptron Neural Network for Anomaly Detection in Cloud Environment

Authors

  • S. Edwin Raja Assistant Professor, Department of Computer Science and Engineering, Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology, Avadi , Chennai -600062, Tamil Nadu,India.
  • R. V. V. S. V. Prasad Professor. Department of Information Technology , Swarnandhra College of Engineering & Technology, Narsapur-534280, India.
  • Gunaselvi Manohar Professor, Department of Electronics and Instrumentation Engineering, Easwari Engineering College (Autonomous), Chennai,India.
  • V. Krishna Meera Assistant Professor, Department of Electronics and communication engineering ,Ramco Institute of Technology, Rajapalayam, Tamil Nadu 626117,India.
  • G. Sathya Assistant Professor, Department of Electronics and Communication Engineering P.S.R.R College of Engineering, Sivakasi-626140, Tamil Nadu, India.
  • S. Gnanasambanthan Assistant Professor, Department of Electronics and Communication Engineering, P. S. R Engineering College, Sevalpatti, Sivakasi-626140,Tamil Nadu,India.

Keywords:

Ensemble federated learning, Support vector, feature selection and classification, neural network, anomaly detection

Abstract

Wireless Sensor Network (WSN) plays an important role in identifying, monitoring, and securing scalable networks along the Internet of communication all over the world in a cloud environment. Integrating the cloud manages transmission and data control securely to make an effort using energy efficient techniques by various machine learning techniques. The anomalies take part due to malfunction of WSN nodes, leads an inappropriate activity to unsecure the communication. Identifying the anomalies is a big problem based on the feature dimension and behavioural analysis in the communication medium. Most of the prevailing techniques failed to analyze the anomaly properties and behavior response features, leading to increasing feature dimension to produce low-level detection accuracy, precision, and recall rate f1 score, with more false rates and time complexity. To resolve this problem, to propose an Optimistic Ensemble Federated Learning (OEFL) Based on Spread Spectral Support Vector Feature Selection (Ss-SSVFS) with Multi Perceptron Neural Network (MPNN) for anomaly detection in a cloud Environment. Initially, the preprocessing is carried out by min-max normalization techniques to make the affine transformation to reduce the unleased values in the dataset. The Spread spectral support vector feature selection (Ss-SCVFA) is applied to select the features by screening the Support Vector Machine (SVM) class spread with Decision Tree Neural Unit (DTNN) to form feature patterns. The patterns are ranked by ordered class depending on the outlier forming anomaly weight factor to choose the feature limits to reduce the dimension. The feature patterns are trained with a Perceptron Neural Network (MPNN) to identify the behavioral properties into class by reference. The proposed system achieves high performance in identifying the anomalies effectively with a high precision rate, recall rate, and f1-score with a low false rate, and redundant time complexity. Compared to the prevailing techniques, the identification accuracy is at a high level by attaining the accuracy level.

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Published

13.12.2023

How to Cite

Raja, S. E. ., Prasad, R. V. V. S. V. ., Manohar, G. ., Meera, V. K. ., Sathya, G. ., & Gnanasambanthan, S. . (2023). Optimistic Ensemble Federated Learning Based on Spread Spectral Support Vector Feature Selection with Multi Perceptron Neural Network for Anomaly Detection in Cloud Environment. International Journal of Intelligent Systems and Applications in Engineering, 12(8s), 201–210. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/4110

Issue

Vol. 12 No. 8s (2024)

Section

Research Article

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