Performance Analysis of Different Activation and Loss Functions of Stacked Autoencoder for Dimension Reduction for NIDS on Cloud Environment

  IJETT-book-cover  International Journal of Engineering Trends and Technology (IJETT)          
© 2021 by IJETT Journal
Volume-69 Issue-4
Year of Publication : 2021
Authors : Nirmalajyothi Narisetty, Gangadhara Rao Kancherla, Basaveswararao Bobba, K. Swathi
DOI :  10.14445/22315381/IJETT-V69I4P224


MLA Style: Nirmalajyothi Narisetty, Gangadhara Rao Kancherla, Basaveswararao Bobba, K. Swathi  "Performance Analysis of Different Activation and Loss Functions of Stacked Autoencoder for Dimension Reduction for NIDS on Cloud Environment" International Journal of Engineering Trends and Technology 69.4(2021):169-176. 

APA Style:Nirmalajyothi Narisetty, Gangadhara Rao Kancherla, Basaveswararao Bobba, K. Swathi. Performance Analysis of Different Activation and Loss Functions of Stacked Autoencoder for Dimension Reduction for NIDS on Cloud Environment  International Journal of Engineering Trends and Technology, 69(4),169-176.

This paper`s objective is twofold, i) to provide a framework for a better intrusion detection system with an SVM classifier to detect new types of attacks in a cloud environment. ii) Performance comparative study is carried out to identify the best combination of Stacked Autoencoder (SAE) activation and loss functions for dimension reduction. To achieve the first objective, the CICIDS2017 is considered because it consists of modern attacks on Cloud environment-related. The Stacked Autoencoder with backpropagation and Adam Optimizer algorithms meet the second objective of this paper. For this purpose, to conducting experiments, three activation functions and two-loss functions are considered. The Activation functions Rectified Linear Unit (ReLU), SoftMax, and Scaled Exponential Linear Unit (SeLU) are being used as input/hidden and output layers. For loss functions, Mean Squared Error (MSE) and Cross-Entropy (CE) are chosen. To find the effect of these functions` performance metrics, accuracy, precision, recall, f-measure, and computational time are evaluated with SVM classifier using CICIDS 2017 benchmark dataset. The experimental results show that the ReLU-ReLU-CE yields better accuracy, and the SeLU-SeLU-MSE executes with less computational time.

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Auto-encoder, cloud computing, dimensionality reduction, intrusion detection system, machine learning