Dirichlet Feature Embedding with Adaptive Long Short-Term Memory Model for Intrusion Detection System
subject: adaptive long short-term memory; Dirichlet feature embedding; false alarm rate; intrusion detection system; NSL-KDD
description: Intrusion Detection System is applied in the network to monitor the network activity and detect the intruder to protect the user data. Various existing models have been applied in the intrusion detection system and have the limitations of high False Alarm Rate (FAR), overfitting problem and data imbalance problem. In this research, Dirichlet Feature Embedding based Adaptive Long Short Term Memory (DFE-LSTM) model is proposed to improve the efficiency of the intrusion detection. The Dirichlet Feature Embedding (DFE) method is applied to effectively represent the feature to analysis the multi-variate of the input data. The enhanced Adaptive Long Short Term Memory (ALSTM) model is applied to select the optimal parameter for the LSTM model to improve the learning rate. The proposed DFE-ALSTM model is compared to three datasets such as UNSW-NB15, NSL-KDD and Kyoto 2006+ for evaluate the efficiency. The proposed DFE-ALSTM model has the accuracy of 94.32 % and existing NB-SVM has 93.75 % accuracy in intrusion detection on UNSW-NB15 dataset. 2022, Success Culture Press. All rights reserved.
creator: Sivakami R.; Uday Kiran G.; Arun M.; David L.G.; Manohar M.
source: Journal of System and Management Sciences, Vol-12, No. 4, pp. 398-412.
publisher: Success Culture Press
date: 2022-01-01
rights: All Open Access; Gold Open Access
relation: ISSN: 18166075
format: Online
language: English
type: Article
coverage: Sivakami R., Department of Computer Science and Engineering, Sona College of Technology, Tamil Nadu, Salem, 636005, India; Uday Kiran G., Department of Computer Science and Engineering, B V Raju Institute of Technology, Telangana, Narsapur, 502313, India; Arun M., Department of Electronics and Communication Engineering, Panimalar Institute of Technology, Chennai, India; David L.G., Department of Visual Communication, Kumaraguru College of Liberal Arts and Science, Coimbatore, India; Manohar M., Department of Computer Science and Engineering, CHRIST (Deemed to be University), Karnataka, Bangalore, 560029, India