ItemArticle

A comprehensive novel model for network speech anomaly detection system using deep learning approach

Title
A comprehensive novel model for network speech anomaly detection system using deep learning approach
Creator
Manimaran A.; Chandramohan D.; Shrinivas S.G.; Arulkumar N.
Description
Network Intrusion Detection System (NIDS) is the key technology for information security, and it plays significant role for classifying various attacks in the networks accurately. An NIDS gains an understanding of normal and anomalous behavior by examining the network traffic and can identify unknown and new attacks. Analyzing and Identifying unfamiliar attacks are one of the big challenges in Network IDS research. A huge response has been given to deep learning over the past several years and novelty in deep learning techniques are also improved regularly. Deep learning based Network Intrusion Detection approach is highly essential for improved performance. Nowadays, Machine learning algorithms made a revolution in the area of human computer interaction and achieved significant advancement in imitating human brain exactly. Convolutional Neural Network (CNN) is a powerful learning algorithm in deep learning model for improving the machine learning ability in order to achieve high attack classification accuracy and low false alarm rate. In this article, an overview of deep learning methodologies for commonly used NIDS such as Auto Encoder (AE), Deep Belief Network (DBN), Deep Neural Network (DNN), Restricted Boltzmann Machine (RBN). Moreover, the article introduces the most recent work on network anomaly detection using deep learning techniques for better understanding to choose appropriate method while implementing NIDS through widespread literature analysis. The experimental results designate that the accuracy, false alarm rate, and timeliness of the proposed CNN-NIDS model are superior than the traditional algorithms. 2020, Springer Science+Business Media, LLC, part of Springer Nature.
Source
International Journal of Speech Technology, Vol-23, No. 2, pp. 305-313.
Date
2020-01-01
Publisher
Springer
Subject
Attack classification; Deep learning; Network intrusion detection system
Coverage
Manimaran A., Department of Computer Applications, Madanapalle Institute of Technology & Science, Madanapalle, India; Chandramohan D., Department of Computer Science & Engineering, Madanapalle Institute of Technology & Science, Madanapalle, India; Shrinivas S.G., Department of Computer Applications, Madanapalle Institute of Technology & Science, Madanapalle, India; Arulkumar N., Department of Computer Science, Christ (Deemed to be University), Bangalore, India
Rights
Restricted Access
Relation
ISSN: 13812416; CODEN: ISTEF
Format
Online
Language
English
Type
Article

Collection

Articles

Citation

Manimaran A.; Chandramohan D.; Shrinivas S.G.; Arulkumar N., “A comprehensive novel model for network speech anomaly detection system using deep learning approach,” CHRIST (Deemed To Be University) Institutional Repository, accessed February 24, 2025, https://archives.christuniversity.in/items/show/16259.

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