Accurate detection of network anomalies within SNMP-MIB data set using deep learning
by Ghazi Al-Naymat; Hanan Hussain; Mouhammd Al-Kasassbeh; Nidal Al-Dmour
International Journal of Computer Applications in Technology (IJCAT), Vol. 66, No. 1, 2021

Abstract: An efficient algorithm for supporting the Intrusion Detection System is required for identifying unauthorised access that attempts to collapse a computer network's features. Machine Learning (ML) approaches like MLP and SVM Classifiers showed higher accuracy when the additional feature selection techniques are used. Another ML approach called Deep Learning (DL) algorithm does the feature selection, automatically to overcome the extra computation of feature selection. In this paper, DL method called Stacked Autoencoder (SA) is proposed for detecting known network anomalies using the SNMP-MIB data. SA transforms the set of inputs to a different set of reduced outputs (encoding). Previous outputs are decoded to get the desired output of n dimension identical to the initial input. The proposed DL method attains a high accuracy of 100% and saves the extra computations and resources spent on feature selection. The proposed model was compared with 22 ML techniques and found to outperform all other all algorithms.

Online publication date: Sat, 11-Dec-2021

The full text of this article is only available to individual subscribers or to users at subscribing institutions.

Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.

Pay per view:
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.

Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Computer Applications in Technology (IJCAT):
Login with your Inderscience username and password:

    Username:        Password:         

Forgotten your password?

Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.

If you still need assistance, please email