Citation

Azmi, Muhammad Fariz and Abdul Karim, Hezerul and AlDahoul, Nouar (2022) Anomaly Detection For Network Security. None. (Unpublished)

Text MMUPaper3 SASSP 2022 Muhammad Fariz.pdf Restricted to Repository staff only Download (222kB)

Abstract

Network security is critical these days as network technology advances quickly and internet technology advances even faster, particularly since network threats increase daily. Network anomaly detection is one of the techniques that can be used to protect the security of a network. There has been a lot of recent research on approaches for detecting anomalies. This article presents a unique method for detecting network anomalies that makes use of the Autoencoder model. This proposed method has been shown to be applicable to time series data. The traffic data is determined to be normal or anomaly by comparing the reconstruction error to the threshold value. Referring to the real modern large scale network traffic data, UNSW-NB 15 and CICIDS2017 data set is chosen to evaluate the performance of the proposed Autoencoder model.

Item Type:	Other
Uncontrolled Keywords:	Anomaly detection, Autoencoder, UNSW NB-15, CICDS2017.
Subjects:	Q Science > QA Mathematics > QA71-90 Instruments and machines > QA75.5-76.95 Electronic computers. Computer science
Divisions:	Faculty of Engineering (FOE)
Depositing User:	Dr. Sarina Mansor
Date Deposited:	30 Nov 2022 04:20
Last Modified:	30 Nov 2022 04:20
URII:	http://shdl.mmu.edu.my/id/eprint/10661

Downloads

Edit (login required)