Intrusion detection system using autoencoder based deep neural network for SME cybersecurity

Khaizuran Aqhar, Ubaidillah and Syifak Izhar, Hisham and Ferda, Ernawan and Badshah, Gran and Suharto, Edy (2021) Intrusion detection system using autoencoder based deep neural network for SME cybersecurity. In: Proceedings - International Conference on Informatics and Computational Sciences. 5th International Conference on Informatics and Computational Sciences, ICICos 2021 , 24 - 25 November 2021 , Semarang. pp. 210-215., Volume 2021-November. ISSN 2767-7087 ISBN 978-166543807-0 (Published)

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Abstract

This paper proposes an intermediate solution using artificial intelligence to monitor any potential threat for SME, specifically in Malaysia. The proposed method uses Autoencoder based Deep Neural Network (AEDNN) trained with NSL-KDD dataset to efficiently detect possible cyber threats. This paper proposed AEDNN to detect automated threats cybersecurity and it does not intend to replace any existing security solutions. The proposed AEDNN is designed to detect any possible cyber threats accurately and consistently in the real-time network. The experimental results show that accurate results in the range between 96% to 99% specifically for SMEs in Malaysia.

Item Type:	Conference or Workshop Item (Lecture)
Additional Information:	Indexed by Scopus
Uncontrolled Keywords:	Cyber threats; Cybersecurity; Deep neural network; Detection system; Network monitoring; Security
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science Q Science > QA Mathematics > QA76 Computer software T Technology > T Technology (General) T Technology > TA Engineering (General). Civil engineering (General)
Faculty/Division:	Faculty of Computing
Depositing User:	Mr Muhamad Firdaus Janih@Jaini
Date Deposited:	30 Oct 2024 04:35
Last Modified:	30 Oct 2024 04:35
URI:	http://umpir.ump.edu.my/id/eprint/42366
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