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

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

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 p...

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Bibliographic Details
Main Authors: Khaizuran Aqhar, Ubaidillah, Syifak Izhar, Hisham, Ferda, Ernawan, Badshah, Gran, Suharto, Edy
Format: Conference or Workshop Item
Language:English
English
Published: Institute of Electrical and Electronics Engineers Inc. 2021
Subjects:
QA75 Electronic computers. Computer science
QA76 Computer software
T Technology (General)
TA Engineering (General). Civil engineering (General)
Online Access:http://umpir.ump.edu.my/id/eprint/42366/1/Intrusion%20detection%20system%20using%20autoencoder%20based.pdf
http://umpir.ump.edu.my/id/eprint/42366/2/Intrusion%20detection%20system%20using%20autoencoder%20based%20deep%20neural%20network%20for%20SME%20cybersecurity_ABS.pdf

Internet

http://umpir.ump.edu.my/id/eprint/42366/1/Intrusion%20detection%20system%20using%20autoencoder%20based.pdf
http://umpir.ump.edu.my/id/eprint/42366/2/Intrusion%20detection%20system%20using%20autoencoder%20based%20deep%20neural%20network%20for%20SME%20cybersecurity_ABS.pdf

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