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

• 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; http://umpir.ump.edu.my/id/eprint/42366/; https://doi.org/10.1109/ICICoS53627.2021.9651851

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...