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...
| Main Authors: | , , , , |
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| Format: | Conference or Workshop Item |
| Language: | English English |
| Published: |
Institute of Electrical and Electronics Engineers Inc.
2021
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| Subjects: | |
| Online Access: | http://umpir.ump.edu.my/id/eprint/42366/ 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 |
| _version_ | 1848826591755370496 |
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| author | Khaizuran Aqhar, Ubaidillah Syifak Izhar, Hisham Ferda, Ernawan Badshah, Gran Suharto, Edy |
| author_facet | Khaizuran Aqhar, Ubaidillah Syifak Izhar, Hisham Ferda, Ernawan Badshah, Gran Suharto, Edy |
| author_sort | Khaizuran Aqhar, Ubaidillah |
| building | UMP Institutional Repository |
| collection | Online Access |
| description | 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. |
| first_indexed | 2025-11-15T03:47:15Z |
| format | Conference or Workshop Item |
| id | ump-42366 |
| institution | Universiti Malaysia Pahang |
| institution_category | Local University |
| language | English English |
| last_indexed | 2025-11-15T03:47:15Z |
| publishDate | 2021 |
| publisher | Institute of Electrical and Electronics Engineers Inc. |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | ump-423662024-10-30T04:35:44Z http://umpir.ump.edu.my/id/eprint/42366/ Intrusion detection system using autoencoder based deep neural network for SME cybersecurity Khaizuran Aqhar, Ubaidillah Syifak Izhar, Hisham Ferda, Ernawan Badshah, Gran Suharto, Edy QA75 Electronic computers. Computer science QA76 Computer software T Technology (General) TA Engineering (General). Civil engineering (General) 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. Institute of Electrical and Electronics Engineers Inc. 2021 Conference or Workshop Item PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/42366/1/Intrusion%20detection%20system%20using%20autoencoder%20based.pdf pdf en http://umpir.ump.edu.my/id/eprint/42366/2/Intrusion%20detection%20system%20using%20autoencoder%20based%20deep%20neural%20network%20for%20SME%20cybersecurity_ABS.pdf 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) https://doi.org/10.1109/ICICoS53627.2021.9651851 |
| spellingShingle | QA75 Electronic computers. Computer science QA76 Computer software T Technology (General) TA Engineering (General). Civil engineering (General) Khaizuran Aqhar, Ubaidillah Syifak Izhar, Hisham Ferda, Ernawan Badshah, Gran Suharto, Edy Intrusion detection system using autoencoder based deep neural network for SME cybersecurity |
| title | Intrusion detection system using autoencoder based deep neural network for SME cybersecurity |
| title_full | Intrusion detection system using autoencoder based deep neural network for SME cybersecurity |
| title_fullStr | Intrusion detection system using autoencoder based deep neural network for SME cybersecurity |
| title_full_unstemmed | Intrusion detection system using autoencoder based deep neural network for SME cybersecurity |
| title_short | Intrusion detection system using autoencoder based deep neural network for SME cybersecurity |
| title_sort | intrusion detection system using autoencoder based deep neural network for sme cybersecurity |
| topic | QA75 Electronic computers. Computer science QA76 Computer software T Technology (General) TA Engineering (General). Civil engineering (General) |
| url | http://umpir.ump.edu.my/id/eprint/42366/ http://umpir.ump.edu.my/id/eprint/42366/ 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 |