A comparative study between deep learning algorithm and bayesian network on Advanced Persistent Threat (APT) attack detection
Advanced Persistent Threat (APT) attacks are a major concern for the cybersecurity in digital world due to their advanced nature. Attackers are skilful to cause maximal destruction for targeted cyber environment. These APT attacks are also well funded by governments in many cases. The APT atta...
| Main Authors: | , |
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| Format: | Other |
| Language: | English |
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Penerbit UTHM
2021
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| Online Access: | http://eprints.uthm.edu.my/6712/ http://eprints.uthm.edu.my/6712/1/P13606_fc6b8cdc19cf367513bb5fecde8c41eb.pdf |
| _version_ | 1848888889990709248 |
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| author | Ooi, Hui Ni Ab Rahman, Nurul Hidayah |
| author_facet | Ooi, Hui Ni Ab Rahman, Nurul Hidayah |
| author_sort | Ooi, Hui Ni |
| building | UTHM Institutional Repository |
| collection | Online Access |
| description | Advanced Persistent Threat (APT) attacks are a major concern for the
cybersecurity in digital world due to their advanced nature. Attackers are skilful to
cause maximal destruction for targeted cyber environment. These APT attacks are
also well funded by governments in many cases. The APT attacker can achieve his
hostile goals by obtaining information and gaining financial benefits regarding the
infrastructure of a network. It is highly important to study proper countermeasures to
detect these attacks as early as possible due to sophisticated methods. It is difficult to
detect this type of attack since the network may crash because of high traffic. Hence,
in this study, this research is to study the comparison between Multilayer
Perceptron and Naïve-Bayes of APT attack detection. Since the APT attack is
persistent and permanent presence in the victim system, so minimal false
positive rate (FPR) and high accuracy detection is required to detect the APT
attack detection. Besides, Multilayer Perceptron algorithm has high true
positive rate (TPR) in the detection of APT attack compared to Naïve Bayes
algorithm. This means that Multilayer Perceptron algorithm can detect APT
attack more accurately. Based on the result, it also can conclude that the lower
the false positive rate (FPR), the more accurate to detect APT attack. Lastly,
the research would also help to spread the awareness about the APT intrusion
where it possibly can cause huge damage to everyone. |
| first_indexed | 2025-11-15T20:17:28Z |
| format | Other |
| id | uthm-6712 |
| institution | Universiti Tun Hussein Onn Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T20:17:28Z |
| publishDate | 2021 |
| publisher | Penerbit UTHM |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | uthm-67122022-03-14T02:15:55Z http://eprints.uthm.edu.my/6712/ A comparative study between deep learning algorithm and bayesian network on Advanced Persistent Threat (APT) attack detection Ooi, Hui Ni Ab Rahman, Nurul Hidayah TK Electrical engineering. Electronics Nuclear engineering TK5101-6720 Telecommunication. Including telegraphy, telephone, radio, radar, television Advanced Persistent Threat (APT) attacks are a major concern for the cybersecurity in digital world due to their advanced nature. Attackers are skilful to cause maximal destruction for targeted cyber environment. These APT attacks are also well funded by governments in many cases. The APT attacker can achieve his hostile goals by obtaining information and gaining financial benefits regarding the infrastructure of a network. It is highly important to study proper countermeasures to detect these attacks as early as possible due to sophisticated methods. It is difficult to detect this type of attack since the network may crash because of high traffic. Hence, in this study, this research is to study the comparison between Multilayer Perceptron and Naïve-Bayes of APT attack detection. Since the APT attack is persistent and permanent presence in the victim system, so minimal false positive rate (FPR) and high accuracy detection is required to detect the APT attack detection. Besides, Multilayer Perceptron algorithm has high true positive rate (TPR) in the detection of APT attack compared to Naïve Bayes algorithm. This means that Multilayer Perceptron algorithm can detect APT attack more accurately. Based on the result, it also can conclude that the lower the false positive rate (FPR), the more accurate to detect APT attack. Lastly, the research would also help to spread the awareness about the APT intrusion where it possibly can cause huge damage to everyone. Penerbit UTHM 2021 Other NonPeerReviewed text en http://eprints.uthm.edu.my/6712/1/P13606_fc6b8cdc19cf367513bb5fecde8c41eb.pdf Ooi, Hui Ni and Ab Rahman, Nurul Hidayah (2021) A comparative study between deep learning algorithm and bayesian network on Advanced Persistent Threat (APT) attack detection. Penerbit UTHM, UTHM. https://doi.org/10.30880/aitcs.2021.02.02.015 |
| spellingShingle | TK Electrical engineering. Electronics Nuclear engineering TK5101-6720 Telecommunication. Including telegraphy, telephone, radio, radar, television Ooi, Hui Ni Ab Rahman, Nurul Hidayah A comparative study between deep learning algorithm and bayesian network on Advanced Persistent Threat (APT) attack detection |
| title | A comparative study between deep learning algorithm and bayesian network on Advanced Persistent Threat (APT) attack detection |
| title_full | A comparative study between deep learning algorithm and bayesian network on Advanced Persistent Threat (APT) attack detection |
| title_fullStr | A comparative study between deep learning algorithm and bayesian network on Advanced Persistent Threat (APT) attack detection |
| title_full_unstemmed | A comparative study between deep learning algorithm and bayesian network on Advanced Persistent Threat (APT) attack detection |
| title_short | A comparative study between deep learning algorithm and bayesian network on Advanced Persistent Threat (APT) attack detection |
| title_sort | comparative study between deep learning algorithm and bayesian network on advanced persistent threat (apt) attack detection |
| topic | TK Electrical engineering. Electronics Nuclear engineering TK5101-6720 Telecommunication. Including telegraphy, telephone, radio, radar, television |
| url | http://eprints.uthm.edu.my/6712/ http://eprints.uthm.edu.my/6712/ http://eprints.uthm.edu.my/6712/1/P13606_fc6b8cdc19cf367513bb5fecde8c41eb.pdf |