Detection of Unknown Polymorphic Patterns Using Feature-Extracting Part of a Convolutional Autoencoder
Background: The present paper proposes a novel approach for detecting the presence of unknown polymorphic patterns in random symbol sequences that also comprise already known polymorphic patterns. Methods: We propose to represent rules that define the considered patterns as regular expressions and s...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-09-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/13/19/10842 |
| _version_ | 1797576163837607936 |
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| author | Przemysław Kucharski Krzysztof Ślot |
| author_facet | Przemysław Kucharski Krzysztof Ślot |
| author_sort | Przemysław Kucharski |
| collection | DOAJ |
| description | Background: The present paper proposes a novel approach for detecting the presence of unknown polymorphic patterns in random symbol sequences that also comprise already known polymorphic patterns. Methods: We propose to represent rules that define the considered patterns as regular expressions and show how these expressions can be modeled using filter cascades of neural convolutional layers. We adopted a convolutional autoencoder (CAE) as a pattern detection framework. To detect unknown patterns, we first incorporated knowledge of known rules into the CAE’s convolutional feature extractor by fixing weights in some of its filter cascades. Then, we executed the learning procedure, where the weights of the remaining filters were driven by two different objectives. The first was to ensure correct sequence reconstruction, whereas the second was to prevent weights from learning the already known patterns. Results: The proposed methodology was tested on sample sequences derived from the human genome. The analysis of the experimental results provided statistically significant information on the presence or absence of polymorphic patterns that were not known in advance. Conclusions: The proposed method was able to detect the existence of unknown polymorphic patterns. |
| first_indexed | 2024-03-10T21:49:19Z |
| format | Article |
| id | doaj.art-263ec4ad4af84958b35594fb55c8b504 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T21:49:19Z |
| publishDate | 2023-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-263ec4ad4af84958b35594fb55c8b5042023-11-19T14:04:57ZengMDPI AGApplied Sciences2076-34172023-09-0113191084210.3390/app131910842Detection of Unknown Polymorphic Patterns Using Feature-Extracting Part of a Convolutional AutoencoderPrzemysław Kucharski0Krzysztof Ślot1Institute of Applied Computer Science, Lodz University of Technology, Stefanowskiego 18, 90-537 Lodz, PolandInstitute of Applied Computer Science, Lodz University of Technology, Stefanowskiego 18, 90-537 Lodz, PolandBackground: The present paper proposes a novel approach for detecting the presence of unknown polymorphic patterns in random symbol sequences that also comprise already known polymorphic patterns. Methods: We propose to represent rules that define the considered patterns as regular expressions and show how these expressions can be modeled using filter cascades of neural convolutional layers. We adopted a convolutional autoencoder (CAE) as a pattern detection framework. To detect unknown patterns, we first incorporated knowledge of known rules into the CAE’s convolutional feature extractor by fixing weights in some of its filter cascades. Then, we executed the learning procedure, where the weights of the remaining filters were driven by two different objectives. The first was to ensure correct sequence reconstruction, whereas the second was to prevent weights from learning the already known patterns. Results: The proposed methodology was tested on sample sequences derived from the human genome. The analysis of the experimental results provided statistically significant information on the presence or absence of polymorphic patterns that were not known in advance. Conclusions: The proposed method was able to detect the existence of unknown polymorphic patterns.https://www.mdpi.com/2076-3417/13/19/10842polymorphic pattern detectionknowledge and learning integrationconvolutional autoencoder |
| spellingShingle | Przemysław Kucharski Krzysztof Ślot Detection of Unknown Polymorphic Patterns Using Feature-Extracting Part of a Convolutional Autoencoder Applied Sciences polymorphic pattern detection knowledge and learning integration convolutional autoencoder |
| title | Detection of Unknown Polymorphic Patterns Using Feature-Extracting Part of a Convolutional Autoencoder |
| title_full | Detection of Unknown Polymorphic Patterns Using Feature-Extracting Part of a Convolutional Autoencoder |
| title_fullStr | Detection of Unknown Polymorphic Patterns Using Feature-Extracting Part of a Convolutional Autoencoder |
| title_full_unstemmed | Detection of Unknown Polymorphic Patterns Using Feature-Extracting Part of a Convolutional Autoencoder |
| title_short | Detection of Unknown Polymorphic Patterns Using Feature-Extracting Part of a Convolutional Autoencoder |
| title_sort | detection of unknown polymorphic patterns using feature extracting part of a convolutional autoencoder |
| topic | polymorphic pattern detection knowledge and learning integration convolutional autoencoder |
| url | https://www.mdpi.com/2076-3417/13/19/10842 |
| work_keys_str_mv | AT przemysławkucharski detectionofunknownpolymorphicpatternsusingfeatureextractingpartofaconvolutionalautoencoder AT krzysztofslot detectionofunknownpolymorphicpatternsusingfeatureextractingpartofaconvolutionalautoencoder |