Network Intrusion Detection based on Amino Acid Sequence Structure Using Machine Learning

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dc.contributor.authorIbaisi, Thaer AL
dc.contributor.authorKuhn, Stefan
dc.contributor.authorKaiiali, Mustafa
dc.contributor.authorKazim, Muhammad
dc.date.acceptance2023-10-13
dc.date.accessioned2023-11-09T09:33:33Z
dc.date.available2023-11-09T09:33:33Z
dc.date.issued2023-10-17
dc.descriptionopen access article
dc.description.abstractThe detection of intrusions in computer networks, known as Network-Intrusion-Detection Systems (NIDSs), is a critical field in network security. Researchers have explored various methods to design NIDSs with improved accuracy, prevention measures, and faster anomaly identification. Safeguarding computer systems by quickly identifying external intruders is crucial for seamless business continuity and data protection. Recently, bioinformatics techniques have been adopted in NIDSs’ design, enhancing their capabilities and strengthening network security. Moreover, researchers in computer science have found inspiration in molecular biology’s survival mechanisms. These nature-designed mechanisms offer promising solutions for network security challenges, outperforming traditional techniques and leading to better results. Integrating these nature-inspired approaches not only enriches computer science, but also enhances network security by leveraging the wisdom of nature’s evolution. As a result, we have proposed a novel Amino-acid-encoding mechanism that is bio-inspired, utilizing essential Amino acids to encode network transactions and generate structural properties from Amino acid sequences. This mechanism offers advantages over other methods in the literature by preserving the original data relationships, achieving high accuracy of up to 99%, transforming original features into a fixed number of numerical features using bio-inspired mechanisms, and employing deep machine learning methods to generate a trained model capable of efficiently detecting network attack transactions in real-time.
dc.funderNo external funder
dc.identifier.citationIbaisi, T.A.; Kuhn, S.; Kaiiali, M.; Kazim, M. (2023) Network Intrusion Detection Based on Amino Acid Sequence Structure Using Machine Learning. Electronics, 12, 4294
dc.identifier.doihttps://doi.org/10.3390/electronics12204294
dc.identifier.urihttps://hdl.handle.net/2086/23339
dc.language.isoen
dc.peerreviewedYes
dc.publisherMDPI
dc.researchinstituteCyber Technology Institute (CTI)
dc.rightsAttribution 2.0 UK: England & Walesen
dc.rights.urihttp://creativecommons.org/licenses/by/2.0/uk/
dc.subjectIntrusion Detection
dc.subjectAmino Acid Structural Properties
dc.subjectMachine Learning
dc.subjectAmino Acid 18 Encoding
dc.subjectPattern Recognition
dc.subjectSequence-to-Structure Mapping
dc.subjectAmino Acid Signatures
dc.titleNetwork Intrusion Detection based on Amino Acid Sequence Structure Using Machine Learning
dc.typeArticle

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