A Machine Learning-Based Intrusion Detection Algorithm for Securing Bioinformatics Pipelines
-д хадгалсан:
| -д хэвлэсэн: | International Conference on Cyber Warfare and Security (Mar 2025), p. 345 |
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| Үндсэн зохиолч: | |
| Бусад зохиолчид: | , , , , , , , , |
| Хэвлэсэн: |
Academic Conferences International Limited
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| Нөхцлүүд: | |
| Онлайн хандалт: | Citation/Abstract Full Text Full Text - PDF |
| Шошгууд: |
Шошго байхгүй, Энэхүү баримтыг шошголох эхний хүн болох!
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MARC
| LEADER | 00000nab a2200000uu 4500 | ||
|---|---|---|---|
| 001 | 3202190727 | ||
| 003 | UK-CbPIL | ||
| 035 | |a 3202190727 | ||
| 045 | 2 | |b d20250301 |b d20250331 | |
| 084 | |a 142229 |2 nlm | ||
| 100 | 1 | |a Osamor, Jude |u Cyblack, Manchester, UK | |
| 245 | 1 | |a A Machine Learning-Based Intrusion Detection Algorithm for Securing Bioinformatics Pipelines | |
| 260 | |b Academic Conferences International Limited |c Mar 2025 | ||
| 513 | |a Conference Proceedings | ||
| 520 | 3 | |a Bioinformatics pipelines, which process vast amounts of sensitive biological data, are increasingly targeted by cyberattacks. Traditional security measures often fail to provide adequate protection due to the unique computational and network characteristics of these pipelines. This study proposes a machine learning-based Intrusion Detection System (IDS) tailored specifically for bioinformatics workflows. While the CICIDS2017 dataset serves as the primary benchmark, we augment the study with bioinformatics-specific network traffic to ensure relevance. We compare the performance of four machine learning algorithms Random Forest (RF), Support Vector Machine (SVM), Convolutional Neural Network (CNN), and Gradient Boosting Machine (GBM) and explore hybrid models for enhanced detection. Our findings highlight GBM's superior accuracy (98.3%) while also addressing its computational overhead and susceptibility to adversarial attacks. The study contributes novel insights by integrating real-world bioinformatics traffic data and proposing adaptive security strategies for genomic research environments. | |
| 653 | |a Machine learning | ||
| 653 | |a Accuracy | ||
| 653 | |a Deep learning | ||
| 653 | |a Datasets | ||
| 653 | |a Security | ||
| 653 | |a Bioinformatics | ||
| 653 | |a Support vector machines | ||
| 653 | |a Pipelines | ||
| 653 | |a Communications traffic | ||
| 653 | |a Artificial neural networks | ||
| 653 | |a Neural networks | ||
| 653 | |a Cybersecurity | ||
| 653 | |a Data processing | ||
| 653 | |a Feature selection | ||
| 653 | |a Algorithms | ||
| 653 | |a Workloads | ||
| 653 | |a Efficiency | ||
| 653 | |a Intrusion detection systems | ||
| 653 | |a Susceptibility | ||
| 653 | |a Genomics | ||
| 653 | |a Medical informatics | ||
| 653 | |a Networks | ||
| 653 | |a Social networks | ||
| 653 | |a Intrusion | ||
| 700 | 1 | |a Yisa, Aliyu |u Cyblack, Manchester, UK | |
| 700 | 1 | |a Olanipekun, Febisola |u Cyblack, Manchester, UK | |
| 700 | 1 | |a Olowosule, Omotolani |u Cyblack, Manchester, UK | |
| 700 | 1 | |a Akerele, Samuel |u Cyblack, Manchester, UK | |
| 700 | 1 | |a Anyalechi, Onyekachi | |
| 700 | 1 | |a Sadiq, Simbiat | |
| 700 | 1 | |a Akerele, Iretioluwa | |
| 700 | 1 | |a Palmer, Xavier | |
| 700 | 1 | |a Barnett, Michaela | |
| 773 | 0 | |t International Conference on Cyber Warfare and Security |g (Mar 2025), p. 345 | |
| 786 | 0 | |d ProQuest |t Political Science Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3202190727/abstract/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text |u https://www.proquest.com/docview/3202190727/fulltext/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3202190727/fulltextPDF/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |