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| Format: | Recurso digital |
| Language: | English |
| Published: |
Zenodo
2026
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| Online Access: | https://doi.org/10.5281/zenodo.20390900 |
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| _version_ | 1866902228796702720 |
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| author | Soni, Ankit Kumar |
| author_facet | Soni, Ankit Kumar |
| contents | <p>This research paper presents a machine learning based approach for cyber intrusion detection using K-Nearest Neighbors (KNN) and Logistic Regression algorithms. The study focuses on identifying malicious network activities and improving cybersecurity threat detection accuracy. Different machine learning techniques, data preprocessing methods, and performance evaluation metrics are applied to analyze intrusion patterns effectively. The experimental results demonstrate the importance of machine learning in modern cyber security systems for detecting network attacks efficiently and accurately.</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_20390900 |
| institution | Zenodo |
| language | eng |
| publishDate | 2026 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Cyber Intrusion Detection using K-Nearest Neighbors and Logistic Regression Soni, Ankit Kumar <p>This research paper presents a machine learning based approach for cyber intrusion detection using K-Nearest Neighbors (KNN) and Logistic Regression algorithms. The study focuses on identifying malicious network activities and improving cybersecurity threat detection accuracy. Different machine learning techniques, data preprocessing methods, and performance evaluation metrics are applied to analyze intrusion patterns effectively. The experimental results demonstrate the importance of machine learning in modern cyber security systems for detecting network attacks efficiently and accurately.</p> |
| title | Cyber Intrusion Detection using K-Nearest Neighbors and Logistic Regression |
| url | https://doi.org/10.5281/zenodo.20390900 |