Ai in Cyber Security
Fuente:
Zenodo
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Recurso digital |
| Veröffentlicht: |
Zenodo
2026
|
| Online-Zugang: | |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| _version_ | 1866901536606519296 |
|---|---|
| author | MALVIYA, GAUTAM |
| author_facet | MALVIYA, GAUTAM |
| contents | <p>This research paper focuses on phishing detection using machine learning techniques in the field of cybersecurity. Phishing attacks are one of the most common cyber threats, targeting users through fake websites and emails to steal sensitive information.</p> <p>The study explores how machine learning algorithms can be used to identify and classify phishing websites more accurately compared to traditional methods. The research includes data preprocessing, feature selection, and model training using publicly available datasets.</p> <p>The goal of this work is to improve detection accuracy and reduce false positives in phishing identification systems. The paper also discusses the implementation approach and evaluates the effectiveness of different machine learning models in detecting malicious activities.</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_19953677 |
| institution | Zenodo |
| language | |
| publishDate | 2026 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Ai in Cyber Security MALVIYA, GAUTAM <p>This research paper focuses on phishing detection using machine learning techniques in the field of cybersecurity. Phishing attacks are one of the most common cyber threats, targeting users through fake websites and emails to steal sensitive information.</p> <p>The study explores how machine learning algorithms can be used to identify and classify phishing websites more accurately compared to traditional methods. The research includes data preprocessing, feature selection, and model training using publicly available datasets.</p> <p>The goal of this work is to improve detection accuracy and reduce false positives in phishing identification systems. The paper also discusses the implementation approach and evaluates the effectiveness of different machine learning models in detecting malicious activities.</p> |
| title | Ai in Cyber Security |
| url | https://doi.org/10.5281/zenodo.19953677 |