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Traditional signature-based detection methods are ineffective against zero-day attacks and advanced persistent threats. This paper proposes a machine learning-based approach for cyber threat detection. Our approach uses a combination of supervised and unsupervised learning algorithms to detect anomalies in network traffic. Experimental results demonstrate the effectiveness of our approach in detecting cyber threats with high accuracy.\n\nKeywords: Cyber Threat Detection, Machine Learning, Artificial Intelligence, Cybersecurity, Network Security, Anomaly Detection, Predictive Analytics\n\n### Project include: \n\n1. Synopsis\n\n2. PPT\n\n3. Research Paper\n\n\n4. Code\n\n5. Explanation video\n\n6. Documents\n\n7. Report\n\n\n### Need Code, Documents \u0026 Explanation video ? \n\n## How to Reach me :\n\n### Mail : vatshayan007@gmail.com \n\n### WhatsApp: +91 9310631437 (Helping 24*7) **[CHAT](https://wa.me/message/CHWN2AHCPMAZK1)** \n\n### Website : https://www.finalproject.in/\n\n### Contact me for any kind of help on projects.\n### 1000 Computer Science Projects : https://www.computer-science-project.in/\n\n\nMail/Message me for Projects Help 🙏🏻\n","funding_links":[],"categories":[],"sub_categories":[],"project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fprojects-developer%2Fcyber-threat-detection-using-machine-learning","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fprojects-developer%2Fcyber-threat-detection-using-machine-learning","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fprojects-developer%2Fcyber-threat-detection-using-machine-learning/lists"}