• Anirudh S
• Anagha K T P
• Karthik A P
• Nivin K S
• Daya Naik

This paper introduces a Python and Flask-based Intrusion Detection System (IDS) designed for real-time cybersecurity by analyzing network traffic using machine learning to detect and alert users of potential intrusions. Key features include data collection, processing, and email notifications for alerts, demonstrating high accuracy, low false positives, and efficient scalability. Ethical considerations, such as data privacy, user consent, and legal compliance, are addressed to ensure responsible deployment. The system integrates emerging technologies, including machine learning, cloud computing, blockchain, IoT, and big data analytics, making it applicable across sectors such as finance, healthcare, government, and critical infrastructure. This IDS offers a robust and scalable solution to address evolving cyber threats.

Intrusion Detection System, Machine Learning, Network Security, Anomly Detection,Denial-Of-Service(DOS)

"Intrusion Detection System Using Machine Learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.12, Issue 7, page no.733-740, July-2025, Available :http://www.jetir.org/papers/JETIRGX06137.pdf

"Intrusion Detection System Using Machine Learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.12, Issue 7, page no. pp733-740, July-2025, Available at : http://www.jetir.org/papers/JETIRGX06137.pdf