We are working to develop a real-time Network Intrusion Detection System using machine learning. Different tools are used to detect different types of malicious traffic. However, new attacks are invented at a high rate by just a small change in pre-existing malware. It is then required to ana
Real Time Network Intrusion Detection System Using Machine Learning
We are working to develop a real-time Network Intrusion Detection System using machine learning.
Different tools are used to detect different types of malicious traffic. However, new attacks are invented at a high rate by just a small change in pre-existing malware. It is then required to analyze the signature of new malware to detect. A more general solution is needed that can detect and predict new malware. This is where Machine Learning (ML) becomes useful. An ML-model can be trained from a data-set with malicious and normal traffic. This model can then be used along CICflowmeter to detect new malicious traffic.
The aim of the project is to develop a system that protects the networks from any type of exploitation.
Objectives of the Project are:
A modern approach to the real-world problems of network security. It will help the administrator to monitor network flow and get alerts for any type of intrusion.
It will also help the researchers to integrate their model and test in a real environment using this application.
The final deliverable will be a Linux based desktop application that will detect intrusion.
| Item Name | Type | No. of Units | Per Unit Cost (in Rs) | Total (in Rs) |
|---|---|---|---|---|
| Network broker | Equipment | 1 | 18000 | 18000 |
| Miscellaneous | Miscellaneous | 1 | 10000 | 10000 |
| Total in (Rs) | 28000 |
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