maliksh7/DeepMAD
Malicious Activity Detection System. Final Year Project. Deep Learning-based solution, which analyses Network Activity sequences to classify whether the certain node is Malicious or Benign. Devising a tool/software which will detect malicious Network Activity Detection using Deep Learning Model. Tools: Python, Neural Network (BERT), Google Colaboratory, PyTorch, Kaggle, Tensorflow, and Flowmeter,
This tool helps network security professionals and IT administrators identify malicious activity on their networks. It takes raw network traffic data, usually captured in a .pcap file, analyzes it for patterns, and then classifies network nodes as either malicious or benign. The output is a log file detailing any detected malicious activity.
No commits in the last 6 months.
Use this if you need an automated system to monitor network traffic and detect potential threats or unauthorized activities using deep learning.
Not ideal if you need a real-time, production-ready intrusion detection system, as this project is currently a work in progress and not yet fully deployed.
Stars
13
Forks
3
Language
Python
License
GPL-2.0
Category
Last pushed
Sep 27, 2024
Commits (30d)
0
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