💡[Feature]: Cyber Attack Prediction (Random Forest)

[AMAN1011011]

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Feature Description

Implementing machine learning algorithms for cyber attack detection involves analyzing network data to identify potential threats by finding correlations between variables. This approach enhances the accuracy and efficiency of detecting cyber attacks, thereby improving the security of digital networks and systems.

Use Case

Towards improved cyber security Some use cases are as follows:

Intrusion Detection Phishing Detection Malware Detection Network Traffic Analysis Endpoint Security User and Entity Behavior Analytics Threat Intelligence

Benefits

• Improved Accuracy as Machine learning models can analyze vast amounts of data and detect subtle patterns that traditional methods might miss, leading to more accurate threat detection.
• Real-time Detection Machine learning can process data in real-time, allowing for the immediate identification and response to cyber threats, minimizing potential damage.
• Reduced False Positives by learning from historical data, machine learning models can distinguish between legitimate activities and actual threats, reducing the number of false positives.

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Priority

High

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• [X] I'm a GSSOC'24 contributor
• [X] I want to work on this issue

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[AMAN1011011]

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[github-actions[bot]]

Hello @AMAN1011011! Your issue #720 has been closed. Thank you for your contribution!