Unsupervised learning using ML for network intrusion
$begingroup$
I want to use unsupervised machine learning to classify network traffic as good and bad.
For that, I have an unlabeled attack dataset downloaded from here: http://kdd.ics.uci.edu/databases/kddcup99/kddcup99.html.
So far I have seen and read that ML allows you to create clusters of data which is similar.
So, my question is that how can I tell whether a certain cluster represents attack data points and another cluster represents normal traffic?
Is that something you need to figure out yourself or machine learning magically tells you that certain cluster represents attack traffic?
machine-learning unsupervised-learning
asked 17 hours ago
cpxcpx
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$endgroup$
add a comment |
$begingroup$
I want to use unsupervised machine learning to classify network traffic as good and bad.
For that, I have an unlabeled attack dataset downloaded from here: http://kdd.ics.uci.edu/databases/kddcup99/kddcup99.html.
So far I have seen and read that ML allows you to create clusters of data which is similar.
So, my question is that how can I tell whether a certain cluster represents attack data points and another cluster represents normal traffic?
Is that something you need to figure out yourself or machine learning magically tells you that certain cluster represents attack traffic?
machine-learning unsupervised-learning
asked 17 hours ago
cpxcpx
1011
New contributor
cpx is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
add a comment |
$begingroup$
I want to use unsupervised machine learning to classify network traffic as good and bad.
For that, I have an unlabeled attack dataset downloaded from here: http://kdd.ics.uci.edu/databases/kddcup99/kddcup99.html.
So far I have seen and read that ML allows you to create clusters of data which is similar.
So, my question is that how can I tell whether a certain cluster represents attack data points and another cluster represents normal traffic?
Is that something you need to figure out yourself or machine learning magically tells you that certain cluster represents attack traffic?
machine-learning unsupervised-learning
asked 17 hours ago
cpxcpx
1011
New contributor
cpx is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
I want to use unsupervised machine learning to classify network traffic as good and bad.
For that, I have an unlabeled attack dataset downloaded from here: http://kdd.ics.uci.edu/databases/kddcup99/kddcup99.html.
So far I have seen and read that ML allows you to create clusters of data which is similar.
So, my question is that how can I tell whether a certain cluster represents attack data points and another cluster represents normal traffic?
Is that something you need to figure out yourself or machine learning magically tells you that certain cluster represents attack traffic?
machine-learning unsupervised-learning
machine-learning unsupervised-learning
asked 17 hours ago
cpxcpx
1011
New contributor
cpx is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 17 hours ago
cpxcpx
1011
New contributor
cpx is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 17 hours ago
cpxcpx
1011
New contributor
cpx is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 17 hours ago
cpxcpx
1011
asked 17 hours ago
cpxcpx
1011
1011
New contributor
cpx is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
New contributor
cpx is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
cpx is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
add a comment |
add a comment |
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