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c0bfb4b0e1 | |
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4f02522166 |
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@ -0,0 +1,17 @@
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# Fail2ban Analyze
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Analyze fail2ban logs to find most common ip's and plot histogram of access rates.
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## Example usage
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Plot the histogram of the access hits
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```
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zcat -f /var/log/fail2ban.log* | ./fail2ban-analyze.py histogram
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```
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Rank IPs by bans, first 20 results
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```
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zcat -f /var/log/fail2ban.log* | ./fail2ban-analyze.py rank 4 --only-bans --count 20
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```
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Rank all IPs by bans, clobber by first 2 subnets
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```
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zcat -f /var/log/fail2ban.log* | ./fail2ban-analyze.py rank 2 --only-bans --count 20
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```
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@ -75,7 +75,9 @@ else:
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num_buckets = (max_date - min_date).total_seconds() / (bucket_size * 3600)
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num_buckets = (max_date - min_date).total_seconds() / (bucket_size * 3600)
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num_buckets = max(1, int(num_buckets))
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num_buckets = max(1, int(num_buckets))
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counts, bin_edges = np.histogram(timestamps, bins=num_buckets)
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counts, bin_edges = np.histogram(timestamps, bins=num_buckets)
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ts_edges = [datetime.fromtimestamp(t) for t in bin_edges]
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labels = [x[0].strftime('%Y-%m-%d %H:%M - ') + x[1].strftime('%Y-%m-%d %H:%M') for x in zip(ts_edges[:-1], ts_edges[1:])]
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fig = tpl.figure()
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fig = tpl.figure()
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fig.hist(counts, bin_edges)
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fig.barh(counts, labels)
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fig.show()
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fig.show()
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