Graphing SSH dictionary attacks with HighCharts

After my 10-year-old basement Linux server died this week from a power outage, I took the sad step of giving up on it. It’s died before and I’ve patched it back together with a new power supply here or an addon PCI SATA card there, but I finally decided to throw in the towel since I had a newer old computer that had been idle for several years. The one that died was an Athlon K7 750 MHz with 512 MB ram. The new one is an Athlon 2 GHz (3200+) with 1 gig. For my uses, specs don’t really matter that much, but it’s nice to have more power for free.

I put CentOS 6 on it and configured Samba and copied all the data off the old machine and was back up and running within a few hours. Since I forward ports through my FiOS router to this box I did my standard lockdown procedure, including adding myself to the AllowUsers in sshd_config. Afterwards I took a look in /var/log/secure and saw the typical flood of dictionary attacks trying to get in as root or bob or tfeldman or jweisz. I have iptables configured to rate-limit SSH connections to 2 per 5 seconds per IP so the box doesn’t get DoSed out of existence, but some stuff does make it through to sshd.

Looking through /var/log/secure, I got to thinking it would be interesting if there was some way to visualize the attacks in a handy graph. Then I remembered, oh, wait, I can do that.

I wrote a perl script to parse out the attacks from /var/log/secure and insert them into a Postgres DB. This turned out to be pretty easy. Then I thought it would be more interesting to tie the IP of each attack to its originating country. I’ve used MaxMind’s GeoIP DB pretty extensively before, but I was looking something free. That’s when I remembered that MaxMind has a free GeoIP DB: GeoLiteCity. I grabbed it and yum-installed the Perl lib and added the geo data to the attack DB. Rather than worry about normalizing the schema I just shoved the info into the same table. Life is easier this way, and it’s just a for-fun project.

So I got that all working and parsed it against the existing /var/log/secures via

[root@lunix2011 ~]# zcat /var/log/secure-20111117.gz | perl parse-secure.pl 

I wrote ssh.php to see what’s in the table:

ssh.php list of hacking attempts
ssh.php list of hacking attempts

So now that the data was all in place, time to move on to the graphs, which is what I really wanted to do. Last time I wanted to graph data programmatically I used JPGraph, which does everything in PHP and is super versatile. But I wanted something… cooler. Maybe something interactive. A little Googling turned up Highcharts which is absolutely awesome, and does everything in JavaScript. I basically modified some of their example charts and pumped my data into them and got the charts below.

Pie chart of attacks grouped by country for the past 30 days:

Pie chart by country
Pie chart by country

Bar graph of attacks per day:

Bar graph of daily attacks
Bar graph of daily attacks

So, that’s that. Code is in github if anyone wants to play around with it. I’ve cronned parse-secure.pl to run every 5 minutes so the data gets updated automatically.

Exchange (OWA) CAS crashes with 503 error – again

This just started happening again, with these errors appearing in the event viewer:

Log Name: System
Source: Microsoft-Windows-WAS
Date: 9/18/2011 11:16:33 AM
Event ID: 5011
Task Category: None
Level: Warning
Keywords: Classic
User: N/A
Computer: exch2010fe1
Description:
A process serving application pool 'MSExchangeOWAAppPool' suffered a
fatal communication error with the Windows Process Activation Service.
The process id was '3760'. The data field contains the error number.

Log Name: System
Source: Microsoft-Windows-WAS
Date: 9/17/2011 6:47:07 AM
Event ID: 5009
Task Category: None
Level: Warning
Keywords: Classic
User: N/A
Computer: exch2010fe1
Description:
A process serving application pool 'MSExchangeOWAAppPool' terminated
unexpectedly. The process id was '3108'. The process exit code was
'0x800703e9'.

Log Name: Application
Source: Application Error
Date: 9/17/2011 6:46:30 AM
Event ID: 1000
Task Category: (100)
Level: Error
Keywords: Classic
User: N/A
Computer: exch2010fe1
Description:
Faulting application name: w3wp.exe, version: 7.5.7600.16385, time
stamp: 0x4a5bd0eb
Faulting module name: KERNELBASE.dll, version: 6.1.7600.16385, time
stamp: 0x4a5bdfe0
Exception code: 0xe053534f
Fault offset: 0x000000000000aa7d
Faulting process id: 0x%9
Faulting application start time: 0x%10
Faulting application path: %11
Faulting module path: %12
Report Id: %13

After reviewing the IIS logs and the event logs, I think it has to do with the WebReady document viewer – the thing in OWA that renders and lets you view .doc attachments within the browser rather than forcing you to open Word or Excel. I think users were attempting to open corrupted files and that was causing it to crash. I’ve disabled Webready in EMC (Server Config -> CAS) and I’ll see what happens.

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Who pays the most tax? Who pays the least tax?

Why do I care? Maybe I don’t. But this is fun anyway.

Go to the IRS.gov website to the SOI Tax Stats page.

Download one of the Excel spreadsheets under Basic Tables: Returns Filed and Sources of Income. I used All Returns: Adjusted Gross Income, Exemptions, Deductions, and Tax Items for 2008.

Look at spreadsheet columns I through N. I & J show the amount of taxable income in each bracket and the number of returns in that bracket. M & N show the number of returns that had tax and the total amount of tax in each bracket.

At the top of column J, the number is 5,652,925,474. Dollar values are in thousands, so this is about $5.6 trillion of taxable income earned by US taxpayers in 2008. At the top of column N it shows that to total tax paid by all these people is 1,031,580,923, or about $1 trillion. So of all the taxable income earned in 2008, 18.2% was paid in federal income tax.

I added a new column, to the right of N, and in cell O10 entered the formula =N10/N$9. Then I formatted the column to show results as a percent. Column O now shows the percentage of tax paid by each income bracket:

I then added another column to the right of I, and in cell J10 entered the formula =I10/I$9 and formatted it for a percent. Column J now shows the percentage of the population (of taxpayers) in each tax bracket:

With these extra columns it’s pretty easy to see where the tax revenue comes from. Cells P23 through P28 sum to 33.24% of revenue. J23 through J28 sum to 0.8% – meaning that the top 0.8% of taxpayers (those with over $500k taxable income) really do account pay 33% of the tax in the USA. The next 62.2% of tax revenue is paid by 54.5% of the population – those between $40,000 and $500,000. The bottom 44.7% of the population pays 4.5% of the tax revenue.

The different population segments are color coded below:

It’s interesting (and strange) that the 13,400 people who made over $10m in 2008 contributed more revenue than the bottom 54.7% – 59,000,000 people.

The table with my additions is available here:

08in12ms
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