manually inspect 60 million packets

Dear pcapr users,
As you already know, pcapr has become the largest online repository for packet captures, with over 60 million packets online. With 420+ protocols and 2800+ distinct packet captures, pcapr has become a reference platform for those that seek packet traces and want to collaborate on them. We have global service providers, government agencies, networking vendors, IT and security folks as active users and we continue to see new users rapidly embrace this platform.

Recently, we embarked on transforming all of the pcaps into the pcap-ng format, which is capable of storing meta data into each capture. We thought it would be really cool to tag these packet captures with the user that uploaded it into pcapr, but made a grave error.

Through a not-so-common programming mistake (off-by-31337 error), each of the 60 million packets was tagged with *every* pcapr user's email as well as the password. While we never stored your password in the clear, the MD5-hash of your password in the meta data is prone to brute-force cracking attacks to reveal the plain text password. We are terribly sorry for this grave mistake and we are taking all the right measures to ensure that this will never happen again. We are also stunting innovation across the entire pcapr team and rolling back all of the pcap-ng migration efforts.

We highly recommend that you manually inspect each of the 60 million packets on pcapr to ensure that your credentials are safe. We deeply apologize for the inconvenience and we sincerely hope that this will not discourage your future pcapr experience.

Thanks,
The Pcapr Team
April 1, 2011
http://www.pcapr.net
http://twitter.com/pcapr
http://labs.mudynamics.com

pdb – Interactive Debugger - Python Module of the Week

pdb – Interactive Debugger - Python Module of the Week

Turnkey Linux appliances

For all those times you don't want to do your own split horizon DNS configs....

in reference to: Turnkey Linux Virtual Appliance Library | Best of Open Source: Simplified (view on Google Sidewiki)

How much does it usually rain ?

Cut the weather marketing crap.
How much does it usually rain ?

in reference to: NCDC: U.S. Normals - Products (view on Google Sidewiki)

How much has it actually rained ?

Cut through all the weather marketing.
How much as it actually rained ?
(he says, having camped 3 weekends in
October in the pouring rain, currently listening
to more rain outside)

in reference to: AHPS Precipitation Analysis (view on Google Sidewiki)

Google Visualization API Gallery - Google Visualization API - Google Code

Google Visualization API Gallery - Google Visualization API - Google Code

Yadis! Backup Software

Yadis! Backup Software

Need to compute sunspot frequencies ? Here's some code for you !

#! /usr/bin/python
# $Id: sunspots.py,v 1.1 2009/05/31 12:08:08 george Exp george $
#
# Plot sunspot data.
#
# Adapted from
#
#   http://linuxgazette.net/115/andreasen.html
#
# this was the original program.  Dataset from
#
#   http://linuxgazette.net/115/misc/andreasen/sunspots.dat
#
# Nice examples of using the newer plotting libraries can be found here:
#
#   http://www.daniweb.com/code/snippet691.html
#
# History:
#   Created: George Jones , 5/30/09
#
#   $Log: sunspots.py,v $
#   Revision 1.1  2009/05/31 12:08:08  george
#   Initial revision
#
#

import math
import pylab  # matplotlib
from scipy import *
import scipy.io.array_import

# Generate x,y datasets (year,wolfer)
tempdata = scipy.io.array_import.read_array('sunspots.dat')
year=tempdata[:,0]
wolfer=tempdata[:,1]

#
# Plot year vs number of sunspots
#

pylab.xlabel("Year")
pylab.ylabel("Wolfer number")

pylab.plot(year, wolfer, 'b')

# save the plot as a PNG image file (optional)
pylab.savefig('sunspots_time.png')

# show the pylab plot window
# you can zoom the graph, drag the graph, change the margins, save the graph
pylab.show()


#
# Take FFT of #s of sunspots, generating real and imaginary
#

Y=fft(wolfer)

pylab.xlabel("real(FFT)")
pylab.ylabel("img(FFT)")
pylab.title("Meas")
pylab.plot(Y.real, Y.imag, 'ro')
pylab.xlim(-4000,2000)
pylab.savefig('sunspots_FFT.png')
pylab.xlim(-4000,2000)
pylab.show()

#
# Compute the frequency of sunspots
#

n=len(Y)
power = abs(Y[1:(n/2)])**2
nyquist=1./2
freq=array(range(n/2))/(n/2.0)*nyquist

pylab.xlabel("Frequency [1/year]")
pylab.ylabel("|FFT|**2")
pylab.title("Meas")
pylab.plot(freq[1:len(freq)], power, 'b')
pylab.xlim(0,0.20)
pylab.savefig('sunspots_freq.png')
pylab.xlim(0,0.20)
pylab.show()

#
# Given the frequency, compute the period
#


period=1./freq

pylab.xlabel("Period [year]")
pylab.ylabel("|FFT|**2")
pylab.title("Meas")
pylab.plot(period[1:len(period)], power, 'b')
pylab.xlim(0,40)
pylab.savefig('sunspots_period.png')
pylab.xlim(0,40)
pylab.show()