Free up to the minute software for linux

popcheck is a SuperKaramba theme that displayes the most recent 20 e-mails received at a POP account that are still awaiting download.

Queries the POP server once per minute and as new e-mails as they arrive.

The sender and the title are displayed.

You must configure the hostname and username/password info in the popcheck.py file.

You can include a list of substrings, such that if one of them is found in a new mail, an audible alert will sound. This allows you to know when new mail from particular people has arived.

The default alert sound is the word "MAIL" in Morse code. You can change this in the popcheck.py file by pointing to a different alert_file, or by replacing the included mail.ogg file with a different sound.

Clicking on an e-mail will cause popcheck to download the beginning of the file and display it in a kdialog message box. This way you can look at the start of an e-mail (up to 30 lines) without opening your e-mail client to download the message.

RIR to DNS converter is a tool to convert Regional Internet Registry data to a DNS country lookup zone. You can use it to build your own DNS zone for looking up country codes from IP addresses.

It uses data directly from RIPE, ARIN, APNIC, LACNIC, and AFRINIC. The data can be updated on a schedule of your choosing.

The input data comes from:

ftp://ftp.afrinic.net/pub/stats/afrinic/delegated-afrinic-latest
ftp://ftp.apnic.net/pub/stats/apnic/delegated-apnic-latest
ftp://ftp.arin.net/pub/stats/arin/delegated-arin-latest
ftp://ftp.ripe.net/pub/stats/ripencc/delegated-ripencc-latest
ftp://ftp.lacnic.net/pub/stats/lacnic/delegated-lacnic-latest

The input data format is described in:

http://www.apnic.net/db/rir-stats-format.html

The output is a BIND 9 zone file that can be used to look up country codes
in a similar fashion to in-addr.arpa. For example, to find out what country
203.30.47.58 is:

host 58.47.30.203.rir.example.com
58.47.30.203.rir.example.com has address 127.0.65.86

where 65 and 85 are ASCII for A and U, which means 203.30.47.58 is
in Australia (AU).

HOW TO USE IT

Just feed it the above delegated- -latest files into stdin and it will
spit out the zone file to stdout. The zone file will only have the IP addresses,
so you could $INCLUDE it into a zone file that contains NS records, SOA, $ORIGIN,
etc.

WHY USE IT

You dont need the resolution of MaxMinds GeoIP database, but you do want
something that is free and you want it kept up to date on a schedule that
you decide.

You could use this to block or tag email based on countries, block or redirect
visitors to your website based on end-user country, and so on. Be very
careful about blocking mail this way, though, as you may block legitimate
email. Instead of blocking outright, use it in a SpamAssassin rule to add
something to the spam level, based on where the email comes from.

HOW IT WORKS

The RIR files contain ranges of IP addresses, and indicate what CC each range is allocated to. At the simplest level, rir2dns just sorts the ranges then iterates
through the IPs in each range and generates a reverse-dns-style A record that
represents the country code.

HOW IT WORKS - IN DETAIL

Rather than iterate through each IP address, the program tries to skip through
entire classes at a time (256 IPs, 65536 IPs, etc). Rather than iterate
through each IP, the loop iterates through classes or IP ranges (whichever are
smaller at the loop control), using control-breaks to accummulate neighbouring
ranges where possible so that entire classes that are in the same country dont
generate huge numbers of records.

Firstly, IPs are considered to be 4-digit numbers, but in base-256. In other
words, each octet is dealt with as if it were a single base-256 digit. This
turns out to be convenient because optimisations of large chunks of IP space can be done by looking for places where least-significant base-256 digits are zero.

Next, IP ranges are broken down into the following sub-ranges:

Optional individual IP addresses (ie: 4 octets)
Optional A-class ranges (ie: 3 octets)
Optional B-class ranges (ie: 2 octets)
Optional C-class ranges (ie: 1 octet)
Optional B-class ranges (ie: 2 octets)
Optional A-class ranges (ie: 3 octets)
Optional individual IP addresses (ie: 4 octets)

Considering that there is a pattern here, Im sure theres an elegant way to
handle breaking this down into two loops (one reducing the octets and one
increasing the octets), but I cant be bothered, so Ill break it down into
seven loops. Kind of hard-coded, but at least its simple.

For ease of processing, the IP addresses are actually converted to 32-bit numbers, then back again. This simplifies mathematics and looping through ranges.

Thats pretty much it, really...

Note that currently there are about 80,000 RIR records between all five
registries. This takes about 35 seconds on a 2.4GHz P4 to process, and
generates a 26MB file with around 3/4 million lines (RRs). This causes BIND
to use about 100MB or so of memory, and on a slow machine will probably cause it to take too long to reply, while it searches the zone. That size zone can
take a minute or two to load, which is quite a while.

Basic algorithm:

Read & process RIR data:

Read RIR ranges
Sort RIR ranges by start IP address
Glue together contiguous ranges of the same country

For each range

Generate the IPs at the start of the range

Generate the A-classes at the start of the range

Generate the B-classes at the start of the range

Generate the C-classes in the middle of the range

Generate the B-classes at the end of the range

Generate the A-classes at the end of the range

Generate the IPs at the end of the range

DKP Log Parser (DKPLP) is a tool designed to help administrate time-based and/or event-based DKP reward system in conjunction with EQDKP (or any other DKP software implementing a required interface). The project is configurable to be compatible with any game that produces any kind of log. There are currently predefined pattern sets for EverQuest, EverQuest2 and World of Warcraft (with CT RaidTracker). If you are playing another game then you can either write new patterns for it yourself, or ask the the forum (be sure to include a sample log though).
DKP Log Parsers goal is to reduce the administrative overhead of running complex DKP reward system, by parsing the logs, calculating the DKP per person and sending the results (the loot, raids, participants and DKP) directly to EQDKP (or other DKP software). Its widely configurable, allowing it to be used by the many variations of zero-sum and time-based DKP around.
Overview of how DKP Log Parser is used
DKP Log Parser is a software program run on a users desktop (Windows or Unix). It reads in the contents of a game log once a raid has been completed, parses all the information and displays the data for administrators to edit. Once happy with the information, the administrator uploads the data from their PC to the DKP Log Parser plugin on an EQDKP webserver (or any other server implementing the required DKPLP interface), where it can be viewed by all members of the DKP system.
Main features:
- Inbuilt support for Everquest, Everquest II, World of Warcrafts CT RaidTracker plugin, and Ventrilo.
- Extensible enough to be able to handle most logs.
- Time based dkp:
- Accrue DKP by minute, by interval, by event or by a combination of these.
- Zero-sum DKP can be enabled (by minute or by interval) or disabled.
- Tag certain intervals as being more or less important by using weightings to adjust the DKP.
- Data export:
- Upload data to for example EQDKP, select from a number of different data representations.
- Export to plain text
- Export to BB forum markup
- Export to wiki markup
- Export to XML
- Member list and alt character list synchronises with server, making it easy to share setups with other users.
- Optional autocompletion database to remember item names and values between sessions.
- Preprocessors available to perform other operations on the log file. The skys the limit!

Spoink is an output-plugin for snort that works by blocking access to attackers using OpenBSDs pf api.

All you need is an OpenBSD machine (or pf compatible), and snort (last version works well).

Spoink uses a pf table and a blocking rule to stop "attackers" accessing our system. To protect from false negatives you must have a whitelist full of ips you want save (see section 2).

Spoink program only blocks attacks defined in snort rules so think for a minute what rules you want to use first.

JCapture is an automatic Webcam image grabber & image manipulator. Control it from your crontab once a minute, or however often you want to update the image.

A working installation of ImageMagick is required for this to run properly. I also use CQCam v0.90p7 to capture the image. You will need to change the associated lines in the file where necessary to adjust for paths to the proper executables on your machine.

yauap is a simple commandline audio player that is using the GStreamer multimedia framework.

yauap project comes complete with a amarok engine that is able to control the player over DBus.

yauap is licenced under the GNU LESSER GENERAL PUBLIC LICENSE.

Keybindings:

SPACE
Pause/Unpause

Seek backward/forward 10 seconds.
up and down
Seek backward/forward 1 minute.

q / ESC
Quit
/ and *
Decrease/increase volume.

Useful Traffic Accounting Dragon is a traffic accounter that actually gives useful information about traffic instead of stupidly counting incoming/outgoing packets.
Dragon accounts traffic by user, program and timestamp and puts this information into useful correlation.
Dragon is implemented as a Perl script for the backend, using /etc/passwd to automatically get information about user accounts, /proc/net/ to get information about open connections and /proc/ to get information about running processes and which processes currently use which network connection.
All this information can be combined so you can actually see what user caused which traffic using what program and when it happened.
All this information is acquired automatically so the minimal configuration necessary is to tell dragon where it can find its MySQL database.
Enhancements:
Features:
- cleanup on CTRL-C
- add patch for Linux Kernel 2.6.17.7
- for now, graphs are disabled in the webfrontend
- webfrontend has now Total, User and Program tabs
Bugfixes:
- forgot to close a filedescriptor fixed
- use leading zeroes on port numbers in messages about connections that no longer exist
- really fix wait for next minute

Python Traffic Analyzer is a Python base class and sample driver script written to retrieve and manipulate images from the TrafficLand cameras and calculate a numeric value representing the current traffic flow.

PyTrAn, an example driver script, an image collector and an image mask creator are available for download from the link shown at the bottom. To use the PyTrAn package begin by choosing a camera that you wish to analyze, for this example well use the camera captioned above.

We want to construct a mask over the area of the image that we are interested in, namely the road. In this particular example the road takes up the majority of the image but that is not always the case.

We will apply the mask over captured images to fine tune the area over which we are looking for movement. To create the mask we will first need to collect a sequential series of snapshots from the target camera. The image_collector.py script was written for this task:

$ mkdir mask_200003
$ cd mask_200003
$ ../image_collector.py 200003 30
Collecting 30 images...
30

Done.

The script is hard coded to capture images on a 2-second delay. The delay is necessary to ensure the image has changed. I believe 2-seconds to be the absolute minimum. Once complete, 30 images numbered 1 through 30 will be created in the current directory.

We construct a mask from these captured images by creating a diff-image for each sequential image pair and then adding each diff-image together. Naturally, a script was written to automate this task as well:

$ ../mask_maker.py 1 30
Creating a diff for each sequential image pair.
Diffing 29

Creating the initial mask from the first image pair.

Adding the rest of the diffs to the mask.
Masking 29

Done.

A number of .diff files are generated in this process. These files repesent the movement between individual sequence pairs.

The .diff files are simply intermediary files, the important bit is the mask file, which is generated as the sum of all differences.

The mask file may be dirty (as in this case) and require manual cleanup. The basic shape of the road however is clearly visible, evidence that we can with minimal effort automate the mask generation process. Also, this run was conducted at night, day-time images yield better results.

There are a few final steps we need to take before we can use the example PyTrAn driver script. First we need to convert the mask to ASCII (noraw) format:

$ pnmnoraw mask > mask_200003.ascii

Then we need to open an ImageMagick display window and get its X-window-ID using xwininfo. Finally, update camera_id and window_id in pytran_sampling.py and launch the driver:

$ ../pytran_sampling.py
DEBUG> grabbing frame from camera 200003
DEBUG> rotating image: pytran.this > pytran.last
DEBUG> refreshing image in 3 secs
taking a 5 minute sample at various thresholds.
DEBUG> grabbing frame from camera 200003
DEBUG> generating frame diff on pytran.last, pytran.this
DEBUG> displaying image: pytran.diff
DEBUG> converting pytran.diff to ascii
DEBUG> calculating traffic ratio...
ratio[5]: 55%
DEBUG> calculating traffic ratio...
ratio[10]: 52%
...
...
5 minute sample[5]: 67.88
5 minute sample[10]: 42.66
5 minute sample[15]: 30.57
5 minute sample[20]: 23.03
5 minute sample[25]: 18.39
5 minute sample[30]: 14.79
5 minute sample[35]: 12.42
5 minute sample[40]: 10.53
5 minute sample[45]: 9.06
5 minute sample[50]: 7.85

The sampling script will take 5 minute samples at varying color thresholds. The optimal threshold must be manually chosen. Furthermore, you will need to sample the traffic ratios during both heavy and light traffic times to get a good feel for your acceptable range. Also, keep in mind that the traffic ratio value is simply the percent change detected, or in other words the movement detected within the masked region. This means that a completely empty road will register similar values to a road so congested it looks like a parking lot. The time of day can be combined with the traffic ration to determine the logical truth.

With this task implemented and abstracted more complex systems can be built. When I find the time Id like to create a system that will take multiple potential travel routes and times, and during the travel time e-mail the traveler with the best route to take. Another idea I had would be to record the traffic flow values for each camera, for each day and for each half hour interval. Travelers and other interested parties can then analyze traffic patterns to determine the fastest route dependant on date/time.