NMSL - User contributions [en-ca]

Private:progress-alkurbi

2011-03-29T05:57:10Z

Mma105:

= Spring 2011 =
* '''Courses:''' None
* '''Research:''' Developing Online SIP-Botnet Detection System

* '''Progress Report:''' Please read "Progress" section [https://cs-nsl-svn.cs.surrey.sfu.ca/cssvn/nsl-members/alkurbi/projects/Online-Sip-Botnet-Detection/Report/Report.pdf here]

=== March 29 ===
* I've addressed the questions and I've applied the comments of the examining committee.
* I'm reviewing the report line by line to do a final check up and any necessary revisions.

=== March 23 ===
* My defense will take place in Room 3580 Surrey.

=== March 15 ===
* I submitted the report to the Examining Committee last week.
* I distributed hard copies as well.

=== March 08 ===
* I submitted the revised and final draft report to the Prof after applying all the comments.

=== March 01 ===
* Incremental mode has been implemented and tested. The results show 65%~94% reduction of execution time.
* Previous set of comments have been applied.

=== Feb 15 ===
* All figures on the report are on eps format.
* I implemented and tested "`Tracking IP-flows"' instead of SIP sessions (To avoid the need to the application layer content which could be encrypted). I ran number of experiments, which showed unstable precision (low, mild, high, very high FP/FN), so I discard it.
* In order to improve the performance of calculating the similarity between two users from $O(N^2)$ into O(N), I redesigned the correlation\&detection engine to insert sessions in the order of their lengths, then implemented 3 types of similarity/distance algorithms: Cosine Similarity, Normalized Euclidean distance, and Canberra distance. Sessions are inserted in order so that we can eliminate the session time factor. The experiments showed that I could not find a threshold value that would minimize both FP/FN to the minimum.
* I almost implement incremental processing mode to improve the performance, so instead of processing the whole data within a time window, over again at every sliding window, I tried to take advantage of what has been built from the previous time window, and integrate only those new users/sessions in the new sliding time window into it. Next is to test the code, and I'm sure that it will need quite refinements.

=== Feb 08 ===
* Rewrite the experiments and evaluation results in a formal manner under "`Experimental Evaluation"' chapter. (Done)
* Implementing \& Testing Identifying Sip-Botnet controllers. (Done)
* Implementing and testing Online Mode. (Done)

=== Feb 01 ===
* Evaluation according to the plan (Large Scale Evaluation & Documentation) is complete, as following:
** Generated Traffics have been checked.
** Alpha & Beta has been tuned.
** Different traffic have been generated for different number of bots [10, 50, 100].
** FP/FN has been calculated for different Win [1h, 2h, 3h], and for different Sliding-Win [5m, 10m, 15m, 20m, 25m, 30m], with different number of bots.
** Average running time has been computed for different Win [1h, 2h, 3h] and different number of bots [10, 50, 100].
** A total of 34 figures have been plotted and included in the report.
** The attached report has all the update.

=== Jan 24 ===
* Works (Large Scale Evaluation & Documentation):
** Generated 24h SIP traffic with "1000" users, "10" bots.
** Tuned Alpha & Beta values.
** Ran the proposed system against the generated traffic with different win sizes (3h, 2h), and different Sliding-Win sizes (5m, 10m, 15m, 20m, 25m, 30m), to calculate False Positives/Negatives, and generated 12 statistics reports.
** Exporting statistics reports into Matlab and generating figures.

Private:progress-alkurbi

2011-03-15T00:32:14Z

Mma105:

= Spring 2011 =
* '''Courses:''' None
* '''Research:''' Developing Online SIP-Botnet Detection System

* '''Progress Report:''' Please read "Progress" section [https://cs-nsl-svn.cs.surrey.sfu.ca/cssvn/nsl-members/alkurbi/projects/Online-Sip-Botnet-Detection/Report/Report.pdf here]

=== March 15 ===
* I submitted the report to the Examining Committee last week.
* I distributed hard copies as well.

=== March 08 ===
* I submitted the revised and final draft report to the Prof after applying all the comments.

=== March 01 ===
* Incremental mode has been implemented and tested. The results show 65%~94% reduction of execution time.
* Previous set of comments have been applied.

=== Feb 15 ===
* All figures on the report are on eps format.
* I implemented and tested "`Tracking IP-flows"' instead of SIP sessions (To avoid the need to the application layer content which could be encrypted). I ran number of experiments, which showed unstable precision (low, mild, high, very high FP/FN), so I discard it.
* In order to improve the performance of calculating the similarity between two users from $O(N^2)$ into O(N), I redesigned the correlation\&detection engine to insert sessions in the order of their lengths, then implemented 3 types of similarity/distance algorithms: Cosine Similarity, Normalized Euclidean distance, and Canberra distance. Sessions are inserted in order so that we can eliminate the session time factor. The experiments showed that I could not find a threshold value that would minimize both FP/FN to the minimum.
* I almost implement incremental processing mode to improve the performance, so instead of processing the whole data within a time window, over again at every sliding window, I tried to take advantage of what has been built from the previous time window, and integrate only those new users/sessions in the new sliding time window into it. Next is to test the code, and I'm sure that it will need quite refinements.

=== Feb 08 ===
* Rewrite the experiments and evaluation results in a formal manner under "`Experimental Evaluation"' chapter. (Done)
* Implementing \& Testing Identifying Sip-Botnet controllers. (Done)
* Implementing and testing Online Mode. (Done)

=== Feb 01 ===
* Evaluation according to the plan (Large Scale Evaluation & Documentation) is complete, as following:
** Generated Traffics have been checked.
** Alpha & Beta has been tuned.
** Different traffic have been generated for different number of bots [10, 50, 100].
** FP/FN has been calculated for different Win [1h, 2h, 3h], and for different Sliding-Win [5m, 10m, 15m, 20m, 25m, 30m], with different number of bots.
** Average running time has been computed for different Win [1h, 2h, 3h] and different number of bots [10, 50, 100].
** A total of 34 figures have been plotted and included in the report.
** The attached report has all the update.

=== Jan 24 ===
* Works (Large Scale Evaluation & Documentation):
** Generated 24h SIP traffic with "1000" users, "10" bots.
** Tuned Alpha & Beta values.
** Ran the proposed system against the generated traffic with different win sizes (3h, 2h), and different Sliding-Win sizes (5m, 10m, 15m, 20m, 25m, 30m), to calculate False Positives/Negatives, and generated 12 statistics reports.
** Exporting statistics reports into Matlab and generating figures.

Private:progress-alkurbi

2011-03-08T17:57:24Z

Mma105:

= Spring 2011 =
* '''Courses:''' None
* '''Research:''' Developing Online SIP-Botnet Detection System

* '''Progress Report:''' Please read "Progress" section [https://cs-nsl-svn.cs.surrey.sfu.ca/cssvn/nsl-members/alkurbi/projects/Online-Sip-Botnet-Detection/Report/Report.pdf here]

=== March 08 ===
* I submitted the revised and final draft report to the Prof after applying all the comments.

=== March 01 ===
* Incremental mode has been implemented and tested. The results show 65%~94% reduction of execution time.
* Previous set of comments have been applied.

=== Feb 15 ===
* All figures on the report are on eps format.
* I implemented and tested "`Tracking IP-flows"' instead of SIP sessions (To avoid the need to the application layer content which could be encrypted). I ran number of experiments, which showed unstable precision (low, mild, high, very high FP/FN), so I discard it.
* In order to improve the performance of calculating the similarity between two users from $O(N^2)$ into O(N), I redesigned the correlation\&detection engine to insert sessions in the order of their lengths, then implemented 3 types of similarity/distance algorithms: Cosine Similarity, Normalized Euclidean distance, and Canberra distance. Sessions are inserted in order so that we can eliminate the session time factor. The experiments showed that I could not find a threshold value that would minimize both FP/FN to the minimum.
* I almost implement incremental processing mode to improve the performance, so instead of processing the whole data within a time window, over again at every sliding window, I tried to take advantage of what has been built from the previous time window, and integrate only those new users/sessions in the new sliding time window into it. Next is to test the code, and I'm sure that it will need quite refinements.

=== Feb 08 ===
* Rewrite the experiments and evaluation results in a formal manner under "`Experimental Evaluation"' chapter. (Done)
* Implementing \& Testing Identifying Sip-Botnet controllers. (Done)
* Implementing and testing Online Mode. (Done)

=== Feb 01 ===
* Evaluation according to the plan (Large Scale Evaluation & Documentation) is complete, as following:
** Generated Traffics have been checked.
** Alpha & Beta has been tuned.
** Different traffic have been generated for different number of bots [10, 50, 100].
** FP/FN has been calculated for different Win [1h, 2h, 3h], and for different Sliding-Win [5m, 10m, 15m, 20m, 25m, 30m], with different number of bots.
** Average running time has been computed for different Win [1h, 2h, 3h] and different number of bots [10, 50, 100].
** A total of 34 figures have been plotted and included in the report.
** The attached report has all the update.

=== Jan 24 ===
* Works (Large Scale Evaluation & Documentation):
** Generated 24h SIP traffic with "1000" users, "10" bots.
** Tuned Alpha & Beta values.
** Ran the proposed system against the generated traffic with different win sizes (3h, 2h), and different Sliding-Win sizes (5m, 10m, 15m, 20m, 25m, 30m), to calculate False Positives/Negatives, and generated 12 statistics reports.
** Exporting statistics reports into Matlab and generating figures.

Private:progress-alkurbi

2011-03-01T02:20:13Z

Mma105:

= Spring 2011 =
* '''Courses:''' None
* '''Research:''' Developing Online SIP-Botnet Detection System

* '''Progress Report:''' Please read "Progress" section [https://cs-nsl-svn.cs.surrey.sfu.ca/cssvn/nsl-members/alkurbi/projects/Online-Sip-Botnet-Detection/Report/Report.pdf here]

=== March 01 ===
* Incremental mode has been implemented and tested. The results show 65%~94% reduction of execution time.
* Previous set of comments have been applied.

=== Feb 15 ===
* All figures on the report are on eps format.
* I implemented and tested "`Tracking IP-flows"' instead of SIP sessions (To avoid the need to the application layer content which could be encrypted). I ran number of experiments, which showed unstable precision (low, mild, high, very high FP/FN), so I discard it.
* In order to improve the performance of calculating the similarity between two users from $O(N^2)$ into O(N), I redesigned the correlation\&detection engine to insert sessions in the order of their lengths, then implemented 3 types of similarity/distance algorithms: Cosine Similarity, Normalized Euclidean distance, and Canberra distance. Sessions are inserted in order so that we can eliminate the session time factor. The experiments showed that I could not find a threshold value that would minimize both FP/FN to the minimum.
* I almost implement incremental processing mode to improve the performance, so instead of processing the whole data within a time window, over again at every sliding window, I tried to take advantage of what has been built from the previous time window, and integrate only those new users/sessions in the new sliding time window into it. Next is to test the code, and I'm sure that it will need quite refinements.

=== Feb 08 ===
* Rewrite the experiments and evaluation results in a formal manner under "`Experimental Evaluation"' chapter. (Done)
* Implementing \& Testing Identifying Sip-Botnet controllers. (Done)
* Implementing and testing Online Mode. (Done)

=== Feb 01 ===
* Evaluation according to the plan (Large Scale Evaluation & Documentation) is complete, as following:
** Generated Traffics have been checked.
** Alpha & Beta has been tuned.
** Different traffic have been generated for different number of bots [10, 50, 100].
** FP/FN has been calculated for different Win [1h, 2h, 3h], and for different Sliding-Win [5m, 10m, 15m, 20m, 25m, 30m], with different number of bots.
** Average running time has been computed for different Win [1h, 2h, 3h] and different number of bots [10, 50, 100].
** A total of 34 figures have been plotted and included in the report.
** The attached report has all the update.

=== Jan 24 ===
* Works (Large Scale Evaluation & Documentation):
** Generated 24h SIP traffic with "1000" users, "10" bots.
** Tuned Alpha & Beta values.
** Ran the proposed system against the generated traffic with different win sizes (3h, 2h), and different Sliding-Win sizes (5m, 10m, 15m, 20m, 25m, 30m), to calculate False Positives/Negatives, and generated 12 statistics reports.
** Exporting statistics reports into Matlab and generating figures.

Private:progress-alkurbi

2011-03-01T02:19:53Z

Mma105:

= Spring 2011 =
* '''Courses:''' None
* '''Research:''' Developing Online SIP-Botnet Detection System

* '''Progress Report:''' Please read "Progress" section [https://cs-nsl-svn.cs.surrey.sfu.ca/cssvn/nsl-members/alkurbi/projects/Online-Sip-Botnet-Detection/Report/Report.pdf here]

=== March 01 ===
* Incremental mode has been implemented and tested. The results show 65%~94% reduction of execution time.
* Previous set of comments have been applied.

=== Feb 15 ===
* All figures on the report are on eps format.
* I implemented and tested "`Tracking IP-flows"' instead of SIP sessions (To avoid the need to the application layer content which could be encrypted). I ran number of experiments, which showed unstable precision (low, mild, high, very high FP/FN), so I discard it.
* In order to improve the performance of calculating the similarity between two users from $O(N^2)$ into O(N), I redesigned the correlation\&detection engine to insert sessions in the order of their lengths, then implemented 3 types of similarity/distance algorithms: Cosine Similarity, Normalized Euclidean distance, and Canberra distance. Sessions are inserted in order so that we can eliminate the session time factor. The experiments showed that I could not find a threshold value that would minimize both FP/FN to the minimum.
* I almost implement incremental processing mode to improve the performance, so instead of processing the whole data within a time window, over again at every sliding window, I tried to take advantage of what has been built from the previous time window, and integrate only those new users/sessions in the new sliding time window into it. Next is to test the code, and I'm sure that it will need quite refinements.

=== Feb 08 ===
* Rewrite the experiments and evaluation results in a formal manner under "`Experimental Evaluation"' chapter. (Done)
* Implementing \& Testing Identifying Sip-Botnet controllers. (Done)
* Implementing and testing Online Mode. (Done)

=== Feb 01 ===
* Evaluation according to the plan (Large Scale Evaluation & Documentation) is complete, as following:
** Generated Traffics have been checked.
** Alpha & Beta has been tuned.
** Different traffic have been generated for different number of bots [10, 50, 100].
** FP/FN has been calculated for different Win [1h, 2h, 3h], and for different Sliding-Win [5m, 10m, 15m, 20m, 25m, 30m], with different number of bots.
** Average running time has been computed for different Win [1h, 2h, 3h] and different number of bots [10, 50, 100].
** A total of 34 figures have been plotted and included in the report.
** The attached report has all the update.

=== Jan 24 ===
* Works (Large Scale Evaluation & Documentation):
** Generated 24h SIP traffic with "1000" users, "10" bots.
** Tuned Alpha & Beta values.
** Ran the proposed system against the generated traffic with different win sizes (3h, 2h), and different Sliding-Win sizes (5m, 10m, 15m, 20m, 25m, 30m), to calculate False Positives/Negatives, and generated 12 statistics reports.
** Exporting statistics reports into Matlab and generating figures.

Private:progress-alkurbi

2011-02-14T19:44:34Z

Mma105:

= Spring 2011 =
* '''Courses:''' None
* '''Research:''' Developing Online SIP-Botnet Detection System

* '''Progress Report:''' Please read "Progress" section [https://cs-nsl-svn.cs.surrey.sfu.ca/cssvn/nsl-members/alkurbi/projects/Online-Sip-Botnet-Detection/Report/Report.pdf here]

=== Feb 15 ===
* All figures on the report are on eps format.
* I implemented and tested "`Tracking IP-flows"' instead of SIP sessions (To avoid the need to the application layer content which could be encrypted). I ran number of experiments, which showed unstable precision (low, mild, high, very high FP/FN), so I discard it.
* In order to improve the performance of calculating the similarity between two users from $O(N^2)$ into O(N), I redesigned the correlation\&detection engine to insert sessions in the order of their lengths, then implemented 3 types of similarity/distance algorithms: Cosine Similarity, Normalized Euclidean distance, and Canberra distance. Sessions are inserted in order so that we can eliminate the session time factor. The experiments showed that I could not find a threshold value that would minimize both FP/FN to the minimum.
* I almost implement incremental processing mode to improve the performance, so instead of processing the whole data within a time window, over again at every sliding window, I tried to take advantage of what has been built from the previous time window, and integrate only those new users/sessions in the new sliding time window into it. Next is to test the code, and I'm sure that it will need quite refinements.

=== Feb 08 ===
* Rewrite the experiments and evaluation results in a formal manner under "`Experimental Evaluation"' chapter. (Done)
* Implementing \& Testing Identifying Sip-Botnet controllers. (Done)
* Implementing and testing Online Mode. (Done)

=== Feb 01 ===
* Evaluation according to the plan (Large Scale Evaluation & Documentation) is complete, as following:
** Generated Traffics have been checked.
** Alpha & Beta has been tuned.
** Different traffic have been generated for different number of bots [10, 50, 100].
** FP/FN has been calculated for different Win [1h, 2h, 3h], and for different Sliding-Win [5m, 10m, 15m, 20m, 25m, 30m], with different number of bots.
** Average running time has been computed for different Win [1h, 2h, 3h] and different number of bots [10, 50, 100].
** A total of 34 figures have been plotted and included in the report.
** The attached report has all the update.

=== Jan 24 ===
* Works (Large Scale Evaluation & Documentation):
** Generated 24h SIP traffic with "1000" users, "10" bots.
** Tuned Alpha & Beta values.
** Ran the proposed system against the generated traffic with different win sizes (3h, 2h), and different Sliding-Win sizes (5m, 10m, 15m, 20m, 25m, 30m), to calculate False Positives/Negatives, and generated 12 statistics reports.
** Exporting statistics reports into Matlab and generating figures.

Private:progress-alkurbi

2011-02-14T19:44:09Z

Mma105:

= Spring 2011 =
* '''Courses:''' None
* '''Research:''' Developing Online SIP-Botnet Detection System

* '''Progress Report:''' Please read "Progress" section [https://cs-nsl-svn.cs.surrey.sfu.ca/cssvn/nsl-members/alkurbi/projects/Online-Sip-Botnet-Detection/Report/Report.pdf here]

=== Feb 15 ===
* All figures on the report are on eps format.
* I implemented and tested "`Tracking IP-flows"' instead of SIP sessions (To avoid the need to the application layer content which could be encrypted). I ran number of experiments, which showed unstable precision (low, mild, high, very high FP/FN), so I discard it.
* In order to improve the performance of calculating the similarity between two users from $O(N^2)$ into O(N), I redesigned the correlation\&detection engine to insert sessions in the order of their length, then implemented 3 types of similarity/distance algorithms: Cosine Similarity, Normalized Euclidean distance, and Canberra distance. Sessions are inserted in order so that we can eliminate the session time factor. The experiments showed that I could not find a threshold value that would minimize both FP/FN to the minimum.
* I almost implement incremental processing mode to improve the performance, so instead of processing the whole data within a time window, over again at every sliding window, I tried to take advantage of what has been built from the previous time window, and integrate only those new users/sessions in the new sliding time window into it. Next is to test the code, and I'm sure that it will need quite refinements.

=== Feb 08 ===
* Rewrite the experiments and evaluation results in a formal manner under "`Experimental Evaluation"' chapter. (Done)
* Implementing \& Testing Identifying Sip-Botnet controllers. (Done)
* Implementing and testing Online Mode. (Done)

=== Feb 01 ===
* Evaluation according to the plan (Large Scale Evaluation & Documentation) is complete, as following:
** Generated Traffics have been checked.
** Alpha & Beta has been tuned.
** Different traffic have been generated for different number of bots [10, 50, 100].
** FP/FN has been calculated for different Win [1h, 2h, 3h], and for different Sliding-Win [5m, 10m, 15m, 20m, 25m, 30m], with different number of bots.
** Average running time has been computed for different Win [1h, 2h, 3h] and different number of bots [10, 50, 100].
** A total of 34 figures have been plotted and included in the report.
** The attached report has all the update.

=== Jan 24 ===
* Works (Large Scale Evaluation & Documentation):
** Generated 24h SIP traffic with "1000" users, "10" bots.
** Tuned Alpha & Beta values.
** Ran the proposed system against the generated traffic with different win sizes (3h, 2h), and different Sliding-Win sizes (5m, 10m, 15m, 20m, 25m, 30m), to calculate False Positives/Negatives, and generated 12 statistics reports.
** Exporting statistics reports into Matlab and generating figures.

Private:progress-alkurbi

2011-02-14T19:43:02Z

Mma105:

= Spring 2011 =
* '''Courses:''' None
* '''Research:''' Developing Online SIP-Botnet Detection System

* '''Progress Report:''' Please read "Progress" section [https://cs-nsl-svn.cs.surrey.sfu.ca/cssvn/nsl-members/alkurbi/projects/Online-Sip-Botnet-Detection/Report/Report.pdf here]

=== Feb 15 ===
* All figures on the report are on eps format.
* I implemented and tested "`Tracking IP-flows"' instead of SIP sessions (To avoid the need to the application layer content which could be encrypted). I ran number of experiments, which showed unstable precision (low, mild, high, very high FP/FN), so I discard it.
* In order to improve the performance of calculating the similarity between two users from $O(N^2)$ into O(N), I redesigned the correlation\&detection engine to insert sessions on order of their length, then implemented 3 types of similarity/distance algorithms: Cosine Similarity, Normalized Euclidean distance, and Canberra distance. Sessions are inserted in order so that we can eliminate the session time factor. The experiments showed that I could not find a threshold value that would minimize both FP/FN to the minimum.
* I almost implement incremental processing mode to improve the performance, so instead of processing the whole data within a time window, over again at every sliding window, I tried to take advantage of what has been built from the previous time window, and integrate only those new users/sessions in the new sliding time window into it. Next is to test the code, and I'm sure that it will need quite refinements.

=== Feb 08 ===
* Rewrite the experiments and evaluation results in a formal manner under "`Experimental Evaluation"' chapter. (Done)
* Implementing \& Testing Identifying Sip-Botnet controllers. (Done)
* Implementing and testing Online Mode. (Done)

=== Feb 01 ===
* Evaluation according to the plan (Large Scale Evaluation & Documentation) is complete, as following:
** Generated Traffics have been checked.
** Alpha & Beta has been tuned.
** Different traffic have been generated for different number of bots [10, 50, 100].
** FP/FN has been calculated for different Win [1h, 2h, 3h], and for different Sliding-Win [5m, 10m, 15m, 20m, 25m, 30m], with different number of bots.
** Average running time has been computed for different Win [1h, 2h, 3h] and different number of bots [10, 50, 100].
** A total of 34 figures have been plotted and included in the report.
** The attached report has all the update.

=== Jan 24 ===
* Works (Large Scale Evaluation & Documentation):
** Generated 24h SIP traffic with "1000" users, "10" bots.
** Tuned Alpha & Beta values.
** Ran the proposed system against the generated traffic with different win sizes (3h, 2h), and different Sliding-Win sizes (5m, 10m, 15m, 20m, 25m, 30m), to calculate False Positives/Negatives, and generated 12 statistics reports.
** Exporting statistics reports into Matlab and generating figures.

Private:progress-alkurbi

2011-02-08T01:07:08Z

Mma105:

Private:progress-alkurbi

2011-02-01T01:38:59Z

Mma105:

Private:progress-alkurbi

2011-01-28T20:25:26Z

Mma105:

Private:progress-alkurbi

2011-01-28T20:20:34Z

Mma105:

Private:progress-alkurbi

2011-01-28T18:43:51Z

Mma105:

Private:progress-alkurbi

2011-01-28T18:43:19Z

Mma105:

Private:progress-alkurbi

2011-01-28T18:41:57Z

Mma105:

Private:progress-alkurbi

2011-01-28T18:30:03Z

Mma105:

Private:progress-alkurbi

2011-01-28T18:28:33Z

Mma105:

Private:progress-alkurbi

2011-01-27T02:03:45Z

Mma105:

Private:progress-alkurbi

2011-01-27T01:20:11Z

Mma105:

= Spring 2011 =
* '''Courses:''' None
* '''Research:''' Developing Online SIP-Botnet Detection System

=== Jan 24 ===
* '''Report:''' Please read "Progress" section [https://cs-nsl-svn.cs.surrey.sfu.ca/cssvn/nsl-members/alkurbi/projects/Online-Sip-Botnet-Detection/Report/Thesis/Thesis/doc/thesis.pdf here]
* [[]]
* Works:
** Generate 24h SIP traffic with "1000" users, "10" bots, Win=3h, and Sliding-Win=15m.
**

=== Feb 01 ===

Private:progress-alkurbi

2011-01-27T01:06:47Z

Mma105:

'''Spring 2011'''

----
'''Jan 25'''

Private:progress-alkurbi

2011-01-27T01:06:33Z

Mma105:

<nowiki>Insert non-formatted text here</nowiki>''''Spring 2011''''

----
'''Jan 25'''

Private:progress-alkurbi

2011-01-27T01:06:18Z

Mma105:

''''Spring 2011''''

----
'''Jan 25'''

Private:progress-alkurbi

2011-01-27T01:05:59Z

Mma105:

''Spring 2011''

----
'''Jan 25'''

Private:progress-alkurbi

2011-01-27T01:04:57Z

Mma105: New page: ''Italic text''Spring 2011 ---- '''Bold text'''Jan 25

''Italic text''Spring 2011

----
'''Bold text'''Jan 25