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A:
This does appear to be a bug in SVN. We’ve been using GitHub for our repository and we recently switched to GitHub Pages to host it. I’m not familiar with GitHub but their help pages indicate that you should simply be able to create another repository and then specify your new location in your repo URL. In this case, the command to make it is
svn –bare –fsfs-scheme http setfsfs
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After reading about out algos, i feel that i need to define the first element in the samples list as a matcher. Some outlier data to test the robustness of the algos.
Thank you in advance.
A:
At the moment you are only expanding on the test cases and the strategies you have are very simple. So it is too early to talk about robustness.
I would suggest you to write a small part of your program in a more appropriate programming language, e.g. C++ or Java. Then you can run the test from the command line, as a JUnit test. This will allow you to analyze your program very easily.
Furthermore, you may want to adapt it to your problem and optimize it by trying to determine different strategies, which will be reflected in running times and memory. You may even want to switch your computer to your problem domain to get a feel for the algorithms used there.
A:
I don’t think that “to catch’malicious’ programs out of a huge amount of programs is a reasonable approach” because the ruleset, the logic, etc has to be adjusted, or at least you would want to spend more time on that, and then you could do more extensive testing.
I would like to mention the many power tools you have in your toolbox, including the aforementioned own string matching logic, to go after malware out of large numbers of programs.
There is something that you can do, that you did not do, to reduce the programming effort. You could use a preprocessor instead of a compiler.
If you want to do it in your own way, then you need to recognize, why you are doing it:
First you use this pattern
if (strcmp(program, “your_malicious_program”)
then you compare that string to every program and use some comparison logic to split that program into substrings.
Next step is, if you have found “dangerous” programs that should be monitored, create a database with (something like) ID, date, checksum, description, etc.
Now you just check that same database to see if you find the same ID.
Third step is, monitor the database for suspicious changes.
Problem, what you do not do, is to check that you did not get too much traffic and now you have found too much traffic
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