How to validate experimental data using statistics? I am calculating shannon entropy from an experiment on Computer Network. The entropy is computaded from IP addresses in network. A have some entropy values in attack condition and another entropy values in "normal" condition. There is no standard values that I can compare. For example:
No attacks:
3.36247297985
3.41986105867
3.54255608391
3.40470906978
3.51915910324
3.53429621173
3.74549136098
3.44319360325

Under attacks:
1.80297042947
0.509454817684
0.529487864782
0.589599900586
1.41917987628
1.79816685786

What kind of "statistics formulas"/model I can use to validate the data/experiments?
Type of distribution the entropy : I dont know. 
The size of data: there are about 50/60 values of entropy for attack/no attack in a text file
 A: Since you don't know about the distribution of the entropy values you will have to use a non-parametric test.
Essentially you want to test if the populations are the same but "The populations are the same" is a very broad statement, usually we test a more specific hypothesis like "The medians are equal" or "The variances are equal". I know that your example values are just made up but for the example a test of medians would be very good. If the real non-attack entropy values are consistently near 3.5 and the attack values are much lower/higher then a median test is a good option.
A popular non-parametric test is the Mann-Whitney U test, it's easy to find software for it. Unfortunately this test can only be used when the attack and non-attack sample sizes are equal. This is still a good choice, you might consider randomly selecting values from your bigger data-set so that you get equal sample sizes.
An option for non-equal sample sizes is the median test.
If you have data for historical attacks then perhaps you could try both tests to see which gives fewer false positives/false negatives
