Why is random noise amplitude inversely proporional to the frequency? http://astarte.csustan.edu/~tom/SFI-CSSS/info-theory/info-lec.pdf compares a bacteria DNA vs a random string: 


  My first step was to generate for myself a "random genome" of
  comparable size to compare things with. In this case, I simply used
  the Unix `random' function to generate a file containing a random
  sequence of about 4 million A, C, G, T. In the actual genome, these
  letters stand for the nucleotides adenine, cytosine, guanine, and
  thymine.
  
  We want to find "interesting" sections (and features) of a genome. As a
  starting place, we can slide a "window" over the genome, and estimate
  the entropy within the window. The plot below shows the entropy
  estimates for the E. coli genome, within a window of size 6561 (= 3^8
  ). The window is slid in steps of size 81 (= 3^4 ). This results in
  57,194 values, one for each placement of the window. For comparison,
  the values for a "random" genome are also shown.





  At this level, we can make the simple observation that the actual
  genome values are quite different from the comparative random string.
  The values for E. coli range from about 5.8 to about
  5.96, while the random values are clustered quite closely above 5.99 (the maximum possible is log2 (64) = 6).
  
  There are various "data massage" techniques we could use. For example,
  we could take the fourier transform of the entropy estimates, and
  explore that. Below is an example of such a fourier transform. Notice
  that it has some interesting "periodic" features which might be worth
  exploring. It is also interesting to note that the fourier
  
  transform of the entropy of a "random" genome has the shape of
  approximately $1/f = 1/f^1$ (not unexpected), whereas the E. coli
  data are closer to $1/f^{1.5}$




I must draw you attention that the fourier transform is applied to the entropy rather than to the random signal itself. Why is it expected that resulting spectrum is pink rather than absolutely random? I do not understand why it is expected that the random frequency amplitude is inverse to the frequency, why not $amplitude=1/freq^{1.5}$? The higher low frequencies mean that the next number has higher probability to be closer to the current one and random is not quite random, right? 

I have also noted that, in the end of the book, author says that "random" is naturally band-limited by the power law, $1/f^\alpha,$  and, in SETI project, we better watch after the white noise because optimal use of the channel is a sign of intelligent life.
This looks like a pink noise model, where the power spectral density of the noise is proportional to $\frac{1}{f}$. This model is appropriate for some real-world phenomena; I can't speak to whether it's the right choice for this particular application.

Your interpretation of the meaning of this sort of spectrum is correct, although the noise is still considered random. Instead, we consider consecutive noise samples to be correlated with one another (in contrast to white noise, where all samples are jointly uncorrelated). That doesn't mean that the signal isn't random.