borne to born, link to models in-line

introduction.tex

@@ -67,7 +67,7 @@ testing of SSH implementations \cite{Boss2012}.  All this research
 only considered the SSH transport layer, and not the other SSH
 protocol layers.
 
-This paper is borne out of 2 recent theses \cite{Verleg2016,Toon2016}
+This paper is born out of two recent theses \cite{Verleg2016,Toon2016},
 and is to our knowledge the first combined application of model
 learning and model checking in verifying SSH implementations, or more
 generally, implementations of any network security protocol.

learning_results.tex

@@ -44,7 +44,7 @@ We then ran an exhaustive test suite with {\dk} of 2 for all implementations.
 
 
 Table~\ref{tab:experiments} describes the exact versions of the systems analyzed together with statistics on learning and testing, namely:
-(1) the number of states in the learned model, (2) the number of hypotheses built during the learning process and (3) the total number of learning and test queries run. For test queries, we only consider those run on the last hypothesis. All learned models along with the specifications checked can be found at~\cite{SSHResults}.
+(1) the number of states in the learned model, (2) the number of hypotheses built during the learning process and (3) the total number of learning and test queries run. For test queries, we only consider those run on the last hypothesis. All learned models along with the specifications checked can be found at \url{https://gitlab.science.ru.nl/pfiteraubrostean/Learning-SSH-Paper/tree/master/models}.
 %BitVise: MemQ: 24996 TestQ: 58423
 %Dropbear: MemQ: 3561 TestQ: 30629
 %OpenSSH: MemQ: 19836 TestQ: 76418

sigproc.bib

@@ -24,7 +24,7 @@
 }
 
 @misc{SSHResults,
-  title = {Learned SSH Models},
+  title = {Learned {SSH} Models},
   year = {2017},
   publisher = {GitLab},
   journal = {GitLab repository},