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\newtheorem{algorithm}{Algorithm}

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\markright{(CISIM'2006 -- at Miedzyzdroje, Poland)}
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\begin{document}
\begin{huge}

\clearpage  % (title) =========================================
\begin{center}
{\bf When a Family of  Iris Flower is Normal,\\ Then are Others Abnormal?}\\

\hspace*{1in}\\
\hspace*{1in}\\ 
Akira Imada  \\
\hspace*{1in}\\
Brest State Technical University \\ 
Belarus
\end{center}

\clearpage  % =========================================
\begin{center}
{\bf Network Intrusion Detection}\\

%%\bigskip
\hspace*{1in}\\
When we design such a system,\\
by means of a machine learning technique\\
we need an {\it artificial} data to train and to test the system.\\
\hspace*{1in}\\
$\Downarrow$\\
\hspace*{1in}\\
Is this method reliable? \\
\end{center}

\clearpage
\hfill {\large (Cont'd)}
\begin{center}
\hspace*{1in}\\
\hspace*{1in}\\
Specifically, is the frequently used {\it ``Iris Flower Data''} \\
useful for the purpose?\\
\end{center}

\clearpage
\begin{center}
\hspace*{1in}\\
\hspace*{1in}\\
\hspace*{1in}\\
This talk is not a report of success, \\
\bigskip
but a challange to those who have claimed success.
\end{center}

\clearpage
\begin{figure}[hb]
 \begin{center}
 \epsfile{file=ayame2.eps,width=8cm,height=6cm}
 \epsfile{file=yellow-iris.eps,width=8cm,height=6cm}
 \end{center}
\end{figure}



\clearpage  % (iris dataset) =========================================
\begin{center}
{\bf What is iris flower dataset?}\\
\bigskip
3 families of iris flower;\\
Each family has 50 samples $\Rightarrow$ 150 samples in total;

\end{center}
\begin{figure}[hb]
 \begin{center}
 \epsfile{file=iris-family2.eps,width=9cm,height=3cm}
 \end{center}
\end{figure}
\begin{center}
Each with 4 features (length \& width of sepal \& petal) like\\
{\large {\bf (0.35 0.28 0.46 0.02 Setosa)}}
\end{center}

\clearpage
\begin{center}
{\bf Typical implimentation by a neural network}\\
\end{center}
\begin{figure}[hb]
 \begin{center}
 \epsfile{file=nn-for-iris.eps,width=15cm,height=8cm}
 \end{center}
\end{figure}

\clearpage
\begin{center}
{\bf Training with GA as an example}\\

\hspace*{1in}\\

chromosome:\\
$(w_1, w_2, w_3, \cdots\cdots, w_N)$ \\

\hspace*{1in}\\

To evaluate fitness: \\
Firstly, we give a training set of normal and attack samples\\
{\it to each of the individuals.}\\
Then ratio of correct outputs is the fitness value.
\end{center}

\clearpage
\begin{center}
{\bf 10-fold cross validation }\\
\bigskip
The whole 150 samples are divided into 10 parts, \\
$\Downarrow$\\
so that each has 15 samples uniformly drawn from 3 classes.\\
$\Downarrow$\\
The system is {\it trained} by the remaining 135 samples.\\
$\Downarrow$\\
Then the 15 samples originally picked up are used to {\it test.}\\
$\Downarrow$\\
This is repeated 10 times.

\end{center}


\clearpage
\begin{center}
{\bf 2-fold cross validation for simplicity here}\\
\hspace*{1in}\\
Train with a half of both of normal and attack.\\
$\Downarrow$\\
Test with the remaining half.
\end{center}

\clearpage
\clearpage
 \begin{center}
{\bf Our GA experiment -- three families of iris}\\
Evolution of average fitness
\end{center}
\begin{figure}[hb]
 \begin{center}
 \epsfile{file=evol-iris-1.eps,width=16cm,height=8cm}
 \end{center}
\end{figure}


\clearpage
\begin{center}
{\Large {\bf Reference}}\\
{\bf Iris-flower \& Network Intrusion Detection}\\
\end{center}
\bigskip
\begin{description}
%%\setlength{\itemsep}{0pt}
{\Large
\item[]
Kim, J. and Bentley, P., (1999a), �gThe Human Immune
System and Network Intrusion Detection.�h
7th European Conference on Intelligent Techniques and Soft 
Computing (EUFIT�f99), Aachen, Germany.

\item[]
Kim, J. and Bentley, P., (1999b), 
�gThe Artificial Immune Model for Network Intrusion Detection, 7th
European Conference on Intelligent Techniques and Soft
Computing (EUFIT�f99), Aachen, Germany.

\item[]
Kim, J. and Bentley, P., (2000),
�gNegative Selection within an Artificial Immune System for Network Intrusion Detection'',
the 14th Annial Fall Symposium of the Korean Information Processing Society, Seoul, Korea.

\item[]
Yu Guan et al. (2004)
"K-means+: An Autonomous Clustering Algorithm"
University of New Brunswick, Fuculty of Computer Science Technical Reports, TR04-164

\item[]
G. G. Grinstein et al. (2002)
"Benchmark Development for the Evaluation of Visualization for Data Mining"
 Information Visualization in Data Mining and Knowledge Discovery, 
Morgan Kaufmann, San Francisco, pp. 129-176.

\item[]
D. E. Goodman et al.
An Investigation into the Source of Power for AIRS, an Artificial Immune Classification System."
Proceedings of International IEEE Joint Conference on Neural Networks, pages 1678-1683.

\item[]
Emma Hart (2002)
"Immunology as a Metaphor for Computational Information Processing: Fact or Fiction ?"
 Unpublished dissertation.

\item[]
J. Timmis et al. (2004)
"An Overview of Artificial Immune Systems."
Computation in Cells and Tissues: Perspectives and Tools for Thought,
Natural Computation Series, pp. 51-86. Springer, November 2004. 

\item[]
I.Antoniou et al.
"Information processing in Artificial Immune System: Approaches and State of the Art."

\item[]
Z. Ji and D. Dasgupata (2004)
"Augmented Negative Selection Algorithm with Variable-Coverage Detectors."
Proceedings of the Congress on Evolutionary Computation

\item[]
G. Castellano and A. M. Fanelli(2000)
"Fuzzy Inference and Rule Extraction using a Neural Network." 
Neural Network World Journal Vol. 3, pp. 361--371.

}
\end{description}

\clearpage
\begin{center}
%%{\bf Let's name a few from success reports so far -- 1}\\
{\bf From success reports so far -- 1}\\
\hspace*{1in}\\
Castellano et al. (2000) \\
assumed one to be attack data while the other two normal.\\
{\Large (by Fuzzy-NN with T-S model on Iris-data)}\\
$\Downarrow$\\
$\bullet$ successful attack detection rate = 96\%  \\ 
$\bullet$ false alarm rate = 0.6\%\\
\end{center}


%% ---
\clearpage
\begin{center}
{\bf Yet another success report so far -- 2}\\
\hspace*{1in}\\
Kim \& Bentley (2001) claimed \\
{\Large (by Artificial Immune Model on Iris-data)}\\
$\Downarrow$ \\
\hspace*{1in}\\
$\bullet$ Successful Detection Rate reached 100\%  \\
$\bullet$ False Alarm Rate was only 1\%.
\end{center}




\clearpage 
\begin{center}
\hspace*{1in}\\
\hspace*{1in}\\
But can we be so optimistic?\\

$\Downarrow$\\

``When a family of iris is normal then are others abnormal?''\\
\end{center}


\clearpage
\begin{center}
{\bf Let's try a thought-experiment \\ an extreme case:}\\
\hspace*{1in}\\
What if we design\\
\bigskip
{\it always-answer-yes-machine}\\
or\\
{\it randomly-answer-machine}?\\
$\Downarrow$\\
Detection rate of normal $\approx 100/150 = 66\%$.
\end{center}

\clearpage
\hfill {\large (Cont'd)}
\begin{center}
Evolving NN by GA often try to create this\\
{\it ``always-output-yes-NN''}
\bigskip
\begin{figure}[hb]
 \begin{center}
 \epsfile{file=nn-for-iris.eps,width=10cm,height=5cm}
 \end{center}
\end{figure}




\end{center}



\clearpage
\begin{center}
{\bf Let's visualize iris families --- Sammon mapping} \\
\hspace*{1in}\\
Sammon mapping maps points in a high-D space into 2-D \\
\hspace*{1in}\\
by\\
\hspace*{1in}\\
keeping distance relation preserved as much as possible
\hspace*{1in}\\
or \\
approximating distances in the high-D space \\
by distances in 2-D space with a minimal error.\\
\end{center}

\clearpage
\begin{center} 
{\bf A Sammon mapping of iris families}
\end{center}
\begin{figure}[h]\label{fig:iris}
\begin{center}
     \epsfile{file=iris.eps,width=16cm,height=8cm}
\end{center}
\end{figure}

\clearpage
\begin{center} {\bf How is a hucker like? (From a newspaper)}\\ 

\bigskip
\noindent
{\it 
``Those highly qualified hackers who provide \\security services to companies during 
the daytime \\
%%\hspace*{1in}\\
\bigskip
and then \\
%%\hspace*{1in}\\
\bigskip
go home at night to conduct totally illegal hacking \\
\bigskip
are \\
\bigskip
the ones who are the most dangerous.``\\
\bigskip
{\large -- by Enis Senerdem from Turkish Daily News on 29 March 2006}}.
\end{center}



\clearpage
\begin{center} 
{\bf Where do outliers lie?}\\
\hspace*{1in}\\
Not simply in the domain for the other families, \\like IRIS data!!\\
\begin{figure}[h]\label{fig:iris}
\begin{center}
     \epsfile{file=iris.eps,width=8cm,height=4cm}
\end{center}
\end{figure}
$\Downarrow$\\
{\Large
Not at the point at random, either.\\
But outlier usually hides behind Normal.
}
\end{center}



\clearpage  % =========================================
\begin{center} {\bf Standard Experiment on Iris-data} \\
\hspace*{1in}\\
Train and test with \\
one family as  attack while the other two as normal\\
\hspace*{1in}\\
Train with a part of the data\\
$\Downarrow$\\
Test with the remaining data
\end{center}

\clearpage
\begin{center} 
{\bf Outliers lie:}\\
\hspace*{1in}\\
{\bf Not in the domain for the other families}\\
$\Downarrow$\\
Not at the point at random, either.\\
$\Downarrow$\\
But outlier usually hides behind Normal.
\end{center}


\clearpage  % =========================================
\begin{center} 
{\bf What if the 3 families were distributed like below?}\\
\end{center}
\bigskip
\begin{figure}[hb]
 \begin{center}
 \epsfile{file=random-iris.eps,width=12cm,height=6cm}
 \end{center}
\end{figure}


\clearpage
\hfill {\large (Cont'd)}
\begin{center}
\hspace*{1in}\\
This doesn't look like a reality, but locally,\\
normal and attack data are distributed in this way, \\more or less.
\end{center}
\begin{figure}[h]\label{fig:iris}
\begin{center}
     \epsfile{file=iris.eps,width=12cm,height=4cm}
\end{center}
\end{figure}


\clearpage  % =========================================
\begin{center} {\Large {\bf Challange I}} \\ {\bf Randomly located normals and attacks}\\
(assuming both normal and attack don't create cluster)\\

\hspace*{1in}\\

Create 100 normal samples and 50 attacks\\ all at random. \\
\hspace*{1in}\\

Train with half of normal and atacks. \\
$\Downarrow$\\
Test with remaining half.
\end{center}

% ---
\clearpage
 \begin{center}
{\LARGE {\bf Ayara et al. (2002)\\
"Negative Selection: How to Generate Detectors."}} \\
{\LARGE Random attacks in 8-bit binary universe}\\
\begin{figure}[h]
 \begin{center}
   \epsfile{file=ayara.eps,width=7cm,height=3.5cm}
 \end{center}
\end{figure}

152 random attacks out of $2^8 = 256$ search points.\\
%%\hspace*{1in}\\
$\Downarrow$ \\
%%\hspace*{1in}\\
Asserted that successfully trained and detected them.
\end{center}


\clearpage
\hfill {\large (Cont'd)}
\begin{center}
\hspace*{1in}\\
\hspace*{1in}\\
This is not so difficult due to small universe.\\
\bigskip
On iris dataset, \\this is extremely difficult, if not impossible.
\end{center}

\clearpage
 \begin{center}
{\bf Our GA experiment -- random normal \& attack}\\
Evolution of average fitness
\end{center}

\begin{figure}[hb]
 \begin{center}
 \epsfile{file=evol-iris-2.eps,width=16cm,height=8cm}
 \end{center}
\end{figure}


\clearpage
\begin{center} 
{\bf Outliers lie:}\\
\hspace*{1in}\\
Not in the domain for the other families.\\
$\Downarrow$\\
{\bf Not at the point at random, either.}\\
$\Downarrow$\\
{\bf But outlier usually hides behind normal.}
\end{center}




\clearpage  % =========================================
\begin{center} {\Large {\bf Challange II}} \\ {\bf What if attacks hide behind normals?}\\
\hspace*{1in}\\
Create 100 normal samples again all at random.\\ 
Then 50 out of those samples are mutated as attack.\\
\hspace*{1in}\\

Train with half of the normal and mutants behind them.\\
$\Downarrow$\\
Test with the remaining half.
\end{center}


\clearpage
\hfill {\large (Cont'd)}
\begin{center}
\hspace*{1in}\\
\hspace*{1in}\\
This is even more difficult than the previous one.
\end{center}


\clearpage
\hfill {\large (Cont'd)}
\begin{center}
\hspace*{1in}\\
\hspace*{1in}\\
Let's now go back to Iris Data again.
\end{center}



\clearpage  % =========================================
\begin{center} {\Large {\bf Challange III}} \\ {\bf Attacks who hide behind normal iris family}\\
\hspace*{1in}\\
Assume one family as normal.\\
Then those samples are mutated as attack.\\
\hspace*{1in}\\

Train with half of normal and mutated attacks.\\
$\Downarrow$\\
Test with the remaining half.
\end{center}


\clearpage  % =========================================
\begin{center}
{\bf One family and its mutants}\\
\end{center}
\begin{figure}[hb]
 \begin{center}
 \epsfile{file=mutant.eps,width=16cm,height=8cm}
 \end{center}
\end{figure}

\clearpage
\clearpage
 \begin{center}
{\bf Our GA experiment -- normal \& mutant}\\
Evolution of average fitness
\end{center}
\begin{figure}[hb]
 \begin{center}
 \epsfile{file=evol-iris-3.eps,width=16cm,height=8cm}
 \end{center}
\end{figure}


\clearpage  % =========================================
\begin{center} {\Large {\bf Challange IV}} \\ {\bf Can we train only with normal?}\\
(This is actually is our MUST)\\
%%Try to be a somllier.}
%%Like a wine tasting}\\
\hspace*{1in}\\
Again one family is attack while the other two normal.\\

\hspace*{1in}\\
Train {\it ONLY} with normal.\\
$\Downarrow$\\
$\bullet$ Test-1:  with the other family as attack.\\
$\bullet$ Test-2:  with the randomly specified data as attack.\\
\end{center}

%% --- (Figure) ---
\clearpage
\begin{center}
{\bf An immune approach -- Negative Selection}
\end{center}

\begin{figure}[h]
\begin{center}
\epsfile{file=negative-s-2.eps,width=12cm,height=6cm}
\end{center}
\end{figure}
\begin{center}
Exhaustive detector ... Forrest, Perelson et al. (1994)
\end{center}

%% ---
\clearpage
\begin{center}
{\bf A bottleneck of the negative-selection algorithm}\\
\hspace*{1in}\\
\hspace*{1in}\\
Size of candidate antibodies \\increases exponentially \\with size of normal. \\
(Ayara et al.)

\end{center}

%% ---
\clearpage
\begin{center}
{\bf From success reports so far -- 3}\\
\hspace*{1in}\\

Gomez et al.~(2003)\\
{\it ``A  set of fuzzy rules characterized abnormal space 
using only normal samples.''}\\
\bigskip
(with 10\% dataset of KDD-cup-99). \\
\bigskip
$\Downarrow$\\
{\it ``It detects attacks with the detection rate 98.30\% 
and false alarm rate 2.0\%.''} \\
%%$\Downarrow$\\
%%Really satisfactory, if it's really true.\\
\end{center}


\clearpage  % =========================================
\begin{center}
%%{\bf How Gomez trained system only with normal?}\\
%%trained system only with normal?}\\
{\bf Let's apply Gomez' idea to iris data}\\ 
\hspace*{1in}\\
The 5 fuzzy linguistic terms\\

$\Downarrow$\\

\{{\it Low, Midium-low, Midium, Midium-high, High}\} \\
\end{center}

\begin{figure}[h]
 \begin{center}
   \epsfile{file=member-s.eps,width=10cm,height=4cm}
 \end{center}
\end{figure}


\clearpage
\hfill {\large (Cont'd)}
\begin{center}
An example of a set of rules
%%\bigskip
\underline{\hspace{5in}}\\
{\it
IF\\
$x_1$ is Law or Midium-law;                 \\
$x_2$ is High;                              \\
$x_3$ is Midium or Midium-high or High; and \\
$x_4$ is Midium-low or Midium.              \\
$\Downarrow$\\
THEN {\bf x} is ATTACK\\
}
\underline{\hspace{5in}}\\
\end{center}

\clearpage
\hfill {\large (Cont'd)}
\begin{center}
\hspace*{1in}\\
\hspace*{1in}\\
Assume we have $k$ such rules.\\
And then,\\ 
%%$\mu_{\large attack}(\mbox {\bf x})$ --
{\it a degree of membership of {\bf x} to the attack} \\
is calculated for given {\bf x}.
\end{center}


\clearpage
\hfill {\large (Cont'd)}
\begin{center}
Gomez's chromosome in the context of iris data \\
\bigskip
E.g., ((11001)(01001)(10000)(11100)) means \\
\bigskip
{\it 
$x_1$ is L or ML or H\\
$x_2$ is ML or H\\
$x_3$ is L\\
$x_4$ is L or ML or M\\
}
\bigskip
Fitness is evaluated as \\
%%$\mu_{\large attack}(\mbox {\bf x})$ --
{\it a degree of membership of {\bf x} to the attack}\\
by giving $N$ normal smaples
\end{center}

\clearpage
\hfill {\large (Cont'd)}
\begin{center}
\hspace*{1in}\\
\hspace*{1in}\\
Does this still work \\
on  \\
{\it random} or {\it mutated} normal \& attack data?
\end{center}



\clearpage  % =========================================
\begin{center}
{\Large {\bf Challange V}} \\ 
\bigskip
{\bf A metaphor of placebo --- dummy pill} \\
(necessary experiment by pharmaceutical companies) \\

\hspace*{1in}\\
{\it ``Drugs to ... are}\\
\bigskip
{\it no more effective than placebos for most patients.''}\\ 
\bigskip
{\it found no significant differnce than the placebos.''}\\
or\\
{\it at increased risk of death compared to placebo.''}

\bigskip
{\large -- by Benedict Carey from New York Times  on 12 October 2006}.
\end{center}

\clearpage
\hfill {\large (Cont'd)}
\begin{center}
We sometimes have a vias toward a too optimistic \\a'priori expectation.\\
\hspace*{1in}\\
(as a metophor of dummy pill)\\
Create a simple device \\
which randomly returns either normal or attack \\regardless of the input.
\end{center}

\clearpage  % =========================================
\begin{figure}[hb]
 \begin{center}
 \epsfile{file=placebo.eps,width=18cm,height=8cm}
 \end{center}
\end{figure}


\clearpage
\begin{center}
{\bf From success reports so far -- 4}\\
\hspace*{1in}\\
Joshi et al. (2005) \\
\bigskip
{\Large (by Hidden Markov Models on KDD-cup-99)}\\
$\Downarrow$\\
$\bullet$ 79\% accuracy in correctly detecting attacks,\\
$\bullet$ 21\% is accounted for false positive rate 

\hspace*{1in}\\
%%\bigskip
What if data comprises 80\% normal and 20\% attack?\\
(This is of usual situation in KDD-cup-99 dataset)

\end{center}


%% --- conclusion 1
\clearpage
\begin{center}
{\bf Concluding Remarks}\\
\end{center}
\begin{itemize}
\item Can we successfully train the system with randomly distributed attack and normal?
\item Can the system recognize a mutant of normal as an attack?
   \begin{itemize}
   \item[$\cdot$]  with randomly located normal.
   \item[$\cdot$] with one family of iris.
   \end{itemize}
\item Can the system trained only by normal recognize attack?
\item Is the performance better than a {\it random-replying-machine?} 
\end{itemize}


%% --- conclusion 2
\clearpage
\hfill {\large (Cont'd)}
\begin{center}
%%{\bf Concluding Remarks}\\

\bigskip
We are trying all of the five challanges with many methods: \\
NN, GA, AIS, C4.5, Fuzzy-classifyer ... etc. \\
\hspace*{1in}\\
%%$\Downarrow$\\
%%\bigskip
But so far not so succcessful\\ except for the original experiment.\\

\bigskip
Needless to say, \\no any intention of discouraging but a challange.\\
Positive attack to this {\it pessimitic} talk will be welcome.\\
And of course a cooperation too. \\ 
We have many data-set immune to be successfully tested.


\end{center}

\end{huge}
\end{document}
