MINING KNOWLEDGE FROM NETWORK INTRUSION DATA USING DATA MINING TECHNIQUES - Knowledge Mining Using Intelligent Agents - page 197

6.8. Conclusion

Building intrusion detection systems has been a real challenge as new types
of attacks are encountered every day. No single technique can effectively deal
with the growing intrusion scenarios. In this work we have applied different
data mining techniques to analyze available intrusion data sets to extract
knowledge about the nature of intrusions so that suitable counter-measures
can be developed to deal with them. We have discussed the association rule
mining with various interestingness measures in order to obtain the best
rules for the detection of intrusions. Results show that the use of multiple
minimum supports can enhance the performance compared to the single
minimum support threshold. It is observed that unsupervised clustering
algorithms like COBWEB and FFT provide promising results in detecting
network intrusions. Further, the ensemble classifiers can still improve the
accuracy of intrusion detection in many cases.

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