PL EN
REVIEW PAPER
ANALYSIS OF THE HONEYPOT SYSTEM DATA USING DATA MINING TECHNIQUES
 
 
More details
Hide details
1
Politechnika Warszawska
 
 
Publication date: 2014-12-05
 
 
SBN 2014;6(2): 325-340
 
KEYWORDS
ABSTRACT
The HoneyPot systems are used From several years to gather data concerning novel attacks appearing in the Internet. Despite the fact that new types of HoneyPots are developed, there is a lack of analytical software, which can be used for analysis of data provided by this kind of systems. The article contains a description of the WebHP/HPMS (HoneyPot Management System) which allows analysis of HoneyPot gathered data. Additionally, the article presents used data mining techniques and conducted experiments. Preliminary results appeared to be very promising. In the vast amounts of data, discovered patterns rapidly reveal signs of new types of attacks.
 
REFERENCES (11)
1.
R. Agrawal, T. Imielinski, A Swami, Mining Association Rules Between Sets of Items in Large Databases, Proceedings of ACM SIGMOD Int. Conf. Management of Data, (1993).
 
2.
R. Agrawal, R. Srikant, Mining Sequential Patterns: Generalizations and Performance Improvements, In Proceedings of the Fifth International Conference on Extending Database Technology (EDBT), (1996).
 
3.
R. J. Bayardo, Efficiently mining long patterns from databases, In Proc. 1998 ACM-SIGMOD Int. Conf. Management of Data (SIGMOD’98), Seattle, WA, pp. 85–93, (1998).
 
4.
M. L. Bringer, C. A. Chelmecki, H. Fujinoki, A Survey: Recent Advances and Future Trends in Honeypot Research, I. J. Computer Network and Information Security, 10, 63–75, (2012).
 
5.
W. Cheung, O. Za¨iane, Incremental Mining of Frequent Patterns Without Candidate Generation or Support Constraint, 7th International Database Engineering and Applications Symposium (IDEAS 2003), Hong Kong, China. IEEE Computer Society, (2003).
 
6.
X. Fu, W. Yu, D. Cheng, X. Tan, K. Streff, and S. Graham, On Recognizing Virtual Honeypots and Countermeasures, Proceedings of the IEEE International Symposium on Dependable, Autonomic and Secure Computing, pp. 211-218, (2006).
 
7.
J. Han, J. Pei, Y. Yin, Mining Frequent Patterns without Candidate Generation, Proceedings of the 2000 ACM SIGMOD international conference on Management of data, Dallas, Texas, United States, (2000).
 
8.
The Honeynet Project, Know Your Enemy, learning about security threats, Addison-Wesley, ISBN 0-321-16646-9, (2004).
 
9.
N. Provos, T. Holz, Praise for virtual HoneyPots, Pearson Education, ISBN 978-0-321-33632-3, (2007).
 
10.
C. Seifert, I. Welch, P. Komisarczuk, Taxonomy of Honeypots, CS Technical Report TR-06-12, School of Mathematics, Statistics and Computer Science, Victoria University of Wellington, New Zealand., (2006).
 
11.
Ł. Skonieczny, Mining for Unconnected Frequent Graphs with Direct Subgraph Isomorphism Tests, w: Man-Machine Interactions / K. A. Cyran i in. (red.), Advances in Intelligent and Soft Computing, vol. 59, 2009, Springer, ISBN 978-3-642-00562-6, ss. 523–531, DOI:10.1007/978-3-642-00563-3-55.
 
ISSN:2082-2677
Journals System - logo
Scroll to top