Czasopismo
2013
|
Vol. 18, no. 1
|
15--21
Tytuł artykułu
Autorzy
Wybrane pełne teksty z tego czasopisma
Warianty tytułu
Języki publikacji
Abstrakty
The article depicts possibility of using Matching Pursuit decomposition in order to recognize unspecified hazards in network traffic. Furthermore, the work aims to present feasible enhancements to the anomaly detection method, as well as their efficiency on the basis of a wide collection of pattern test traces.
Słowa kluczowe
Czasopismo
Rocznik
Tom
Strony
15--21
Opis fizyczny
Bibliogr. 16 poz., rys.
Twórcy
autor
- Institute of Telecommunications, University of Technology & Life Sciences in Bydgoszcz, ul. Kaliskiego 7, 85-789 Bydgoszcz, Poland, tomasz.andrysiak@utp.edu.pl
autor
- Institute of Telecommunications, University of Technology & Life Sciences in Bydgoszcz, ul. Kaliskiego 7, 85-789 Bydgoszcz, Poland
autor
- Institute of Telecommunications, University of Technology & Life Sciences in Bydgoszcz, ul. Kaliskiego 7, 85-789 Bydgoszcz, Poland
Bibliografia
- [1] L. Coppolino, S. D0Antonio, M. Esposito, L. Romano, Exploiting diversity and correlation to im prove the performance of intrusion detection systems, In: Proc of IFIP/IEEE International Conference on Network and Service, 2009
- [2] N. Ye, Q. Chen, S.M. Emran, Chi-squared statistical profiling for anomaly detection, In Proc. IEEE SMC Inform. Assurance Security Workshop. West Point, pp. 182-188, 2000
- [3] A. Scherrer, N. Larrieu, P. Owezarski, P. Borgant, P. Abry, Non-Gaussian and Long Memory Statistical Characterizations for Internet Traffic with Anomalies, IEEE Trans. On Dependable and Secure Computing, vol.4 no. 1, 2007
- [4] M. Choraś, Ł. Saganowski, R. Renk, W. Hołubowicz, Statistical and signal-based network traffic recognition for anomaly detection, In: Expert Systems Volume 29, Issue 3, pages 232-245, July 2012
- [5] N. Ye, X. Li, Q. Chen,S. Masum Emran, M. Xu, Probabilistic techniques for intrusion detection based on computer audit data, IEEE Trans. On Systems, Man and Cybernetics-Part A: Systems and Humans, vol. 31, no. 4, 2001
- [6] A. Dainotti, A. Pescape, G. Ventre, Wavelet-based Detection of DoS Attacks, IEEE GLOBECOM - Nov 2006, San Francisco (CA, USA), 2006
- [7] L. Wei, A. Ghorbani, Network Anomaly Detection Based on Wavelet Analysis, In: EURASIP Journal on Advances in Signal Processing, vol. 2009, Article ID 837601, 16 pages, doi:10.1155/2009/837601, 2009
- [8] A. Grossman, J. Morlet, Decompositions of Functions into Wavelets of Constant Shape, and Related Transforms, Mathematics and Physics: Lectures an Recent Results, L. Streit, 1985
- [9] S.G. Mallat, Z. Zhang, Matching Pursuit with timefrequency dictionaries, In: IEEE Transactions on Signal Processing, vol. 41, no 12, pp. 3397-3415, 1993
- [10] A. Gilbert, A. Muthukrishnam, M.J. Strauss, Approximation of functions over redundant dictionaries using coherence, In: 14th ACM-SIAM Symposium on Discrete Algorithms, 2003
- [11] Y.C. Pati, R. Rezaiifar, P.S. Krishnaprasad, Orthogonal matching pursuit: recursive function approximation with applications to wavelet decomposition, in Asilomar Conference on Signals, Systems and Computers, vol. 1, pp. 40-44, 1993
- [12] J.A. Troop, Greed is Good: Algorithmic Results for Sparse Approximation, IEEE Transactions on Information Theory, vol. 50, no. 10, 2004
- [13] Defense Advanced Research Projects Agency DARPA Intrusion Detection Evaluation Data Set, http://www.ll.mit.edu/mission/communications/ist/corpora/ideval/data/index.html
- [14] Benchmark Data, http://www.takakura.com//Kyoto_data/, 2010
- [15] J. Song, H. Takakura, Y. Okabe, Y. Kwon, Correlation Analysis Between Honeypot Data and IDS Alerts Using One-class SVM, Intrusion Detection Systems, InTech, DOI: 10.5772/13951, Available from: http://www.intechopen.com/books/intrusiondetection-systems/,2011
- [16] ClamAV antivirus, http://www.clamav.net/
Typ dokumentu
Bibliografia
Identyfikatory
Identyfikator YADDA
bwmeta1.element.baztech-75ed9a8e-5c91-4cb3-bbfc-84b7fb4751f4