Home > CSC-OpenAccess Library > Manuscript Information
EXPLORE PUBLICATIONS BY COUNTRIES |
 |
 |
EUROPE |
|
MIDDLE EAST |
|
ASIA |
|
AFRICA |
| ............................. | |
|
United States of America |
|
United Kingdom |
|
Canada |
|
Australia |
|
Italy |
|
France |
|
Brazil |
|
Germany |
|
Malaysia |
|
Turkey |
|
China |
|
Taiwan |
|
Japan |
|
Saudi Arabia |
|
Jordan |
|
Egypt |
|
United Arab Emirates |
|
India |
|
Nigeria |
Adapting New Data In Intrusion Detection Systems
Aslihan Akyol, Bekir KARLIK, Bariş Koçer
Pages - 1 - 11 | Revised - 31-01-2019 | Published - 28-02-2019
MORE INFORMATION
KEYWORDS
Intrusion Detection Systems, Transfer Learning, Genetic Transfer Learning, Genetic Algorithms, Artificial Neural Networks.
ABSTRACT
Most of the introduced anomaly intrusion detection system (IDS) methods focus on achieving better detection rates and lower false alarm rates. However, when it comes to real-time applications many additional issues come into the picture. One of them is the training datasets that are continuously becoming outdated. It is vital to use an up-to-date dataset while training the system. But the trained system will become insufficient if network behaviors change. As well known, frequent alteration is in the nature of computer networks. On the other hand it is costly to continually collect and label datasets while frequently training the system from scratch and discarding old knowledge is a waste. To overcome this problem, we propose the use of transfer learning which benefits from the previous gained knowledge. The carried out experiments stated that transfer learning helps to utilize previously obtained knowledge, improves the detection rate and reduces the need to recollect the whole dataset.
| 1 | Google Scholar  |
| 2 | refSeek |
| 3 | BibSonomy |
| 4 | ResearchGate |
| 5 | Doc Player |
| 6 | Scribd |
| 7 | SlideShare |
| "KDD Cup 1999 Data," The UCI KDD Archive, 1999. [Online]. Available: http://kdd.ics.uci.edu/databases/kddcup99/kddcup99.html. [Accessed: 05-Jul-2013]. | |
| A Pektas, and T. Acarman, "A deep learning method to detect network intrusion through flow-based features" International Journal of Network Management, special issue paper, pp. 1-19, 2018. | |
| A. Özkaya and B. Karlik, "Protocol type based intrusion detection using RBF neural network," Int. J. Artif. Intell. Expert Syst., vol. 3, no. 4, pp. 90-99, 2012. | |
| A. Akyol, M. Hacibeyoglu, and B. Karlik, "Design of multilevel hybrid classifier with variant feature sets for intrusion detection system" IEICE Transactions on Information and Systems, vol. 99, no.7, pp.1810-1821, 2016. | |
| A. J. Storkey, "When training and test sets are different : Characterising learning transfer," Dataset shift Mach. Learn., pp. 3-28, 2013. | |
| B. Koçer and A. Arslan, "Genetic transfer learning," Expert Syst. Appl., vol. 37, no. 10, pp. 6997-7002, Oct. 2010. | |
| B. Koçer, "Transfer ögrenmede yeni yaklasimlar," PhD thesis (in Turkish), Selcuk University, 2012. | |
| C. Modi, D. Patel, B. Borisaniya, H. Patel, A. Patel, and M. Rajarajan, "A survey of intrusion detection techniques in Cloud," J. Netw. Comput. Appl., vol. 36, no. 1, pp. 42-57, Jan. 2013. | |
| D. Hermawanto, "Genetic algorithm for solving simple mathematical equality problem," arXiv Prepr. arXiv1308.4675, 2013. | |
| E. Baralis, S. Chiusano, and P. Garza, "A lazy approach to associative classification," IEEE Trans. Knowl. Data Eng., vol. 20, no. 2, pp. 156-171, Feb. 2008. | |
| E. Lundin and E. Jonsson, "Anomaly-based intrusion detection: privacy concerns and other problems," Comput. Networks, vol. 34, no. 4, pp. 623-640, Oct. 2000. | |
| H. Bensefia and N. Ghoualmi, "A new approach for adaptive intrusion detection," 2011 Seventh Int. Conf. Comput. Intell. Secur., pp. 983-987, Dec. 2011. | |
| H. Debar, M. Dacier, and A. Wespi, "Towards a taxonomy of intrusion-detection systems," Comput. Networks, vol. 31, no. 8, pp. 805-822, Apr. 1999. | |
| H.-T. Lin, Y.-Y. Lin, and J.-W. Chiang, "Genetic-based real-time fast-flux service networks detection," Comput. Networks, vol. 57, no. 2, pp. 501-513, Feb. 2013. | |
| M. Srinivas and L. M. Patnaik, "Genetic algorithms: A survey," Computer (Long. Beach. Calif)., vol. 27, no. 6, pp. 17-26, Jun. 1994. | |
| MIT, "MIT Lincoln Laboratory: Communications & Information Technology." [Online]. Available: http://www.ll.mit.edu/mission/communications/ist/index.html. [Accessed: 21-Jun-2014]. | |
| N. Weng, L. Vespa, and B. Soewito, "Deep packet pre-filtering and finite state encoding for adaptive intrusion detection system," Comput. Networks, vol. 55, no. 8, pp. 1648-1661, Jun. 2011. | |
| R. P. Lippmann, D. J. Fried, I. Graf, J. W. Haines, K. R. Kendall, D. McClung, D. Weber, S. E. Webster, D. Wyschogrod, R. K. Cunningham, and M. A. Zissman, "Evaluating intrusion detection systems: The 1998 DARPA off-line intrusion detection evaluation," in DARPA Information Survivability Conference and Exposition, 2000. DISCEX'00. Proceedings, vol. 2, pp. 12-26. | |
| S. Axelsson, "Intrusion detection systems: A survey and taxonomy," Göteborg, Sweden, 2000. | |
| S. Gou, Y. Wang, L. Jiao, J. Feng, and Y. Yao, "Distributed transfer network learning based intrusion detection," in 2009 IEEE International Symposium on Parallel and Distributed Processing with Applications, 2009, pp. 511-515. | |
| S. J. Pan and Q. Yang, "A survey on transfer learning," IEEE Trans. Knowl. Data Eng., vol. 22, no. 10, pp. 1345-1359, Oct. 2010. | |
| S. Lee, D. Kim, and J. Park, "A survey and taxonomy of lightweight intrusion detection systems," J. Internet Serv. Inf. Secur., vol. 2, no. 1/2, pp. 119-13, 2012. | |
| U. Maulik and S. Bandyopadhyay, "Genetic algorithm-based clustering technique," Pattern Recognit., vol. 33, no. 9, pp. 1455-1465, Sep. 2000. | |
| V. Chandola, A. Banerjee, and V. Kumar, "Anomaly Detection : A Survey," ACM Comput. Surv., vol. 41, no. 3, pp. 1-72, 2009. | |
| W. Dai, Q. Yang, G. R. Xue, and Y. Yu, "Boosting for transfer learning," in Proceedings of the 24th international conference on Machine learning - ICML '07, 2007, pp. 193-200. | |
| W. Wang, Y. Sheng, J. Wang, X. Zeng, X. Ye, Y. Huang, and M. Zhuhast, "IDS: Learning Hierarchical Spatial-Temporal Features Using Deep Neural Networks to Improve Intrusion Detection" December 11, 2017, vol. 6, pp. 1792-1806, 2018. | |
Dr. Aslihan Akyol
Independent Researcher - Turkey
aslihan.ozkaya@gmail.com
Professor Bekir KARLIK
McGill University, Neurosurgical Simulation Research
& Training Centre, Montréal, QC - Canada
Dr. Bariş Koçer
Selcuk University, Department of Computer Engineering,
Konya, Turkey - Turkey
|
|
|
|
| View all special issues >> | |
|
|
