A New Deep Learning Approach for Anomaly Base IDS using Memetic Classifier

Shahriar Mohammadi, Amin Namadchian

Abstract


A model of an intrusion-detection system capable of detecting attack in computer networks is described. The model is based on deep learning approach to learn best features of network connections and Memetic algorithm as final classifier for detection of abnormal traffic.
One of the problems in intrusion detection systems is large scale of features. Which makes typical methods data mining method were ineffective in this area. Deep learning algorithms succeed in image and video mining which has high dimensionality of features. It seems to use them to solve the large scale of features problem of intrusion detection systems is possible. The model is offered in this paper which tries to use deep learning for detecting best features.An evaluation algorithm is used for produce final classifier that work well in multi density environments.
We use NSL-KDD and Kdd99 dataset to evaluate our model, our findings showed 98.11 detection rate. NSL-KDD estimation shows the proposed model has succeeded to classify 92.72% R2L attack group.


Keywords


Deep learning, KDD99, memetic algorithm, NSL-Kdd, classification function, anomaly base intrusion detection, intrusion-detection system (IDS)

Full Text:

PDF

References


Ahmed M., Naser Mahmood A.,Hu J. (2016); A survey of network anomaly detection techniques, Journal of Network and Computer Applications, 60, 19–31, 2016.
https://doi.org/10.1016/j.jnca.2015.11.016

Amor N. B., Benferhat S., Elouedi Z. (2004); Naive bayes vs decision trees in intrusion detection systems, Proc. of the 2004 ACM Symposium on Applied Computing, NY, USA. ACM, 420–424, 2004.

Axelsson S. (2000); Intrusion Detection Systems : A Survey and Taxonomy, Computer Engineering, 1–27, 2000.

Bengio Y. (2013); Deep learning of representations: Looking forward, Intl. Conf. on Statistical Language and Speech Processing, 1–37, 2013.
https://doi.org/10.1007/978-3-642-36657-4_1

Bengio Y., Courville A. C., Vincent P. (2012); Unsupervised feature learning and deep learning: A review and new perspectives, CoRR, abs/1206.5538, 1, 2012.

Bengio Y., Goodfellow I. J., Courville A. (2016); Deep Learning, The MIT Press, 2016.

Bhuyan M. H., Bhattacharyya D. K., Kalita J. K. (2014); Network anomaly detection: methods, systems and tools, Communications Surveys & Tutorials, IEEE, 16(1), 303–336, 2014.
https://doi.org/10.1109/SURV.2013.052213.00046

Dang Y., Wang B., Brant R., Zhang Z., Alqallaf M., Wu Z. (2017); Anomaly detection for data streams in large-scale distributed heterogeneous computing environments, ICMLG2017 5th Intl. Conf. on Management Leadership and Governance, 121-121, 2017.

Erhan D., Manzagol P.-A., Bengio Y., Bengio S., Vincent P. (2009); The difficulty of training deep architectures and the effect of unsupervised pre-training, Artificial Intelligence and Statistics, 153–160, 2009.

García-Teodoro P., Díaz-Verdejo J., Maciá-Fernández G., Vázquez E. (2009); Anomaly based network intrusion detection: Techniques, systems and challenges, Computers & Security, 28(1-2), 18–28, 2009.
https://doi.org/10.1016/j.cose.2008.08.003

Ng A. (2011). Sparse autoencoder, CS294A Lecture Notes, 72, 1–19, 2011.

Nguyen H., Franke K., Petrovic S. (2010); Improving effectiveness of intrusion detection by correlation feature selection, In ARES 2010 - 5th Intl. Conf. on Availability, Reliability, and Security, 17–24, 2010.

Owais S., Snasel V., Kromer P., Abraham A. (2008); Survey: Using Genetic Algorithm Approach in Intrusion Detection Systems Techniques, 2008 7th Computer Information Systems and Industrial Management Applications, 300–307, 2008.

Qu G., Hariri S., Yousif M. (2005), A new dependency and correlation analysis for features, IEEE Transactions on Knowledge and Data Engineering, 17(9), 1199–1206, 2005.
https://doi.org/10.1109/TKDE.2005.136

Schmidhuber J. (2015), Deep learning in neural networks: An overview, Neural Networks, 61, 85–117, 2015.
https://doi.org/10.1016/j.neunet.2014.09.003

Tavallaee M., Bagheri E., Lu W., Ghorbani A. A. (2009), A detailed analysis of the KDD CUP 99 data set, IEEE Symposium on Computational Intelligence for Security and Defense Applications, CISDA 2009, 2009.
https://doi.org/10.1109/CISDA.2009.5356528

University of California, I. KDD Cup 1999, 1999.

Xia T., Qu G., Hariri S., Yousif M. (2005), An efficient network intrusion detection method based on information theory and genetic algorithm, Performance, Computing, and Communications Conference, 2005. IPCCC 2005, 24th IEEE Intl., 11–17, 2005.




DOI: http://dx.doi.org/10.15837/ijccc.2017.5.2972

Refbacks

  • There are currently no refbacks.




Copyright (c) 2017 Shahriar mohammadi, amin namadchian

Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

CC-BY-NC-ND   license for website user

Articles published in IJCCC user license are protected by copyright.

Users can access, download, copy, translate the IJCCC articles for non-commercial purposes provided that users, but cannot redistribute, display or adapt:

  • Cite the article using an appropriate bibliographic citation: author(s), article title, journal, volume, issue, page numbers, year of publication, DOI, and the link to the definitive published version on IJCCC website;
  • Maintain the integrity of the IJCCC article;
  • Retain the copyright notices and links to these terms and conditions so it is clear to other users what can and what cannot be done with the  article;
  • Ensure that, for any content in the IJCCC article that is identified as belonging to a third party, any re-use complies with the copyright policies of that third party;
  • Any translations must prominently display the statement: "This is an unofficial translation of an article that appeared in IJCCC. Agora University  has not endorsed this translation."
  • NoDerivatives — If you remix, transform, or build upon the material, you may not distribute the modified material.

This is a non commercial license where the use of published articles for commercial purposes is forbiden. 

Commercial purposes include: 

  • Copying or downloading IJCCC articles, or linking to such postings, for further redistribution, sale or licensing, for a fee;
  • Copying, downloading or posting by a site or service that incorporates advertising with such content;
  • The inclusion or incorporation of article content in other works or services (other than normal quotations with an appropriate citation) that is then available for sale or licensing, for a fee;
  • Use of IJCCC articles or article content (other than normal quotations with appropriate citation) by for-profit organizations for promotional purposes, whether for a fee or otherwise;
  • Use for the purposes of monetary reward by means of sale, resale, license, loan, transfer or other form of commercial exploitation;

    The licensor cannot revoke these freedoms as long as you follow the license terms.

[End of license]


INTERNATIONAL JOURNAL OF COMPUTERS COMMUNICATIONS & CONTROL (IJCCC), With Emphasis on the Integration of Three Technologies (C & C & C),  ISSN 1841-9836.

IJCCC was founded in 2006,  at Agora University, by  Ioan DZITAC (A. Editor-in-Chief),  Florin Gheorghe FILIP (Editor-in-Chief), and  Misu-Jan MANOLESCU (Managing Editor).

This journal is a member of, and subscribes to the principles of, the Committee on Publication Ethics (COPE).

Ioan  DZITAC (A. Editor-in-Chief) at COPE European Seminar, Bruxelles, 2015:

IJCCC is covered/indexed/abstracted in Science Citation Index Expanded (since vol.1(S),  2006). IF=1.374 in JCR2016.

IJCCC is indexed in Scopus from 2008 (SNIP2016 = 0.701, SJR2016 =0.319):

Nomination by Elsevier for Journal Excellence Award Romania 2015 (SNIP2014 = 1.029): Elsevier/ Scopus

IJCCC was nominated by Elsevier for Journal Excellence Award - "Scopus Awards Romania 2015" (SNIP2014 = 1.029).

IJCCC is in Top 3 of 157 Romanian journals indexed by Scopus (in all fields) and No.1 in Computer Science field by Elsevier/ Scopus.