nach oben

Erschienen in:

2020 | OriginalPaper | Buchkapitel

MLP4NIDS: An Efficient MLP-Based Network Intrusion Detection for CICIDS2017 Dataset

verfasst von : Arnaud Rosay, Florent Carlier, Pascal Leroux

Erschienen in: Machine Learning for Networking

Verlag: Springer International Publishing

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config

KI-gestützte Suche

Aus

Abstract

More and more embedded devices are connected to the internet and therefore are potential victims of intrusion. While machine learning algorithms have proven to be robust techniques, it is mainly achieved with traditional processing, neural network giving worse results. In this paper, we propose usage of a multi-layer perceptron neural network for intrusion detection and provide a detailed description of our methodology. We detail all steps to achieve better performances than traditional machine learning techniques with a detection of intrusion accuracy above 99% and a low false positive rate kept below 0.7%. Results of previous works are analyzed and compared with the performances of the proposed solution.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Vorheriges Kapitel A New Approach to Determine the Optimal Number of Clusters Based on the Gap Statistic

Nächstes Kapitel Random Forests with a Steepend Gini-Index Split Function and Feature Coherence Injection

http://cve.mitre.org/.

Carlier, F., Renault, V.: IoT-a, embedded agents for smart internet of things. application on a display wall. In: 2016 IEEE/WIC/ACM International Conference on Web Intelligence Workshops (WIW), pp. 80–83, October 2016. https://doi.org/10.1109/WIW.2016.034

Chawla, N.V., Bowyer, K.W., Hall, L.O., Kegelmeyer, W.P.: SMOTE: synthetic minority over-sampling technique. J. Artif. Intell. Res. 16, 321–357 (2002)CrossRef

Dhanabal, L., Shantharajah, D.S.P.: A study on NSL-KDD dataset for intrusion detection system based on classification algorithms. Int. J. Adv. Res. Comput. Commun. Eng. 4, 446–452 (2015)

Garg, A., Maheshwari, P.: Performance analysis of Snort-based intrusion detection system. In: 2016 3rd International Conference on Advanced Computing and Communication Systems (ICACCS), vol. 01, pp. 1–5, January 2016. https://doi.org/10.1109/ICACCS.2016.7586351

Gharib, A., Sharafaldin, I., Lashkari, A.H., Ghorbani, A.A.: An evaluation framework for intrusion detection dataset. In: 2016 International Conference on Information Science and Security (ICISS), pp. 1–6, December 2016. https://doi.org/10.1109/ICISSEC.2016.7885840

Jiang, J., et al.: ALDD: a hybrid traffic-user behavior detection method for application layer DDoS. In: 2018 17th IEEE International Conference on Trust, Security and Privacy in Computing and Communications/12th IEEE International Conference on Big Data Science and Engineering (TrustCom/BigDataSE), pp. 1565–1569, August 2018. https://doi.org/10.1109/TrustCom/BigDataSE.2018.00225

Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. In: 2015 3rd International Conference for Learning Representations (2014)

Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. In: 2017 Advances in Neural Information Processing Systems, pp. 971–980 (2017)

Lee, W., Stolfo, S.J., Mok, K.W.: Mining in a data-flow environment: experience in network intrusion detection. In: Proceedings of the Fifth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 1999, pp. 114–124. ACM, New York (1999). https://doi.org/10.1145/312129.312212

10.

McHugh, J.: Testing intrusion detection systems: a critique of the 1998 and 1999 DARPA intrusion detection system evaluations as performed by Lincoln laboratory. ACM Trans. Inf. Syst. Secur. 3(4), 262–294 (2000). https://doi.org/10.1145/382912.382923CrossRef

11.

Moustafa, N., Slay, J.: UNSW-NB15: a comprehensive data set for network intrusion detection systems (UNSW-NB15 network data set). In: 2015 Military Communications and Information Systems Conference (MilCIS), pp. 1–6, November 2015. https://doi.org/10.1109/MilCIS.2015.7348942

12.

European Parliament: Regulation (EU) 2015/758 of the European Parliament and of the Council of 29 April 2015 concerning type-approval requirements for the deployment of the eCall in-vehicle system based on the 112 service and amending Directive 2007/46/EC. Official Journal of the European Union, May 2015

13.

Riyaz, B., Ganapathy, S.: An intelligent fuzzy rule based feature selection for effective intrusion detection. In: 2018 International Conference on Recent Trends in Advance Computing (ICRTAC), pp. 206–211, September 2018

14.

Sharafaldin, I., Lashkari, A.H., Ghorbani, A.A.: Toward generating a new intrusion detection dataset and intrusion traffic characterization. In: Proceedings of the 4th International Conference on Information Systems Security and Privacy - Volume 1: ICISSP, pp. 108–116. SciTePress, January 2018. https://doi.org/10.5220/0006639801080116

15.

Shone, N., Ngoc, T.N., Phai, V.D., Shi, Q.: A deep learning approach to network intrusion detection. IEEE Trans. Emerg. Top. Comput. Intell. 2(1), 41–50 (2018). https://doi.org/10.1109/TETCI.2017.2772792CrossRef

16.

Tang, T.A., Mhamdi, L., McLernon, D., Zaidi, S.A.R., Ghogho, M.: Deep learning approach for Network Intrusion Detection in Software Defined Networking. In: 2016 International Conference on Wireless Networks and Mobile Communications (WINCOM), pp. 258–263, October 2016. https://doi.org/10.1109/WINCOM.2016.7777224

17.

Tavallaee, M., Bagheri, E., Lu, W., Ghorbani, A.A.: A detailed analysis of the KDD CUP 99 data set. In: 2009 IEEE Symposium on Computational Intelligence for Security and Defense Applications, pp. 1–6, July 2009. https://doi.org/10.1109/CISDA.2009.5356528

18.

Ullah, I., Mahmoud, Q.H.: A two-level hybrid model for anomalous activity detection in IoT networks. In: 2019 16th IEEE Annual Consumer Communications Networking Conference (CCNC), pp. 1–6, January 2019. https://doi.org/10.1109/CCNC.2019.8651782

19.

Ustebay, S., Turgut, Z., Aydin, M.A.: Intrusion detection system with recursive feature elimination by using random forest and deep learning classifier. In: 2018 International Congress on Big Data, Deep Learning and Fighting Cyber Terrorism (IBIGDELFT), pp. 71–76, December 2018. https://doi.org/10.1109/IBIGDELFT.2018.8625318

20.

Zyad, E., Taha, A., Mohammed, B.: Improve R2L attack detection using trimmed PCA. In: 2019 International Conference on Advanced Communication Technologies and Networking (CommNet), pp. 1–5, April 2019. https://doi.org/10.1109/COMMNET.2019.8742361

Titel: MLP4NIDS: An Efficient MLP-Based Network Intrusion Detection for CICIDS2017 Dataset
verfasst von: Arnaud Rosay
Florent Carlier
Pascal Leroux
Verlag: Springer International Publishing
Buch: Machine Learning for Networking
Print ISBN: 978-3-030-45777-8

Electronic ISBN: 978-3-030-45778-5

Copyright-Jahr: 2020
DOI: https://doi.org/10.1007/978-3-030-45778-5_16

Springer Professional

Abstract

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Technik"

Springer Professional "Wirtschaft"