Skip to main content
Fachgebiete
Automobil + Motoren Bauwesen + Immobilien Business IT + Informatik Elektrotechnik + Elektronik Energie + Nachhaltigkeit Finance + Banking Management + Führung Marketing + Vertrieb Maschinenbau + Werkstoffe Versicherung + Risiko
DE
EN
Bücher
Zeitschriften
Themenseiten
Organisationspsychologie Projektmanagement Marketing Smart Manufacturing
Weitere Formate
Podcasts Webinare Technik Webinare Wirtschaft Kongresse Veranstaltungskalender Awards
Jetzt Einzelzugang starten
Zugang für Unternehmen
Referenzkunden
SLX-Digitalkonferenz: Zukunftswerkstatt 2024

Mehr Umsatz mit Top-Kontakten

Am 7. Mai erfahren Sie, wie Vertriebsteams Leadgenerierung, Leadnurturing und Leadstrategien effizient steuern können.
Erweiterte Suche
Anmelden
nach oben
Neural Computing and Applications
Erschienen in: Neural Computing and Applications 6/2016

01.08.2016 | Original Article

A hybrid method consisting of GA and SVM for intrusion detection system

verfasst von: B. M. Aslahi-Shahri, R. Rahmani, M. Chizari, A. Maralani, M. Eslami, M. J. Golkar, A. Ebrahimi

Erschienen in: Neural Computing and Applications | Ausgabe 6/2016

Einloggen

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

search-config
loading …

Abstract

In this paper, a hybrid method of support vector machine and genetic algorithm (GA) is proposed and its implementation in intrusion detection problem is explained. The proposed hybrid algorithm is employed in reducing the number of features from 45 to 10. The features are categorized into three priorities using GA algorithm as the highest important is the first priority and the lowest important is placed in the third priority. The feature distribution is done in a way that 4 features are placed in the first priority, 4 features in the second, and 2 features in the third priority. The results reveal that the proposed hybrid algorithm is capable of achieving a true-positive value of 0.973, while the false-positive value is 0.017.
Vorheriger Artikel Research on group search optimizers for a reconfigurable flow shop sequencing problem
Nächster Artikel Learning node labels with multi-category Hopfield networks

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

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

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+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
Literatur
1.
Massa D, Valverde R (2014) A fraud detection system based on anomaly intrusion detection systems for e-commerce applications. Comput Inf Sci 7(2):117
2.
Luo B, Xia J (2014) A novel intrusion detection system based on feature generation with visualization strategy. Expert Syst Appl 41(9):4139CrossRef
3.
Agah A, Das SK, Basu K, Asadi M (2004) In: Proceedings of network computing and applications, 2004 (NCA 2004). Symposium on third IEEE international. IEEE, pp 343–346
4.
Anantvalee T, Wu J (2007) Wireless network security. Springer, US, pp 159–180CrossRef
5.
Hwang K, Cai M, Chen Y, Qin M (2007) Hybrid intrusion detection with weighted signature generation over anomalous internet episodes. IEEE Trans Dependable Secure Comput 4(1):41–55CrossRef
6.
Tsang CH, Kwong S, Wang H (2007) Genetic-fuzzy rule mining approach and evaluation of feature selection techniques for anomaly intrusion detection. Pattern Recognit 40(9):2373–2391CrossRefMATH
7.
Tsai CF, Lin CY (2010) A triangle area based nearest neighbors approach to intrusion detection. Pattern Recognit 43(1):222–229MathSciNetCrossRefMATH
8.
Jing W, Yan-heng L, Fan-xue M, Rong L (2010) In: The 7th international conference on informatics and systems (INFOS), 2010. IEEE, pp 1–6
9.
Wang G, Hao J, Ma J, Huang L (2010) A new approach to intrusion detection using Artificial Neural Networks and fuzzy clustering. Expert Syst Appl 37(9):6225–6232CrossRef
10.
Kumar G, Reddy DK (2014) In: International conference on electronic systems, signal processing and computing technologies (ICESC), 2014. IEEE, pp 429–433
11.
Li W (2004) In: Proceedings of the United States Department of Energy Cyber Security Group, pp 1–8
12.
13.
Rahmani R, Seyedmahmoudian M, Mekhilef S, Yusof R (2013) Implementation of fuzzy logic maximum power point tracking controller for photovoltaic system. Am J Appl Sci 10:209–218CrossRef
14.
15.
Rahmani R, Othman M, Shojaei A, Yusof R (2014) Static VAR compensator using recurrent neural network. Electr Eng 96(2):109–119CrossRef
16.
Fa HK, Yusof R, Rahmani R, Ahmadi M (2013) Optimization of DNA sensor model based nanostructured graphene using particle swarm optimization technique. J Nanomater 2013(2013):1–9
17.
Rahmani R, Yusof R (2014) A new simple, fast and efficient algorithm for global optimization over continuous search-space problems: radial movement optimization. Appl Math Comput 248:287–300MathSciNetMATH
18.
Rahmani R, Karimi H, Ranjbari L, Emadi M, Seyedmahmoudian M, Shafiabady A, Ismail R (2014) Structure and thickness optimization of active layer in nanoscale organic solar cells. Plasmonics 10(3):495–502CrossRef
19.
Abdullah K, Lee C, Conti G, Copeland JA, Stasko J (2005) IDS rainstorm: Visualizing IDS alarms. In: IEEE workshops on visualization for computer security, 2005. IEEE, p 1
20.
Kruegel C, Toth T (2003) Using decision trees to improve signature-based intrusion detection. In: Recent advances in intrusion detection. Springer, Berlin, pp 173–191
21.
Garcia-Teodoro P, Diaz-Verdejo J, Maciá-Fernndez G, Vázquez E (2009) Anomaly-based network intrusion detection: techniques, systems and challenges. Comput secur 28(1):18–28CrossRef
22.
Wool A (2004) A quantitative study of firewall configuration errors. Computer 37(6):62–67CrossRef
23.
Aneetha A, Indhu T, Bose S In: Proceedings of the second international conference on computational science, engineering and information technology. ACM, pp 47–51
24.
Kohavi R, John GH (1997) Wrappers for feature subset selection. Artif Intell 97(1):273–324CrossRefMATH
25.
Chebrolu S, Abraham A, Thomas JP (2005) Feature deduction and ensemble design of intrusion detection systems. Comput Secur 24(4):295–307CrossRef
26.
Li Y, Wang JL, Tian ZH, Lu TB, Young C (2009) Building lightweight intrusion detection system using wrapper-based feature selection mechanisms. Comput Secur 28(6):466–475CrossRef
27.
Li Y, Xia J, Zhang S, Yan J, Ai X, Dai K (2012) An efficient intrusion detection system based on support vector machines and gradually feature removal method. Expert Syst Appl 39(1):424–430CrossRef
28.
Lippmann RP, Fried DJ, Graf I, Haines JW, Kendall KR, McClung D, Weber D, Webster SE, Wyschogrod D, Cunningham RK (2000) In: Proceedings of DARPA information survivability conference and exposition, 2000. DISCEX’00, vol. 2. IEEE, vol. 2, pp 12–26
29.
Cunningham RK, Lippmann RP, Fried DJ, Garfinkel SL, Graf I, Kendall KR, Webster SE, Wyschogrod D, Zissman MA (1999) Evaluating intrusion detection systems without attacking your friends: the 1998 darpa intrusion detection evaluation. Tech. rep., DTIC Document
30.
Goh VT, Zimmermann J, Looi M (2009) In: International conference on availability, reliability and security, 2009. ARES’09. IEEE, pp 540–545
31.
Goh VT, Zimmermann J, Looi M (2010) Experimenting with an intrusion detection system for encrypted networks. Int J Cryptol Res 5:172
32.
Hashemi VM, Muda Z, Yassin W (2013) Improving intrusion detection using genetic algorithm. Inf Technol J 12(5):2167–2173CrossRef
33.
Cortes C, Vapnik V (1995) Support-vector networks. Mach Learn 20(3):273–297MATH
34.
Gupta P, Shinde SK (2011) Genetic algorithm technique used to detect intrusion detection. Springer, Berlin, pp 122–131
35.
Alcalá R, Alcalá-Fdez J, Casillas J, Cordón O, Herrera F (2006) Hybrid learning models to get the interpretability–accuracy trade-off in fuzzy modeling. Soft Comput 10(9):717–734CrossRef
36.
Abraham A, Corchado E, Corchado JM (2009) Hybrid learning machines. Neurocomputing 72(13):2729–2730CrossRef
37.
Yu E, Cho S (2003) In: Neural networks, 2003. Proceedings of the International Joint Conference on IEEE, vol. 3, pp 2253–2257
38.
Li L, Jiang W, Li X, Moser KL, Guo Z, Du L, Wang Q, Topol EJ, Wang Q, Rao S (2005) A robust hybrid between genetic algorithm and support vector machine for extracting an optimal feature gene subset. Genomics 85(1):16–23CrossRef
39.
Bolon-Canedo V, Sanchez-Marono N, Alonso-Betanzos A (2011) Feature selection and classification in multiple class datasets: an application to KDD Cup 99 dataset. Expert Syst Appl 38(5):5947–5957CrossRef
40.
Engen V, Vincent J, Phalp K (2011) Exploring discrepancies in findings obtained with the KDD Cup’99 data set. Intell Data Anal 15(2):251–276
41.
Raghuveer K et al (2012) Performance evaluation of data clustering techniques using KDD Cup-99 Intrusion detection data set. Int J Inf Netw Secur (IJINS) 1(4):294–305
42.
Cheng J, Hatzis C, Hayashi H, Krogel MA, Morishita S, Page D, Sese J (2002) KDD Cup 2001 report. ACM SIGKDD Explor Newsl 3(2):47–64CrossRef
Metadaten
Titel
A hybrid method consisting of GA and SVM for intrusion detection system
verfasst von
B. M. Aslahi-Shahri
R. Rahmani
M. Chizari
A. Maralani
M. Eslami
M. J. Golkar
A. Ebrahimi
Publikationsdatum
01.08.2016
Verlag
Springer London
Erschienen in
Neural Computing and Applications / Ausgabe 6/2016
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-015-1964-2

Weitere Artikel der Ausgabe 6/2016

Neural Computing and Applications 6/2016 Zur Ausgabe

Original Article

Application of multi-gene genetic programming based on separable functional network for landslide displacement prediction

Original Article

Time-delayed dynamic neural network-based model for hysteresis behavior of shape-memory alloys

Original Article

Learning node labels with multi-category Hopfield networks

Original Article

A hybrid adaptive cuckoo search optimization algorithm for the problem of chaotic systems parameter estimation

Original Article

RBFNN versus FFNN for daily river flow forecasting at Johor River, Malaysia

Original Article

A new multi-criteria weighting and ranking model for group decision-making analysis based on interval-valued hesitant fuzzy sets to selection problems